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cognigy-ai-mcp-management-server

cognigy-ai-mcp-management-server

by TsvetanG2
OverviewSchemaSourceRelated ServersDiscussions(1)Admin
TypeScript

trigger_function

Run a Cognigy.AI function instantly by creating a new function instance. Validate with dryRun before triggering.

Instructions

Triggers a Cognigy.AI Function to run immediately. Creates a new function instance that executes the function code. MUTATING: Set dryRun=false to trigger.

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>"
    },
    "name": "trigger_function"
  }
}

Input Schema

NameRequiredDescriptionDefault
inputNoInput parameters for the function
dryRunNoIf true (default), validates without triggering. Set to false to actually trigger.
functionIdYesThe function ID to trigger

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.4

TDQS

A4/5.0
Behavior4/5

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

Description discloses that the tool is mutating and that dryRun=false actually triggers execution, creating a function instance. Annotations already indicate non-readonly; description adds 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?

Three concise sentences, front-loaded with main action. No redundant phrasing. Efficient for agent parsing.

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?

Adequate for tool with no output schema; explains mutation and dryRun. Could mention return type (e.g., function instance ID) but no output schema exists.

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%; description adds minimal value beyond schema, only noting the dryRun parameter's role in triggering. Does not elaborate on the input object's structure.

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?

Clearly states the tool triggers a Cognigy.AI Function to run immediately by creating a new function instance. Distinct from sibling tools like get_function, create_function, etc., which do not execute functions.

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

Usage Guidelines3/5

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

Implies usage for triggering functions but does not explicitly state when to use versus alternatives. No guidance on prerequisites or when not to use.

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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