Algenta MCP Server
REMOTE · API.ALGENTA.AI · 2 COMPONENTS
Governed data discovery, exact queries, decisions, simulations, and runtime utilities over MCP.
Available components
Last scanned (14 hours ago)
Scores higher than 76% of remote servers
Reachable. Last reached 5 Nov 2026.
Category AI and Machine Learning
How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. How we score → Why this is hard to score →
Endpoint Security89
- The endpoint's TLS certificate is valid, in date, and uses a strong key. View diagnostics → Pass
- Authorisation is enforced on tool calls, but the challenge carries no valid RFC 9728 metadata, so a client cannot discover where to get a token. See how to fix → View diagnostics → Fail
- HTTPS is enforced; there's no plaintext access path. View diagnostics → Pass
- The HSTS (Strict-Transport-Security) header is present. View diagnostics → Pass
- DNSSEC check failed: this domain isn't protected by DNSSEC. See how to fix → View diagnostics → Fail
Transport & Reachability100
- Verified streamable-http transport via a live MCP handshake. View diagnostics → Pass
Schema Quality & AI Usability74
- AI-judged instruction clarity (excellent).Pass
- Context-footprint check failed: tool/resource definitions use about 20559 tokens (~146/item across 140 items; 140 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management13
- Stability observed for 4 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage99
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 96% of tool parameters carry a description.Partial
Tool Safety94
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- 12 of 16 tool(s) whose name or description implies an irreversible operation declare an MCP destructiveHint annotation; "query_data" implies "execute" and declares readOnlyHint instead, contradicting what its own name says it does. See how to fix → Partial
- An AI judge read all 140 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a current MCP spec version (2026-07-28).Pass
Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 4 Nov 26 −12
- Stability: pass → 0.10 functional
- 3 Nov 26 0
- Server version: 1.0.42 → 1.0.44 functional
- 1 Nov 26 +67
- Authorization: unverified → fail ▼ security
- Injection markers: unverified → pass ▲ security
- Stability: unverified → pass ▲ security
- HSTS header: fail → pass ▲ security
- Transport: fail → pass ▲ security
- Endpoint reachability: not serving MCP → reachable ▲ functional
- MCP protocol: unverified → pass ▲ functional
- Tool coverage: unverified → 100 ▲ functional
- Server version: 1.0.33 → 1.0.42 functional
- 10 Oct 26 −65
- Endpoint reachability: reachable → not serving MCP ▼ security
- Stability: pass → unverified ▼ security
- Authorization: fail → unverified ▼ security
- Tool safety: pass → unverified ▼ security
- Transport: pass → fail ▼ security
- Capabilities: pass → unverified ▼ functional
- Tool coverage: 100 → unverified ▼ functional
- First check of Schema quality: unverified functional
- 4 Oct 26 −2
- HSTS header: pass → fail ▼ security
- 1 Oct 26 0
- Server version: 1.0.32 → 1.0.33 functional
- 29 Sept 26 0
- Server version: 1.0.31 → 1.0.32 functional
- 28 Sept 26 0
- We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 5 Nov 2026 · Probed https://api.algenta.ai/mcp
TLS valid
Negotiated TLS 1.3 with TLS_AES_128_GCM_SHA256 .
| Subject | Issuer | Valid from | Valid until | Key | Signature | Serial |
|---|---|---|---|---|---|---|
| CN=algenta.ai | CN=WE1,O=Google Trust Services,C=US | 16 Oct 2026 | 14 Jan 2027 | ECDSA 256 | ECDSA-SHA256 | 184b59994cbb2b570e579ee995b33edd |
| SANs: algenta.ai, *.algenta.ai | ||||||
| CN=WE1,O=Google Trust Services,C=US (CA) | CN=GTS Root R4,O=Google Trust Services LLC,C=US | 13 Dec 2023 | 20 Feb 2029 | ECDSA 256 | ECDSA-SHA384 | 7ff31977972c224a76155d13b6d685e3 |
| CN=GTS Root R4,O=Google Trust Services LLC,C=US (CA) | CN=GlobalSign Root CA,OU=Root CA,O=GlobalSign nv-sa,C=BE | 15 Nov 2023 | 28 Jan 2028 | ECDSA 384 | SHA256-RSA | 7fe530bf331343bedd821610493d8a1b |
Background: What to check on a remote MCP endpoint →
DNSSEC insecure
Validation of api.algenta.ai. — Not signed
| Zone | DS | Keys | Algorithms | Outcome |
|---|---|---|---|---|
| . | trust_anchor | 20326, 38696 | 8, 8 | Verified |
| ai. | present | 1814, 3799 | 8, 8 | Verified |
| algenta.ai. | absent | Unsigned (proven) parent-signed NSEC/NSEC3 proves an unsigned delegation |
Authentication Challenged, unverified
The endpoint asked for a token, but we could not retrieve and validate the RFC 9728 metadata that tells a client how to obtain one.
| Result | Challenged, unverified |
|---|---|
| Enforced | On tool calls |
| HTTP status | 200 |
WWW-Authenticate challenge Bearer realm="algenta", error="invalid_token"
Bearer realm="algenta", error="invalid_token" | Header | Value |
|---|---|
| strict-transport-security | max-age=15552000; includeSubDomains |
Protected resource metadata
| Retrieved | No |
|---|---|
| Problem | no_resource_metadata |
Background: How OAuth 2.1 works in the 2026 MCP spec →
Transports 2 probes
| Transport | URL | Outcome | Status | Location |
|---|---|---|---|---|
| streamable-http | https://api.algenta.ai/mcp | Verified | 200 | |
| http (plaintext) | http://api.algenta.ai/mcp | HTTPS enforced | 301 | https://api.algenta.ai/mcp |
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
get_deployment_cost ~93
Get the current-month cost details of one deployment by id: provider, region, cost_usd_month, billable_cost_usd_month after markup, the applied billing_markup_pct, and last_updated. Requires an admin API key; an unknown deployment_id fails with not_found. Use get_deployment to find the active deployment first. Read-only.
| Name | Type | Req | Description |
|---|---|---|---|
| deployment_id | string | yes | Deployment id from get_deployment. |
No output schema declared.
No examples provided.
get_execution_policy ~62
Get the current autonomous execution policy for the active organization. Read-only and non-destructive; not separately rate-limited. Read this before update_execution_policy. Returns min_confidence, risk_floor, require_calibration, allow_reexecution, and the current snapshot metadata.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
get_job_result ~68
Fetch the completed result payload for an async simulation job by id. Read-only and non-destructive; not separately rate-limited. Use get_job_status to check progress before the job completes. Returns the completed job's result payload.
| Name | Type | Req | Description |
|---|---|---|---|
| job_id | string | yes | UUID of the async job |
No output schema declared.
No examples provided.
get_job_status ~70
Fetch the latest async simulation job status by id. Read-only and non-destructive; not separately rate-limited. Use poll_job to block until a terminal state. Returns the job record with status, progress, timestamps, and poll_url.
| Name | Type | Req | Description |
|---|---|---|---|
| job_id | string | yes | UUID of the async job |
No output schema declared.
No examples provided.
get_limits ~63
Get current plan quotas and limits for the active API key. Read-only and non-destructive; not separately rate-limited. Use get_usage for current consumption against these limits. Returns the plan's quota ceilings, including rate, concurrency, storage, and LLM spend cap.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
get_me ~62
Get current user and organization identity for the active API key. Read-only and non-destructive; not separately rate-limited. Use update_me to change the returned names. Returns user_id, name, email, role, and the organization id, name, and plan.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
get_repository_intelligence_capabilities ~57
List globally supported Repository Intelligence languages and ranked support progress. Read-only and non-destructive. Check language support here before create_repository_snapshot. Returns supported_languages and support_progress (ranked target counts, progress fraction, and label).
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
get_repository_snapshot ~106
Fetch one persisted immutable repository snapshot by repository_id and snapshot_id, including its resolved_revision, content_hash, file_count, language_counts, and graph artifact refs. Use this to re-read a snapshot created earlier with create_repository_snapshot (or through run_repository_pipeline). Read-only; an unknown snapshot or repository id fails with not_found.
| Name | Type | Req | Description |
|---|---|---|---|
| repository_id | string | yes | Saved repository connector id from list_connectors. |
| snapshot_id | string | yes | Snapshot id returned by create_repository_snapshot. |
No output schema declared.
No examples provided.
get_run ~86
Fetch one simulation run by run_id with its full detail — the decision metrics and the request context it ran under. Use list_runs to find run ids. Read-only; an unknown run_id fails with not_found. Returns the run record: run_id, recommended_action, confidence, expected_value, mode, and created_at.
| Name | Type | Req | Description |
|---|---|---|---|
| run_id | string | yes | UUID of the simulation run |
No output schema declared.
No examples provided.
get_runtime_benchmarks ~68
Get the authenticated Algenta runtime benchmark catalog. Use this when an agent needs benchmark classes, benchmark evidence paths, evaluation quality gates, SLO budgets, compiled artifacts, or module benchmark linkage before reasoning about runtime performance claims. Read-only and non-destructive; not separately rate-limited.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
get_runtime_manifest ~62
Get the signed Algenta runtime manifest. Use this when an agent needs the canonical runtime-core inventory, maturity states, proof matrix, typed failure contract, or release theorem before using runtime-backed execution paths. Read-only and non-destructive; not separately rate-limited.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
get_runtime_modules ~61
Get the authenticated Algenta runtime module proof catalog. Use this when an agent needs the shipping module inventory, proof-matrix entries, maturity counts, or compiled module evidence before using runtime-backed paths. Read-only and non-destructive; not separately rate-limited.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
get_runtime_release_validation ~62
Get the authenticated Algenta runtime release validation result. Use this when an agent needs the current manifest-listed release verdict, formal theorem conditions, or fail-closed proof status before using runtime-backed paths. Read-only and non-destructive; not separately rate-limited.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
get_source_schema ~101
Advanced tool. Get the full schema for a specific registered source: column types, cardinality, fill rates, formula relationships, and detected join keys to other sources. Read-only and non-destructive; not separately rate-limited. Use list_sources to find source ids. Returns source_id and the full schema: column types, cardinality, fill rates, formula relationships, and detected join keys.
| Name | Type | Req | Description |
|---|---|---|---|
| source_id | string | yes | Source ID from list_sources. |
No output schema declared.
No examples provided.
get_usage ~56
Get current billing period usage vs quota for this API key. Read-only and non-destructive; not separately rate-limited. Use get_limits for the plan's ceiling values. Returns simulations_used, simulations_limit, billing_period, and plan.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
ingest_data ~181
Auto-map tabular data to a simulation payload. Detects variable distributions, polarity (revenue=positive, cost=negative), units, and builds the objective function automatically. Set run_simulation=true to execute the simulation immediately and get results. Multiple tables: auto-detects join keys and merges before analysis. Returns fields_detected, records_analyzed, join_applied, engine, objective_function, variables, and simulation_payload, plus simulation_result when run_simulation is true.
| Name | Type | Req | Description |
|---|---|---|---|
| domain | string | – | Optional domain hint (finance, supply_chain, hr) for better field mapping. |
| run_simulation | boolean | – | Execute the simulation immediately and return results. |
| runs | integer | – | Scenarios to evaluate (1,000–1,000,000). |
| tables | array | yes | One or more data tables. First table is primary. |
No output schema declared.
No examples provided.
ingest_metering_events ~136
Ingest one batch of execution-analytics events from a managed runtime that explicitly enabled control-plane sync. This endpoint is analytics-only: received events are counted for dashboards and structured-logged, never used for billing or quota enforcement, and self-hosted Algenta profiles never call it automatically. Every event field is optional; events without a timestamp count toward the current billing month. An empty events list fails with empty_events. Returns accepted (event count) and the primary billing_period.
| Name | Type | Req | Description |
|---|---|---|---|
| device_id | string | yes | Managed-runtime device id that produced the events. |
| events | array | yes | Analytics events; every field below is optional. |
No output schema declared.
No examples provided.
invite_team_member ~121
Invite someone to the caller's organization by email and return the pending invite. This creates a pending invitation, emails an accept link, and reserves a seat until the invite is accepted. The caller's API key must have an admin role and the plan must have seats available — single-seat plans fail with seats_not_available. Use list_team_members to see who is already in the org. Role defaults to member.
| Name | Type | Req | Description |
|---|---|---|---|
| string | yes | Email address the invite link is sent to. | |
| role | string | – | Org role granted on accept; defaults to member. |
No output schema declared.
No examples provided.
list_agent_runs ~201
List the organization's persisted agent runs, paginated (defaults page 1, limit 25), with lineage-aware filters: status, request_hash (find reruns of the same request), policy_snapshot_id, and schema_snapshot_id (find runs under one policy or schema revision). Use get_agent_run for one run's full detail and query_agent_run_checkpoints to search checkpoints across runs. Read-only. Returns data, total, page, limit, and pages.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | Runs per page, up to 200; defaults to 25. |
| page | integer | – | 1-based page number; defaults to 1. |
| policy_snapshot_id | string | – | Keep only runs under this execution-policy snapshot. |
| request_hash | string | – | Keep only runs created from this request hash. |
| schema_snapshot_id | string | – | Keep only runs under this schema snapshot. |
| status | string | – | Keep only runs in this lifecycle status. |
No output schema declared.
No examples provided.
list_api_keys ~80
List active API keys for the current organization. Never returns raw secret material. Read-only and non-destructive; not separately rate-limited. Use create_api_key to mint one and revoke_api_key to retire one. Returns the key records with id, label, key_prefix, device_limit, status, created_at, last_used_at, and expires_at.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
list_capabilities ~162
List the unified capability catalog visible to the caller — datasets, MCP tools, resources and prompts, skills, native tools, and runtime libraries — with each entry's kind, provider, binding, and execution owner. Filter by kinds, provider_ids, or binding_ids to narrow the catalog. Use get_capability for one entry's detail, route_capabilities to pick the best entry for an objective, and list_skills for the skill subset. Read-only. Returns the catalog entries with capability_id, kind, provider_id, binding_id, execution_owner, and tags.
| Name | Type | Req | Description |
|---|---|---|---|
| binding_ids | array | – | Keep only capabilities from these bindings. |
| kinds | array | – | Keep only these capability kinds. |
| provider_ids | array | – | Keep only capabilities from these providers. |
No output schema declared.
No examples provided.
list_capability_bindings ~131
List the capability bindings saved under the caller's organization, optionally narrowed by provider_id or scope (user, workspace, organization). A binding pairs a provider profile with credentials/config and is what makes capabilities executable. Use create_capability_binding to add one, test_capability_binding to verify one, and list_capabilities to browse what they expose. Read-only. Returns the binding records with binding_id, provider_id, profile_id, scope, execution_owner, and status.
| Name | Type | Req | Description |
|---|---|---|---|
| provider_id | string | – | Keep only bindings of this provider. |
| scope | string | – | Keep only bindings at this scope. |
No output schema declared.
No examples provided.
list_capability_providers ~103
List the unified capability providers available to the organization — data sources, MCP servers, skill packs, native tools, and runtime libraries — with their profiles, auth kinds, supported execution owners, and binding scopes. Start here to find provider_id and profile_id for create_capability_binding, then discover_capability_binding to see what a binding exposes. Read-only. Returns the provider records with provider_id, provider_type, auth metadata, supported execution owners, and profiles.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
list_connectors ~156
List the data connectors saved under the caller's organization — databases, APIs, file-backed, and repository sources — with id, name, connector_type, status (untested, live, error), and visibility; stored credentials are never returned. Paginated with page and limit; status filters the returned page client-side. Use this first to find a connector_id for get_connector, test_connector, browse_connector, or the repository tools, and create_connector to add one. Read-only.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | Connectors per page; defaults to 25. |
| page | integer | – | 1-based page number; defaults to 1. |
| status | string | – | Keep only connectors in this health status; defaults to all. |
No output schema declared.
No examples provided.
list_data ~209
List visible datasets for the current user. Use search plus compact mode first for low-token dataset discovery, then get_data_schema on the chosen dataset_id. Read-only and non-destructive; lists only the active API key's organization and is not separately rate-limited. Returns the datasets array (dataset_id, name, status, source_names, connection_type, row_count, column_count, refreshable) plus count, total, matched_total, page, limit, and pages.
| Name | Type | Req | Description |
|---|---|---|---|
| compact | boolean | – | When true, request the low-token compact dataset discovery shape. |
| limit | integer | – | Results per page (default: all visible datasets, max 200 when set). |
| page | integer | – | Page number (default 1). |
| search | string | – | Deterministic lexical filter over dataset_id, name, and source_names. |
| source_name | string | – | Optional source-name filter for narrowed dataset discovery. |
| status | string | – | Optional dataset readiness filter such as ready or training. |
No output schema declared.
No examples provided.
list_datasets ~210
List registered datasets and their current model tier. Use search plus compact mode for low-token discovery, then poll status or use the primary data tools once you choose a dataset. Read-only and non-destructive; lists only the active API key's organization and is not separately rate-limited. Returns the datasets array with dataset_id, name, status, model_tier, source_names, column_count, and registered_at, plus count, total, page, limit, and pages.
| Name | Type | Req | Description |
|---|---|---|---|
| compact | boolean | – | When true, request the low-token compact dataset discovery shape. |
| limit | integer | – | Results per page (default: all visible datasets, max 200 when set). |
| page | integer | – | Page number (default 1). |
| search | string | – | Deterministic lexical filter over dataset_id, name, and source_names. |
| source_name | string | – | Optional source-name filter for narrowed dataset discovery. |
| status | string | – | Optional dataset readiness filter such as ready or training. |
No output schema declared.
No examples provided.
list_decisions ~190
Retrieve the Decision Memory audit trail — all logged decisions, most recent first. Use with_outcome_only=true to see only decisions where actual results have been recorded. outcome_delta = actual_outcome - expected_value: negative means worse than predicted. Read-only and non-destructive; not separately rate-limited. Returns decisions with id, chosen_action, expected_value, actual_outcome, outcome_delta, confidence, context, created_at, and outcome_recorded_at, plus total, page, limit, pages, and an accuracy_summary when outcomes exist.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | Canonical results per page (default 20, max 200). |
| page | integer | – | Page number (default 1). |
| page_size | integer | – | Results per page (default 20, max 100). |
| with_outcome_only | boolean | – | When true, return only decisions with recorded actual outcomes. |
No output schema declared.
No examples provided.
list_deployment_regions ~73
List available deployment providers and regions for the current organization. Read-only and non-destructive; not separately rate-limited. Call this before create_deployment to pick a valid provider/region pair. Returns the providers array with each provider's id, name, description, and regions (use a region id when creating).
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
list_devices ~120
List the devices registered to the caller's organization, paginated, together with the plan's device_limit and plan name. Requires an API-key identity (user-session keys fail with api_key_identity_required). Use a device's registration_id with revoke_device to free a slot. Read-only. Returns devices, device_count, total, page, pages, device_limit, and plan.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | Devices per page, up to 200; defaults to 25. |
| page | integer | – | 1-based page number; defaults to 1. |
No output schema declared.
No examples provided.
list_distributions ~63
List the probability distribution types supported in simulation variables — normal, uniform, triangular, lognormal, and fixed — each with its required parameters and a ready-to-use example. Read this before writing variable definitions for simulate, score, compare, or submit_job. Read-only.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
list_execution_policy_snapshots ~59
List the organization's persisted execution-policy snapshots in revision order with total_snapshots. Every policy update writes a new snapshot, so these ids are the lineage trail for replay and audit inspection; get_execution_policy returns only the current one. Read-only.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
list_jobs ~161
List the organization's async simulation jobs, newest first, with pagination (defaults page 1, limit 25, max 200) and an optional status filter such as queued, running, completed, failed, or cancelled. Each entry carries the job id, status, progress, and poll URL. Use get_job_status or poll_job to follow one job and get_job_result for its output. Read-only. Returns jobs plus total, page, limit, and pages.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | Jobs per page, up to 200; defaults to 25. |
| page | integer | – | 1-based page number; defaults to 1. |
| status | string | – | Optional job status filter such as queued, running, completed, failed, or cancelled. |
No output schema declared.
No examples provided.
list_models ~128
List the current Algenta model catalog, including deterministic utility models and any provider-backed routed entries with their routing, failover, timeout, and auth metadata, including capability-specific chat and embedding auth/header readiness. Use this before calling tokenize, count_tokens, chat_completions, responses, embeddings, embedding_similarity, or rerank. Read-only and non-destructive; calls share the plan's per-minute rate limit with the other LLM utility routes. Returns the catalog entries with model id, capabilities, and, for provider-backed entries, routing, failover, timeout, and auth-header readiness metadata.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
list_runs ~128
List the organization's recent simulation runs, newest first, with their recommended_action, confidence, expected_value, mode, and created_at. Optional filters narrow by mode (auto or expert) and status (completed, failed, running); limit caps the results (default 20, up to 100). Use get_run for one run's full detail and get_analytics for aggregate trends. Read-only. Returns runs plus total.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | Max results (1-100) |
| mode | string | – | Filter by mode |
| status | string | – | Keep only runs in this status. |
No output schema declared.
No examples provided.
list_runtime_libraries ~74
List executable local-runtime Mojo libraries. Use this when you need the runtime-backed compute catalog rather than the governed data/query tools. This surface is local/runtime-backed only.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | Maximum number of libraries to return after filtering. |
| search | string | – | Optional lexical filter over module names and function names. |
No output schema declared.
No examples provided.
list_skills ~81
List the skill capabilities in the unified capability plane — prompt skills enabled for the caller's organization with their names, bindings, and execution owners. This is list_capabilities narrowed to kind=skill. Use enable_skill to add one and disable_skill to remove one. Read-only. Returns the skill catalog entries with capability_id, name, binding, and execution owner.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
list_sources ~122
Advanced tool. List all registered data sources for this org with their schema summaries. Use this to discover available tables before calling query_data or register_source. Read-only and non-destructive; not separately rate-limited. Returns the sources array with each source's id, name, and schema summary (columns, roles, detected join keys), plus count, total, page, limit, and pages.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | Results per page (default: all visible sources, max 200 when set). |
| page | integer | – | Page number (default 1). |
No output schema declared.
No examples provided.
list_team_members ~126
List the active users of the caller's organization with user_id, name, email, role, and status. Called with no arguments it returns the full member array; passing page or limit switches to a paginated envelope {members, total, page, limit, pages} (defaults page 1, limit 25). Use the returned user_id with update_team_member_role or remove_team_member. Read-only.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | Members per page (default 25 when paginating). |
| page | integer | – | 1-based page number; enables the paginated envelope. |
No output schema declared.
No examples provided.
list_templates ~59
List the built-in simulation templates available to the active API key, each with its id and intended use. A template id pre-fills a simulation request, so start here instead of hand-writing variables for common cases such as a product launch. Read-only.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
list_triggers ~146
List all registered triggers with their current status, last-checked time, and last-fired simulation result summary. Read-only and non-destructive; not separately rate-limited. Use register_trigger to add one and pause_trigger to silence one without deleting. Returns triggers with trigger_id, name, status, condition, last_checked_at, last_fired_at, and last_result_summary, plus count, total, page, limit, and pages.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | Results per page (default: all visible triggers, max 200 when set). |
| page | integer | – | Page number (default 1). |
| status | string | – | Filter by trigger status (default: all). |
No output schema declared.
No examples provided.
log_decision ~278
Persist a decision to the Decision Memory audit trail. Link to a simulation run_id to bind the full DecisionPlan context. Call record_outcome later to close the feedback loop and measure prediction accuracy. Every logged decision is immutably hashed — no tampering possible. Returns decision_id, chosen_action, expected_value, confidence, and created_at.
| Name | Type | Req | Description |
|---|---|---|---|
| chosen_action | string | yes | The action that was decided upon. |
| confidence | number | – | Confidence score (0–1) from the simulation. |
| context | string | – | Business context — what was the situation when this decision was made? |
| expected_value | number | – | Expected outcome value at decision time. |
| options_considered | array | – | All option names that were evaluated. |
| rationale | string | – | Explanation of why this option was chosen. |
| request_hash | string | – | SHA-256 input fingerprint from the simulation. |
| result_hash | string | – | SHA-256 output fingerprint from the simulation. |
| risk_p5 | number | – | 5th-percentile downside at decision time. |
| risk_p95 | number | – | 95th-percentile upside at decision time. |
| risk_pol | number | – | Probability of loss (0–1) at decision time. |
| run_id | string | – | Simulation run_id that produced this decision (from simulate or recommend). |
No output schema declared.
No examples provided.
onboard_dataset ~223
Register a dataset for semantic querying. Pass column names, inline records, or raw CSV. The engine profiles roles automatically and starts background training. Queries work immediately via a fallback model — accuracy improves once schema-specific training completes (poll status with list_datasets). Registration persists the dataset under the active API key's organization. Use connect_data for live connections instead of inline rows. Returns dataset_id, schema_hash, status, model_tier, column_count, and suggested_aliases.
| Name | Type | Req | Description |
|---|---|---|---|
| async_train | boolean | – | Start background semantic training immediately (default: true). |
| columns | array | – | Column names only — fastest path, no data required. |
| csv | string | – | Raw CSV text with header row. |
| domain_aliases | object | – | Optional map of abbreviation → expansions. Example: {"ppa": ["per", "person", "average"]}. Auto-suggested if omitted. |
| name | string | – | Human-readable name for this dataset. |
| records | array | – | Sample rows as JSON records (list of dicts). Up to 200 rows. |
No output schema declared.
No examples provided.
pause_trigger ~57
Pause or resume an existing trigger without deleting it. Returns trigger_id and the updated status.
| Name | Type | Req | Description |
|---|---|---|---|
| paused | boolean | – | Set true to pause, false to resume (default: true). |
| trigger_id | string | yes | Trigger ID to update. |
No output schema declared.
No examples provided.
plan_decision ~103
Run a validated simulation-style request (the same payload contract as simulate) but return only the structured DecisionPlan summary — the compact plan object with recommended action and calibrated confidence, without the full DecisionEnvelope metrics. Use this when the caller needs the plan summary for a dashboard or a follow-up plan_decision-to-log_decision flow; use simulate for the full envelope. Synchronous deterministic compute governed by the plan's per-minute rate limit and monthly quota; nothing is persisted.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
poll_job ~124
Wait for an async simulation job to reach a terminal state. Returns the final result when the job completes, or the terminal status when it fails, is cancelled, or times out. Read-only: it polls the job's status endpoints and changes nothing. Use get_job_status for a single non-blocking check.
| Name | Type | Req | Description |
|---|---|---|---|
| job_id | string | yes | UUID of the async job |
| poll_interval_seconds | number | – | Delay between status checks while the job is still queued or running. |
| timeout_seconds | number | – | Maximum wall-clock time to wait before returning a timed_out response. |
No output schema declared.
No examples provided.
preview_browse_connector ~87
Browse one inline connector definition without saving it to discover files, tables, endpoints, or items. This opens a real connection to the source and is rate-limited per organization; nothing is saved. Use browse_connector for saved connectors. Returns connector_type, items, total, message, labels, and discovery metadata.
| Name | Type | Req | Description |
|---|---|---|---|
| config | object | – | – |
| connector_type | string | yes | – |
No output schema declared.
No examples provided.
preview_test_connector ~108
Run a real connectivity test against an inline connector definition without saving anything — the dry run for create_connector. This opens an actual connection to the source, is rate-limited per organization, and caches successful outcomes briefly. Nothing is persisted. Returns success, message, latency_ms, status, error_type, and recoverable; call create_connector once the definition passes.
| Name | Type | Req | Description |
|---|---|---|---|
| config | object | – | Inline connection settings and credentials to test. |
| connector_type | string | yes | Connector type id to test. |
No output schema declared.
No examples provided.
product_agent_run ~262
Execute a natural-language task synchronously with the simple product agent and return a compact task result. The agent picks one tool from the task wording (optimize for best/maximum-style tasks, simulate for risk/forecast-style, search for find/lookup-style, otherwise calculate), runs it, and formats the answer as text, json, or markdown. Use this for one-shot task execution; use create_agent_run when you need a paused or approval-gated lifecycle, and get_agent_run to re-fetch the persisted record. The run, its step log, events, and a replayable checkpoint are persisted under the caller's organization. Returns run_id, status (completed on success), result, the step list, tools_used, and latency_ms.
| Name | Type | Req | Description |
|---|---|---|---|
| context | object | – | Optional structured context or data for the task. |
| max_steps | integer | – | Maximum execution steps, 1-50; defaults to 10. |
| output_format | string | – | Result format: text (default), json, or markdown. |
| task | string | yes | What the agent should do, in plain words (min 5 characters). |
| tools | array | – | Restrict the tools the agent may pick from; defaults to search, simulate, optimize, calculate, summarize. |
No output schema declared.
No examples provided.
product_decision ~357
Recommend an action for a business decision from plain inputs, and return the risk summary behind it. Each input becomes a simulation variable: fixed at value, or triangular when low and high bounds are given; inputs named cost/costs/expense/expenses/spending are subtracted in the objective. The engine evaluates scenarios (default 10000) and maps the loss probability to an action: over 50% -> reject, over the risk_tolerance threshold (low 5%, medium 15%, high 30%) -> pause, otherwise proceed. Use simulate for the raw distribution and plan_decision for the structured plan. Synchronous deterministic compute; nothing is persisted. Returns decision_id, action, confidence, reasoning and why bullets, expected_outcome, downside_risk (p5), upside_potential (p95), and probability_of_loss.
| Name | Type | Req | Description |
|---|---|---|---|
| engine | string | – | Simulation engine; auto (default) selects one from the data shape. Options: monte_carlo, lhs, qmc_sobol, bootstrap, mcmc, importance_sampling, time_series, sensitivity. |
| inputs | array | yes | Business inputs as {name, value, low?, high?, unit?} objects; low+high turn a value into a triangular uncertainty range. |
| label | string | – | Optional caller label stored with the decision. |
| objective | string | – | Goal label such as maximize_value, minimize_risk, maximize_profit, or minimize_cost; defaults to maximize_value. |
| risk_tolerance | string | – | Loss-probability ceiling for a proceed recommendation: low, medium (default), or high. |
| scenarios | integer | – | Scenarios to evaluate, 1000-1000000; defaults to 10000. |
No output schema declared.
No examples provided.
product_forecast ~273
Forecast a business metric horizon periods ahead from its historical series and return per-period point forecasts with confidence intervals. The trend comes from the last up-to-6 history values, volatility from the mean absolute period change, and a 5000-scenario simulation quantifies uncertainty; seasonality=true applies an alternating +/-5% seasonal factor. Use query_data to build the history from a connected dataset first. Synchronous deterministic compute; nothing is persisted. Returns baseline (most recent value), forecast_mean (final period), total_change_pct, and one {period, forecast, lower_bound, upper_bound, trend} item per period with trend up, down, or stable.
| Name | Type | Req | Description |
|---|---|---|---|
| confidence_level | number | – | Confidence interval width, 0.5-0.99; defaults to 0.90. The z-value comes from the nearest of 0.90, 0.95, 0.99. |
| history | array | yes | Historical values in chronological order, most recent last; 3-1000 points. |
| horizon | integer | – | How many periods ahead to forecast, 1-120; defaults to 12. |
| metric | string | yes | Name of what you are forecasting, e.g. monthly_revenue. |
| seasonality | boolean | – | Account for seasonal patterns; defaults to true. |
No output schema declared.
No examples provided.
How do I install the Algenta MCP Server server?
Algenta MCP Server is a hosted endpoint at https://api.algenta.ai/mcp, so there is nothing to install locally. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
remote · api.algenta.ai
claude mcp add --transport http thyn-ai-algenta 'https://api.algenta.ai/mcp'
What is the Algenta MCP Server server?
Algenta MCP Server is listed in the public MCP registry as io.github.thyn-ai/algenta. Governed data discovery, exact queries, decisions, simulations, and runtime utilities over MCP. This page covers its hosted endpoint (https://api.algenta.ai/mcp).
Is the Algenta MCP Server server safe to use?
Algenta MCP Server scores 78 out of 100 on VerifyMCP. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the Algenta MCP Server server expose?
Algenta MCP Server exposes 140 tools: onboard_dataset, list_datasets, get_dataset_status, retrain_dataset, connect_data, and 135 more. Their descriptions and schemas cost roughly 20,559 tokens of context every time the server is loaded.
Does the Algenta MCP Server server require authentication?
Yes. Algenta MCP Server asked us for credentials when we connected, so you will need to authorise it in your MCP client before it can do anything.
Is the Algenta MCP Server server still maintained?
Algenta MCP Server is still listed as active in the MCP registry. We last reached this channel on 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.