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by io.github.Shree-git
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Invoke AI Agent

invoke_agent

Invoke a specific AI agent with given inputs.

The agent will execute within policy constraints and return structured output. All agent runs are logged for audit and traceability.

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": {
      "agentId": "<agentId>",
      "input": "<input>"
    },
    "name": "invoke_agent"
  }
}

Input Schema

NameRequiredDescriptionDefault
inputYesAgent-specific input parameters
agentIdYesAgent to invoke
contextNoAdditional context (brand voice ID, date range, etc.)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations indicate the tool is not read-only, not destructive, and open-world, but the description adds valuable context: the agent executes under policy constraints and returns structured output. It also discloses that all runs are logged for audit and traceability. This goes beyond annotations without contradicting them, though it stops short of describing possible side effects.

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 three sentences with front-loaded purpose and no redundant words. Each sentence contributes: invocation, behavior, and logging. It is concise and appropriately structured for an agent-invocation tool.

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?

Given the tool's complexity (12 agents, nested input objects, no output schema), the description covers essential context: policy constraints, structured output, and audit logging. It does not detail how to select an agent or what output to expect, but the schema enumerates agent IDs, and 'structured output' is a reasonable generic promise. The absence of agent-specific behavioral details is a minor gap, not a critical one.

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?

The input schema provides 100% coverage with descriptions for every parameter: agentId, input, and context. The description offers no additional parameter-specific meaning, so it stays at the baseline of 3. The phrase 'given inputs' adds nothing beyond the schema's 'Agent-specific input parameters.'

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 uses a specific verb and resource: 'Invoke a specific AI agent with given inputs.' It clearly distinguishes this from sibling tools like list_agents and get_agent_run, which are about listing or retrieving rather than executing. The mention of execution within policy constraints and structured output further clarifies the action.

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?

The description implies the tool is for invoking an agent but provides no explicit when-to-use guidance or alternatives. It does not mention that list_agents could be used to discover available agent IDs or get_agent_run to fetch results, so usage context is only implicit from the tool name and schema.

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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