Algenta MCP Server

Run the simple product optimization helper and return the best variable values.

Run the simple product retrieval helper over caller-supplied documents or a collection id.

Query persisted checkpoints across Algenta agent runs.

Query canonical mission-event records across persisted Algenta agent runs.

Query runtime telemetry batches across persisted Algenta agent runs.

Execute several governed exact queries in one API call. Use this for multi-metric prompts after choosing a dataset with list_data and get_data_summary. Each item reuses the same structured query contract as query_data; defaults may provide shared dataset_id, filter, limit, and order.

Execute a structured query against connected data sources. Convert the user's question to a structured intent and call this tool — do NOT try to write SQL or parse column names yourself. The engine resolves column meaning from mathematical relationships and statistical structure only. It works on any dataset without configuration. The governed filter shape is a record-predicate contract over normalized rows, not a SQL predicate language, so it also applies to Redis and other non-SQL sources. Structural roles (use in metric.role): - derived_measure: the main financial/operational aggregate (revenue, spend, value) - base_measure: counts, quantities, discrete amounts - unit_measure: per-unit prices, rates - ratio: percentages, margins, fill rates (0-1 range) - metric: let the engine pick the best numeric column If clarification_required is true, or if confidence < 0.85, check the candidates list and ask the user to clarify. Never fabricate column names or SQL.

Query one persisted repository snapshot for dependency, dependent, and change-risk graph edges.

Execute a constrained read-only SQL rowset query over authorized datasets. Use this only for wide reports that do not fit the governed exact-query surface. SQL must be a single SELECT/WITH statement over the provided dataset aliases.

Compare multiple named actions/options and get a ranked recommendation. Use when you need to choose between two or more alternatives with uncertainty.

Close the feedback loop: record what actually happened after a decision was made. Sets actual_outcome and computes outcome_delta = actual - expected. Over time this data measures prediction accuracy and reveals systematic biases.

Issue a compatibility credit batch for a quota-governed managed runtime.

Refresh a saved dataset from its original database/API/object-store origin.

Advanced tool. Register a data source and get full schema profiling + join detection. Profiles every column (type, cardinality, fill rate, distribution). Detects formula relationships (A×B≈C) within the source. Detects join keys to every already-registered source automatically. After registration the source is queryable by name via query_data. Safe to call multiple times — re-registration is a no-op if data is unchanged.

Register a real-time trigger that watches a data source for a threshold condition. When the condition is met, the engine auto-runs the simulation template and optionally fires a webhook. Examples: 'alert me when monthly revenue drops below $80k', 'simulate expansion if Downtown revenue exceeds $200k'.

Remove one team member from the current organization by user id.

Rerank caller-supplied document embeddings deterministically.

Resolve a Hugging Face artifact path through the Algenta compatibility-ring artifact bridge. Defaults to cache-only lookup and never downloads unless local_files_only=false.

Run the unified Algenta utility response surface over deterministic tokenization or lexical embeddings.

Resume a paused Algenta agent run.

Re-trigger semantic training for a dataset. Use after schema changes, alias updates, or to force a fresh model build.

Revoke one API key by id.

Revoke one registered device by registration id for the current organization.

Route an objective to the best unified capability with fallbacks and an authoritative execution_owner.

Run repository pipeline then apply the result, returning the canonical repository envelope.

Run the repository snapshot->triage->plan->simulate chain and return the canonical repository envelope.

Score a single simulation request with explicit weights and return the decision envelope plus score breakdown.

Run a Monte Carlo simulation and get a structured decision recommendation. Use for: quantifying risk in a decision, comparing expected outcomes, getting probability-weighted recommendations.

Simulate repository patch risk and return the gated DecisionEnvelope, resolving snapshot_id from the decision plan when omitted.

Simulate an in-flight repository patch and return the canonical repository envelope.

Submit a long-running async simulation job. Use for n_simulations > 500,000 or when you need a callback. Returns a job_id — poll with get_job_status.

Test a saved capability binding or preview-test an unsaved one.

Run a real connectivity test for one saved connector and persist its live/error status.

Send a test webhook payload to a callback URL and return the delivery result.

Tokenize UTF-8 text with a supported deterministic Algenta tokenizer model.

Triage a repository snapshot into a bounded workspace evidence bundle with suspect files and symbols.

Update one saved connector name, description, visibility, or config.

Update one or more execution-policy thresholds for the active organization.

Update the current user name and or organization name for the active API key.

Update one current organization team member role by user id.