cognigy-ai-mcp-management-server

update_knowledge_source

Idempotent

Update an existing Cognigy.AI knowledge source's name or description. Validate changes with dryRun, then set dryRun=false to apply.

Instructions

Updates an existing Cognigy.AI knowledge source. Use this to change name or description. MUTATING: Set dryRun=false to update.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "sourceId": "<sourceId>",
      "knowledgeStoreId": "<knowledgeStoreId>"
    },
    "name": "update_knowledge_source"
  }
}

Input Schema

Name	Required	Description
`name`	No	New name for the source
`dryRun`	No	If true (default), validates without updating. Set to false to actually update.
`sourceId`	Yes	The knowledge source ID to update
`description`	No	New description
`knowledgeStoreId`	Yes	The knowledge store ID containing the source

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.4Jul 27, 2026

TDQS

A4.4/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate mutation (readOnlyHint: false) and idempotency (idempotentHint: true). The description adds crucial behavior: the dryRun parameter must be set to false for actual updates. This goes beyond annotations to clarify the update workflow.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loading the purpose and following with a usage hint. No extraneous words, and every sentence is necessary.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a mutation tool with no output schema, the description adequately covers purpose and key behavioral trait (dryRun). It does not describe the return value or error conditions, but the schema and annotations provide sufficient context for an AI agent to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already documents all parameters. The description adds value by explaining the mutating behavior tied to the dryRun parameter, which is not fully covered by the schema's default value description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it updates an existing Cognigy.AI knowledge source, specifically for changing name or description. This distinguishes it from create, delete, and get operations in the sibling list.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly says to use this tool for changing name or description, which provides clear context. It does not mention when not to use it, but the scope is well-defined and needs no further exclusion given the sibling context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server