cognigy-ai-mcp-management-server

create_knowledge_source

Creates a knowledge source for RAG content ingestion. Accepts URLs or manual text, then automatically chunks and embeds the content.

Instructions

Creates a new Cognigy.AI knowledge source for RAG content ingestion. Sources can be URLs, uploaded files, or manual text. Content is automatically chunked and embedded. MUTATING: Set dryRun=false to create.

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": {
      "knowledgeStoreId": "<knowledgeStoreId>",
      "name": "<name>"
    },
    "name": "create_knowledge_source"
  }
}

Input Schema

Name	Required	Description	Default
`url`	No	URL to ingest (required if type is 'url')
`name`	Yes	Name for the knowledge source
`text`	No	Text content to ingest (for type 'manual')
`type`	No	Source type: 'manual' for text input, 'url' for web page	manual
`dryRun`	No	If true (default), validates without creating. Set to false to actually create.
`metadata`	No	Custom metadata to attach to all chunks from this source
`description`	No	Description of the source content
`knowledgeStoreId`	Yes	The knowledge store ID to create the source in

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.4Jul 27, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already indicate mutation (readOnlyHint=false). The description adds valuable behavioral context: content is automatically chunked and embedded, and the dryRun behavior is noted. No contradictions with annotations.

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 concise (two sentences), front-loaded with the action, and contains no unnecessary words. Every sentence adds value.

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

Completeness3/5

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

The description explains the creation process and automatic chunking/embedding, but lacks information about return values (no output schema). DryRun behavior is mentioned, but validation result details are omitted. Adequate but not fully complete.

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

Parameters3/5

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

Schema coverage is 100% (all parameters have descriptions). The description adds overall context about source types and dryRun but does not provide per-parameter guidance beyond the schema. Baseline score is appropriate.

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 the tool creates a knowledge source for RAG content ingestion, specifies source types (URLs, uploaded files, manual text), and mentions automatic chunking and embedding. It uniquely describes the core function among siblings.

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?

The description explicitly highlights the dryRun parameter ('Set dryRun=false to create'), guiding the agent on when actual creation occurs. It does not explicitly mention alternatives or when not to use, but the context is clear enough.

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

Deploy Server