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cognigy-ai-mcp-management-server

cognigy-ai-mcp-management-server

by TsvetanG2
OverviewSchemaSourceRelated ServersDiscussions(1)Admin
TypeScript

stop_function_instance

Stop a running Cognigy.AI Function instance to cancel long-running or stuck functions. Set dryRun to false to execute the stop.

Instructions

Stops a running Cognigy.AI Function instance. Use this to cancel a long-running or stuck function. MUTATING: Set dryRun=false to stop.

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": {
      "functionId": "<functionId>",
      "functionInstanceId": "<functionInstanceId>"
    },
    "name": "stop_function_instance"
  }
}

Input Schema

NameRequiredDescriptionDefault
dryRunNoIf true (default), validates without stopping. Set to false to actually stop.
functionIdYesThe function ID
functionInstanceIdYesThe function instance ID to stop

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.4

TDQS

A4.4/5.0
Behavior4/5

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

Description adds the dryRun behavior and states 'MUTATING', which goes beyond the annotations (readOnlyHint=false, destructiveHint=false) by clarifying the safe validation mode. It provides context that stopping is not destructive, consistent 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?

Two short sentences front-load the action and use case. Every word serves a purpose: 'Stops a running...', 'Use this to...', 'MUTATING: Set dryRun=false to stop.' No extraneous information.

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 simple mutation tool with no output schema, the description explains the key behavior and the dryRun parameter. It could briefly mention the return value (e.g., success message), but overall it's sufficiently complete for an agent to invoke 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% (baseline 3). The description adds value by explaining the dryRun parameter's role: 'Set dryRun=false to actually stop', which is not fully captured in the schema's description. It clarifies how to use the tool correctly.

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 verb 'stops' and the resource 'running Cognigy.AI Function instance', with a specific use case 'cancel a long-running or stuck function'. This distinguishes it from sibling tools like trigger_function (starts) or get_function_instance (retrieves).

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 says 'Use this to cancel a long-running or stuck function', providing clear when-to-use guidance. It also hints at the dryRun parameter for validation. However, it doesn't mention when not to use it or compare with alternatives, missing an explicit exclusion.

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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