io.github.TsvetanG2/cognigy-ai-mcp-management-server
NPM · COGNIGY-AI-MCP-MANAGEMENT-SERVER · SCANNED OCT 2
MCP server for Cognigy.AI - 132 tools to build, configure & operate conversational AI agents
Available components
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security89
- No malware found by supply-chain analysis.Pass
- CVE check failed: a known high-severity CVE affects axios 1.18.1, reached via @cognigy/rest-api-client > axios. A fixed version is available. View diagnostics → Fail
- No install/post-install scripts declared.Pass
- 61 of 137 dependencies flagged as unhealthy (2 deprecated). View diagnostics → Partial
Provenance & Transparency48
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 67 days ago).Pass
- Publishes a security disclosure policy (SECURITY.md).Pass
Schema Quality & AI Usability79
- AI-judged instruction clarity (excellent).Pass
- Context-footprint check failed: tool/resource definitions use about 14859 tokens (~107/item across 138 items; 138 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 Management80
- Stability observed for 24 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- All 18 tool(s) whose name or description implies an irreversible operation declare an MCP destructiveHint annotation.Pass
- An AI judge read all 138 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
How do I install the io.github.TsvetanG2/cognigy-ai-mcp-management-server server?
io.github.TsvetanG2/cognigy-ai-mcp-management-server runs locally as an npm package, launched with npx -y cognigy-ai-mcp-management-server. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
npm · cognigy-ai-mcp-management-server
claude mcp add tsvetang2-cognigy-ai-mcp-management-server -- npx -y cognigy-ai-mcp-management-server
{
"mcpServers": {
"tsvetang2-cognigy-ai-mcp-management-server": {
"command": "npx",
"args": [
"-y",
"cognigy-ai-mcp-management-server"
]
}
}
} {
"servers": {
"tsvetang2-cognigy-ai-mcp-management-server": {
"command": "npx",
"args": [
"-y",
"cognigy-ai-mcp-management-server"
]
}
}
} codex mcp add tsvetang2-cognigy-ai-mcp-management-server -- npx -y cognigy-ai-mcp-management-server
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"tsvetang2-cognigy-ai-mcp-management-server": {
"type": "local",
"command": [
"npx",
"-y",
"cognigy-ai-mcp-management-server"
],
"enabled": true
}
}
} openclaw mcp add tsvetang2-cognigy-ai-mcp-management-server --command npx --arg -y --arg cognigy-ai-mcp-management-server
mcp_servers:
tsvetang2-cognigy-ai-mcp-management-server:
command: "npx"
args: ["-y", "cognigy-ai-mcp-management-server"] {
"McpServers": {
"tsvetang2-cognigy-ai-mcp-management-server": {
"Transport": "stdio",
"Command": "npx",
"Arguments": [
"-y",
"cognigy-ai-mcp-management-server"
]
}
}
} assistant mcp add tsvetang2-cognigy-ai-mcp-management-server -t stdio -c npx -a -y cognigy-ai-mcp-management-server
{
"mcpServers": {
"tsvetang2-cognigy-ai-mcp-management-server": {
"command": "npx",
"args": [
"-y",
"cognigy-ai-mcp-management-server"
]
}
}
} 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.
- 2 Oct 26 −3
- Stability: pass → 0.80 functional
- 1 Oct 26 +1
- CVE-2026-101908 affects this package: high ▼ security
- CVE-2026-101909 affects this package: high ▼ security
- CVE-2026-101901 affects this package: high ▼ security
- CVE-2026-101902 affects this package: high ▼ security
- CVE-2026-101900 affects this package: high ▼ security
- CVE-2026-101905 affects this package: high ▼ security
- CVE-2026-101899 affects this package: high ▼ security
- CVE-2026-101904 affects this package: high ▼ security
- CVE-2026-101906 affects this package: high ▼ security
- CVE-2026-101898 affects this package: high ▼ security
- CVE-2026-101907 affects this package: high ▼ security
- CVE-2026-101903 affects this package: high ▼ security
- Stability: 0.97 → pass security
- 29 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 90 to 93. That category is still filling its 30-day observation window: 27 days of observed history at the previous scan, 28 at this one. The score rises as the window fills, whether or not the server changes.
- 28 Sept 26 −2
- We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
- 26 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 80 to 83. That category is still filling its 30-day observation window: 24 days of observed history at the previous scan, 25 at this one. The score rises as the window fills, whether or not the server changes.
- 25 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
- 24 Sept 26 −3
- Stability: pass → 0.77 functional
- 23 Sept 26 0
- Stability: 0.97 → pass security
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 2 Oct 2026 · Analysed npm/cognigy-ai-mcp-management-server@0.1.4
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | npm |
Background: How many MCP packages publish verified provenance →
Vulnerabilities 14 findings
| ID | CVE | Severity | Vector | Fix available |
|---|---|---|---|---|
| GHSA-3pq3-5fj3-cg6v | CVE-2026-101898 | high | yes | |
| GHSA-44g4-m2mj-wpvx | CVE-2026-101899 | medium | yes | |
| GHSA-4hqw-qxg8-jxx2 | CVE-2026-101900 | medium | yes | |
| GHSA-542g-h47m-68v8 | CVE-2026-101901 | high | yes | |
| GHSA-9fr6-4gfg-395g | CVE-2026-101902 | medium | yes | |
| GHSA-c29m-xwm3-cm6r | CVE-2026-101903 | high | yes | |
| GHSA-j8rh-479h-cp32 | CVE-2026-101904 | medium | yes | |
| GHSA-m8m8-qj5v-23w3 | CVE-2026-101905 | high | yes | |
| GHSA-mghh-pgcx-3jjj | CVE-2026-101906 | high | yes | |
| GHSA-r4gj-5m52-g5wh | CVE-2026-101907 | high | yes | |
| GHSA-vh66-26gq-q6x8 | CVE-2026-101908 | medium | yes | |
| GHSA-x97p-jq2g-jp4f | CVE-2026-101909 | high | yes | |
| GHSA-hmw2-7cc7-3qxx | CVE-2026-12143 | high | CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:N | yes |
| GHSA-w5hq-g745-h8pq | CVE-2026-41907 | high | CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:N | yes |
Background: What a vulnerability scan can and cannot prove →
Dependencies 137 packages
| Packages resolved | 137 |
|---|---|
| Deprecated | 2 |
| Stale | 58 |
| No linked repository | 2 |
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
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_flow ~53
Gets detailed metadata about a specific Cognigy.AI flow. Returns flow configuration, locale info, and timestamps. Use this to inspect a flow before modifying it.
| Name | Type | Req | Description |
|---|---|---|---|
| flowId | string | yes | The flow ID to retrieve |
No output schema declared.
No examples provided.
get_flow_settings ~57
Gets the settings/configuration of a Cognigy.AI flow. Returns NLU settings, thresholds, and other flow-level configurations. Use this before updating flow settings.
| Name | Type | Req | Description |
|---|---|---|---|
| flowId | string | yes | The flow ID to retrieve settings for |
No output schema declared.
No examples provided.
get_function ~44
Gets detailed configuration of a specific Cognigy.AI Function. Returns the function code, settings, and runtime configuration.
| Name | Type | Req | Description |
|---|---|---|---|
| functionId | string | yes | The function ID to retrieve |
No output schema declared.
No examples provided.
get_function_instance ~61
Gets detailed information about a specific Cognigy.AI Function instance. Returns execution status, timing, input parameters, and output/error.
| Name | Type | Req | Description |
|---|---|---|---|
| functionId | string | yes | The function ID |
| functionInstanceId | string | yes | The function instance ID to retrieve |
No output schema declared.
No examples provided.
get_handover_provider ~50
Gets detailed information about a specific Cognigy.AI handover provider. Returns provider type, configuration, and connection settings.
| Name | Type | Req | Description |
|---|---|---|---|
| providerId | string | yes | The handover provider ID to retrieve |
No output schema declared.
No examples provided.
get_handover_service ~51
Gets detailed information about a specific Cognigy.AI handover service. Returns service type, configuration schema, and supported features.
| Name | Type | Req | Description |
|---|---|---|---|
| serviceId | string | yes | The handover service ID to retrieve |
No output schema declared.
No examples provided.
get_intent ~89
Gets detailed configuration of a specific intent in a Cognigy.AI flow. Returns the intent's conditions, rules, confirmation sentences, and settings. Use this to inspect NLU behavior before modifying.
| Name | Type | Req | Description |
|---|---|---|---|
| flowId | string | yes | The flow ID containing the intent |
| intentId | string | yes | The intent ID to retrieve |
| localeId | string | – | Optional locale ID for localized content |
No output schema declared.
No examples provided.
get_knowledge_chunk ~91
Gets the full content of a specific Cognigy.AI knowledge chunk. Returns the complete text, metadata, and source information. Use this to inspect what content is being used in RAG searches.
| Name | Type | Req | Description |
|---|---|---|---|
| chunkId | string | yes | The knowledge chunk ID to retrieve |
| knowledgeStoreId | string | yes | The knowledge store ID |
| sourceId | string | yes | The knowledge source ID containing the chunk |
No output schema declared.
No examples provided.
get_knowledge_connector ~66
Gets detailed configuration of a specific Cognigy.AI knowledge connector. Returns connector type, schedule, connection settings, and run status.
| Name | Type | Req | Description |
|---|---|---|---|
| connectorId | string | yes | The knowledge connector ID to retrieve |
| knowledgeStoreId | string | yes | The knowledge store ID containing the connector |
No output schema declared.
No examples provided.
get_knowledge_query_metrics ~116
Gets Cognigy.AI Knowledge AI query metrics. Returns aggregated knowledge search/RAG query counts for a project or entire organization.
| Name | Type | Req | Description |
|---|---|---|---|
| endDate | string | – | End date for metrics (ISO 8601 format) |
| projectId | string | – | Project ID for project-level metrics. Omit for organization-wide metrics. |
| startDate | string | – | Start date for metrics (ISO 8601 format) |
| timezone | string | – | Timezone for aggregation (e.g., 'UTC', 'America/New_York') |
No output schema declared.
No examples provided.
get_knowledge_source ~67
Gets detailed information about a specific Cognigy.AI knowledge source. Returns source metadata, processing status, chunk count, and ingestion details.
| Name | Type | Req | Description |
|---|---|---|---|
| knowledgeStoreId | string | yes | The knowledge store ID containing the source |
| sourceId | string | yes | The knowledge source ID to retrieve |
No output schema declared.
No examples provided.
get_knowledge_store ~51
Gets detailed configuration of a specific Cognigy.AI knowledge store. Returns store settings, language, embedding model, and source counts.
| Name | Type | Req | Description |
|---|---|---|---|
| knowledgeStoreId | string | yes | The knowledge store ID to retrieve |
No output schema declared.
No examples provided.
get_latest_log_entries ~109
Gets the latest execution log entries from a Cognigy.AI project. Use this for debugging flow execution, viewing errors, or monitoring agent behavior.
| Name | Type | Req | Description |
|---|---|---|---|
| flowName | string | – | Filter logs by flow name |
| limit | integer | – | Maximum number of log entries to return (1-100, default 25) |
| projectId | string | yes | The project ID to retrieve logs from |
| type | array | – | Filter by log level(s): debug, info, warn, error |
No output schema declared.
No examples provided.
get_llm ~53
Gets detailed configuration of a specific Cognigy.AI large language model. Returns provider settings, model type, connection details, and fallback configuration.
| Name | Type | Req | Description |
|---|---|---|---|
| largeLanguageModelId | string | yes | The LLM ID to retrieve |
No output schema declared.
No examples provided.
get_nlu_connector ~56
Gets detailed configuration of a specific Cognigy.AI NLU connector. Returns type, settings, and connection details for external NLU service integration.
| Name | Type | Req | Description |
|---|---|---|---|
| nluConnectorId | string | yes | The NLU connector ID to retrieve |
No output schema declared.
No examples provided.
get_node ~88
Gets detailed configuration of a specific node in a Cognigy.AI flow. Returns the node's type, label, config fields, and settings. Use this to inspect node behavior before modifying it.
| Name | Type | Req | Description |
|---|---|---|---|
| flowId | string | yes | The flow ID containing the node |
| localeId | string | – | Optional locale ID for localized content |
| nodeId | string | yes | The node ID to retrieve |
No output schema declared.
No examples provided.
get_node_descriptors ~74
Gets all available node types (blueprints) that can be created in a Cognigy.AI flow. Returns node type definitions including their fields, appearance, and constraints. Use this to understand what nodes can be added to a flow.
| Name | Type | Req | Description |
|---|---|---|---|
| flowId | string | yes | The flow ID to get available node types for |
No output schema declared.
No examples provided.
get_nodes ~89
Lists all nodes in a Cognigy.AI flow. Nodes are the building blocks of conversation logic (Say, Question, If, Code, etc.). Use this to explore flow structure before reading specific nodes or modifying the flow.
| Name | Type | Req | Description |
|---|---|---|---|
| flowId | string | yes | The flow ID to list nodes from |
| limit | integer | – | Maximum number of nodes to return (1-100, default 25) |
No output schema declared.
No examples provided.
get_package ~43
Gets detailed information about a Cognigy.AI package including its name, description, and contained resources.
| Name | Type | Req | Description |
|---|---|---|---|
| packageId | string | yes | The package ID to get details for |
No output schema declared.
No examples provided.
get_playbook ~52
Gets detailed Cognigy.AI playbook configuration including all steps and assertions. Use this to understand what a playbook tests before running it.
| Name | Type | Req | Description |
|---|---|---|---|
| playbookId | string | yes | The playbook ID to retrieve |
No output schema declared.
No examples provided.
get_playbook_run ~70
Gets detailed results of a Cognigy.AI playbook run including step-by-step assertion outcomes. Use this to analyze test failures and debug conversation flows.
| Name | Type | Req | Description |
|---|---|---|---|
| playbookId | string | yes | The playbook ID |
| playbookRunId | string | yes | The playbook run ID to retrieve |
No output schema declared.
No examples provided.
get_snapshot ~53
Gets detailed information about a specific Cognigy.AI snapshot. Returns name, description, hash, and packaging status. Use this to inspect a version before deployment.
| Name | Type | Req | Description |
|---|---|---|---|
| snapshotId | string | yes | The snapshot ID to retrieve |
No output schema declared.
No examples provided.
get_snapshot_resources ~97
Lists resources (flows, locales, NLU connectors, LLMs) contained in a Cognigy.AI snapshot. Use this to inspect what a snapshot contains before restoring or to compare versions.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | Maximum number of resources to return (1-100, default 25) |
| resourceType | string | yes | Type of resources to list |
| snapshotId | string | yes | The snapshot ID to list resources from |
No output schema declared.
No examples provided.
get_task ~70
Gets detailed status of a specific Cognigy.AI async task. Returns progress, status, and failure reason if applicable. Use this to poll long-running operations to completion.
| Name | Type | Req | Description |
|---|---|---|---|
| projectId | string | – | Optional project ID to scope the query |
| taskId | string | yes | The task ID to retrieve |
No output schema declared.
No examples provided.
get_transcript ~100
Assembles a human-readable transcript for a Cognigy.AI session. Shows the conversation flow between user and bot in chronological order. Use this for reviewing conversation quality or debugging.
| Name | Type | Req | Description |
|---|---|---|---|
| format | string | – | Output format: 'full' includes all metadata, 'compact' shows just the conversation flow |
| projectId | string | – | Optional project ID to scope the query |
| sessionId | string | yes | The session ID to get transcript for |
No output schema declared.
No examples provided.
inject_context ~90
Injects context data into a Cognigy.AI session. Context is shared state accessible by flow nodes. Use this to set user data, preferences, or state before/during conversations.
| Name | Type | Req | Description |
|---|---|---|---|
| context | object | yes | The context object to inject (key-value pairs) |
| sessionId | string | yes | The session ID to inject context into |
| userId | string | yes | The user ID for the session |
No output schema declared.
No examples provided.
list_audit_events ~195
Lists Cognigy.AI audit events. Audit events track all changes made to resources (flows, intents, endpoints, etc.) by users. Useful for compliance and debugging.
| Name | Type | Req | Description |
|---|---|---|---|
| endDate | string | – | End date for audit events (ISO 8601 format) |
| eventType | string | – | Filter by event type (e.g., 'create', 'update', 'delete') |
| limit | number | – | Maximum number of events to return (default: 25, max: 100) |
| projectId | string | – | Filter audit events by project ID |
| resourceType | string | – | Filter by resource type (e.g., 'flow', 'intent', 'endpoint') |
| skip | number | – | Number of items to skip for pagination |
| startDate | string | – | Start date for audit events (ISO 8601 format) |
| userId | string | – | Filter audit events by user ID |
No output schema declared.
No examples provided.
list_connections ~133
Lists Cognigy.AI connections (external service integrations like databases, APIs, etc.). Connections store credentials securely. Use this to find available connections for a project or organization.
| Name | Type | Req | Description |
|---|---|---|---|
| filter | string | – | Filter connections by name |
| limit | number | – | Maximum number of connections to return (default: 25, max: 100) |
| projectId | string | – | Filter connections by project ID. Omit for all accessible connections. |
| resourceLevel | string | – | Scope: 'organisation' for global connections, 'project' for project-specific |
| skip | number | – | Number of items to skip for pagination |
No output schema declared.
No examples provided.
list_contact_profiles ~99
Lists Cognigy.AI contact profiles. Contact profiles store user data across sessions (name, preferences, conversation history metadata).
| Name | Type | Req | Description |
|---|---|---|---|
| filter | string | – | Filter profiles by contact ID or other fields |
| limit | number | – | Maximum number of profiles to return (default: 25, max: 100) |
| projectId | string | – | Filter contact profiles by project ID |
| skip | number | – | Number of items to skip for pagination |
No output schema declared.
No examples provided.
list_endpoints ~86
Lists all endpoints in a Cognigy.AI project. Endpoints are channel connectors (Webchat, REST, Voice, etc.) that expose flows/agents to users. Use this to discover deployed channels.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | Maximum number of endpoints to return (1-100, default 25) |
| projectId | string | yes | The project ID to list endpoints from |
No output schema declared.
No examples provided.
list_extensions ~96
Lists Cognigy.AI Extensions. Extensions are custom node packages that add new capabilities to flows (e.g., integrations, custom actions).
| Name | Type | Req | Description |
|---|---|---|---|
| filter | string | – | Filter extensions by name |
| limit | number | – | Maximum number of extensions to return (default: 25, max: 100) |
| projectId | string | – | Filter extensions by project ID |
| skip | number | – | Number of items to skip for pagination |
No output schema declared.
No examples provided.
list_flows ~74
Lists all flows in a Cognigy.AI project. Flows are conversation logic containers. Use this to discover flows before reading or modifying them.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | Maximum number of flows to return (1-100, default 25) |
| projectId | string | yes | The project ID to list flows from |
No output schema declared.
No examples provided.
list_function_instances ~83
Lists running and completed instances of a Cognigy.AI Function. Shows execution history, status, and results.
| Name | Type | Req | Description |
|---|---|---|---|
| functionId | string | yes | The function ID to list instances for |
| limit | number | – | Maximum number of instances to return (default: 25, max: 100) |
| skip | number | – | Number of items to skip for pagination |
No output schema declared.
No examples provided.
list_functions ~99
Lists Cognigy.AI Functions. Functions are custom code modules that can be triggered to run computations, integrations, or scheduled jobs outside of flow execution.
| Name | Type | Req | Description |
|---|---|---|---|
| filter | string | – | Filter functions by name |
| limit | number | – | Maximum number of functions to return (default: 25, max: 100) |
| projectId | string | – | Filter functions by project ID |
| skip | number | – | Number of items to skip for pagination |
No output schema declared.
No examples provided.
list_handover_providers ~93
Lists Cognigy.AI handover providers. Handover providers enable live agent escalation (e.g., Salesforce, Genesys, RingCentral).
| Name | Type | Req | Description |
|---|---|---|---|
| limit | number | – | Maximum number of providers to return (default: 25, max: 100) |
| projectId | string | – | Filter handover providers by project ID |
| skip | number | – | Number of items to skip for pagination |
No output schema declared.
No examples provided.
list_handover_services ~71
Lists available Cognigy.AI handover services. Handover services are the supported integrations for live agent escalation.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | number | – | Maximum number of services to return (default: 25, max: 100) |
| skip | number | – | Number of items to skip for pagination |
No output schema declared.
No examples provided.
list_intents ~113
Lists all intents in a Cognigy.AI flow. Intents are the NLU triggers that match user utterances to flow logic. Use this to explore NLU configuration before training or modifying intents.
| Name | Type | Req | Description |
|---|---|---|---|
| flowId | string | yes | The flow ID to list intents from |
| includeChildren | boolean | – | Include child intents in the results |
| limit | integer | – | Maximum number of intents to return (1-100, default 25) |
| localeId | string | – | Optional locale ID for localized content |
No output schema declared.
No examples provided.
list_knowledge_chunks ~120
Lists knowledge chunks in a Cognigy.AI knowledge store. Chunks are the actual text segments used for RAG retrieval, created by splitting source documents.
| Name | Type | Req | Description |
|---|---|---|---|
| filter | string | – | Filter chunks by text content |
| knowledgeStoreId | string | yes | The knowledge store ID to list chunks from |
| limit | number | – | Maximum number of chunks to return (default: 25, max: 100) |
| skip | number | – | Number of items to skip for pagination |
| sourceId | string | – | Filter chunks by source ID |
No output schema declared.
No examples provided.
list_knowledge_connectors ~107
Lists Cognigy.AI knowledge connectors for automated content ingestion. Connectors can pull content from external sources like SharePoint, Confluence, or custom APIs.
| Name | Type | Req | Description |
|---|---|---|---|
| filter | string | – | Filter connectors by name |
| knowledgeStoreId | string | yes | The knowledge store ID to list connectors from |
| limit | number | – | Maximum number of connectors to return (default: 25, max: 100) |
| skip | number | – | Number of items to skip for pagination |
No output schema declared.
No examples provided.
list_knowledge_sources ~113
Lists knowledge sources in a Cognigy.AI knowledge store. Sources are documents (PDFs, web pages, text files) that have been ingested and chunked for RAG retrieval.
| Name | Type | Req | Description |
|---|---|---|---|
| filter | string | – | Filter sources by name |
| knowledgeStoreId | string | yes | The knowledge store ID to list sources from |
| limit | number | – | Maximum number of sources to return (default: 25, max: 100) |
| skip | number | – | Number of items to skip for pagination |
No output schema declared.
No examples provided.
list_knowledge_stores ~109
Lists Cognigy.AI Knowledge AI stores. Knowledge stores are containers for RAG (Retrieval-Augmented Generation) content used by AI Agents to answer questions from your data.
| Name | Type | Req | Description |
|---|---|---|---|
| filter | string | – | Filter knowledge stores by name |
| limit | number | – | Maximum number of stores to return (default: 25, max: 100) |
| projectId | string | – | Filter knowledge stores by project ID |
| skip | number | – | Number of items to skip for pagination |
No output schema declared.
No examples provided.
list_llms ~152
Lists Cognigy.AI large language model configurations. LLMs are used for generative AI features like Knowledge AI, AI Agents, and node output generation. Shows provider, model type, and connection info.
| Name | Type | Req | Description |
|---|---|---|---|
| filter | string | – | Filter LLMs by name |
| limit | number | – | Maximum number of LLMs to return (default: 25, max: 100) |
| projectId | string | – | Filter LLMs by project ID. Omit for all accessible LLMs. |
| resourceLevel | string | – | Scope: 'organisation' for global LLMs, 'project' for project-specific |
| skip | number | – | Number of items to skip for pagination |
No output schema declared.
No examples provided.
list_nlu_connectors ~113
Lists Cognigy.AI NLU connectors. NLU connectors enable integration with external NLU services like Dialogflow, LUIS, Watson, or custom solutions for intent recognition.
| Name | Type | Req | Description |
|---|---|---|---|
| filter | string | – | Filter NLU connectors by name |
| limit | number | – | Maximum number of NLU connectors to return (default: 25, max: 100) |
| projectId | string | – | Filter NLU connectors by project ID |
| skip | number | – | Number of items to skip for pagination |
No output schema declared.
No examples provided.
list_packages ~83
Lists packages in a Cognigy.AI project. Packages are portable bundles of resources (flows, intents, etc.) that can be transferred between projects or environments.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | Maximum number of packages to return (1-100, default 25) |
| projectId | string | – | Project ID to list packages for. If omitted, lists all accessible packages. |
No output schema declared.
No examples provided.
list_playbook_runs ~78
Lists Cognigy.AI playbook run history showing pass/fail status, timestamps, and run metadata. Use this to review test results over time.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | Maximum number of runs to return (1-100, default 25) |
| playbookId | string | yes | The playbook ID to list runs for |
No output schema declared.
No examples provided.
list_playbooks ~74
Lists all playbooks in a Cognigy.AI project. Playbooks are automated test scenarios with steps and assertions for testing conversational flows.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | Maximum number of playbooks to return (1-100, default 25) |
| projectId | string | yes | The project ID to list playbooks from |
No output schema declared.
No examples provided.
list_projects ~59
Lists all Cognigy.AI projects accessible by your API key. Use this to discover available projects before working with flows, intents, or other resources.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | Maximum number of projects to return (1-100, default 25) |
No output schema declared.
No examples provided.
list_sentences ~86
Lists example sentences (training data) for a Cognigy.AI NLU intent. Use this to review training data quality before training.
| Name | Type | Req | Description |
|---|---|---|---|
| flowId | string | yes | The flow ID containing the intent |
| intentId | string | yes | The intent ID to list sentences for |
| limit | integer | – | Maximum number of sentences to return (1-100, default 25) |
No output schema declared.
No examples provided.
list_snapshots ~78
Lists all snapshots in a Cognigy.AI project. Snapshots are versioned backups of project configuration used for deployment and rollback. Use this to see available versions.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | Maximum number of snapshots to return (1-100, default 25) |
| projectId | string | yes | The project ID to list snapshots from |
No output schema declared.
No examples provided.
list_tasks ~74
Lists async tasks in Cognigy.AI. Tasks track long-running operations like snapshot creation, training, and imports. Use this to monitor background job status.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | Maximum number of tasks to return (1-100, default 25) |
| projectId | string | – | Optional project ID to filter tasks |
No output schema declared.
No examples provided.
What is the io.github.TsvetanG2/cognigy-ai-mcp-management-server server?
io.github.TsvetanG2/cognigy-ai-mcp-management-server is listed in the public MCP registry as io.github.TsvetanG2/cognigy-ai-mcp-management-server. MCP server for Cognigy.AI - 132 tools to build, configure & operate conversational AI agents. This page covers its npm package (cognigy-ai-mcp-management-server).
Is the io.github.TsvetanG2/cognigy-ai-mcp-management-server server safe to use?
io.github.TsvetanG2/cognigy-ai-mcp-management-server scores 80 out of 100 on VerifyMCP. We recorded 14 known advisories against it as of 2 October 2026. It declares no install or post-install scripts. 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 io.github.TsvetanG2/cognigy-ai-mcp-management-server server expose?
io.github.TsvetanG2/cognigy-ai-mcp-management-server exposes 138 tools: list_projects, list_flows, get_flow, get_flow_settings, get_latest_log_entries, and 133 more. Their descriptions and schemas cost roughly 14,859 tokens of context every time the server is loaded.
Is the io.github.TsvetanG2/cognigy-ai-mcp-management-server server still maintained?
io.github.TsvetanG2/cognigy-ai-mcp-management-server is still listed as active in the MCP registry. We last reached this channel on 2 October 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the io.github.TsvetanG2/cognigy-ai-mcp-management-server server under?
io.github.TsvetanG2/cognigy-ai-mcp-management-server declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.