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TsvetanG2

cognigy-ai-mcp-management-server

create_knowledge_connector

Automate content ingestion from external sources by creating a knowledge connector for SharePoint, Confluence, or custom sources into Cognigy.AI.

Instructions

Creates a new Cognigy.AI knowledge connector for automated content ingestion from external sources like SharePoint or Confluence. MUTATING: Set dryRun=false to create.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesName for the connector
typeYesConnector type (e.g., 'sharepoint', 'confluence', 'custom')
dryRunNoIf true (default), validates without creating. Set to false to actually create.
scheduleNoCron expression for scheduled runs
settingsNoType-specific connector settings
connectionIdNoConnection ID for authentication
knowledgeStoreIdYesThe knowledge store ID to create the connector in
Behavior4/5

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

Annotations already indicate mutation (readOnlyHint=false) and open world. Description adds the dryRun pattern and explicitly labels the tool as MUTATING, providing useful behavioral context beyond 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?

Two concise sentences with zero wasted words. Critical information is front-loaded: purpose and mutating nature.

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 and high schema coverage (100%), the description suffices by stating purpose and key behavior (dryRun). Missing discussion of side effects (e.g., scheduling) but schema covers parameters adequately.

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 has 100% description coverage for all 7 parameters. Description only reiterates the dryRun parameter, adding no new semantic meaning beyond what schema already provides. Baseline 3 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?

Description clearly states verb 'creates' and resource 'knowledge connector', specifies domain and example sources (SharePoint, Confluence), and distinguishes from sibling update/delete/run tools.

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?

Provides explicit guidance on using dryRun=false to actually create, indicating validation vs execution. However, does not mention preconditions (e.g., valid knowledgeStoreId) or when to use alternatives, though the name self-documents the create action.

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

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