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Strands MCP Server

Strands MCP Server

by cagataycali
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

invoke_agent

Process complex queries requiring reasoning across multiple tools or conversational responses by invoking the full agent with natural language prompts.

Instructions

Invoke the full strands-mcp-cli agent with a natural language prompt. Use this for complex queries that require reasoning across multiple tools or when you need a conversational response from the agent.

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": {
      "prompt": "<prompt>"
    },
    "name": "invoke_agent"
  }
}

Input Schema

NameRequiredDescriptionDefault
promptYesThe prompt or query to send to the agent

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It mentions the tool invokes an agent for complex reasoning and conversational responses, but lacks details on behavioral traits such as execution time, error handling, authentication needs, or rate limits. This is a significant gap for a tool that likely involves significant processing.

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 appropriately sized and front-loaded, with two clear sentences that efficiently convey purpose and usage without any wasted words. Every sentence earns its place by providing essential information.

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?

Given the tool's complexity (invoking an agent for reasoning) and lack of annotations or output schema, the description is moderately complete. It covers the high-level purpose and usage but lacks details on behavior, response format, or error conditions, leaving gaps for an agent to understand full implications.

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 has 100% description coverage, with the 'prompt' parameter well-documented. The description adds minimal value beyond the schema, only implying that the prompt should be 'natural language' for complex queries, which is somewhat redundant. Baseline 3 is appropriate as the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Invoke the full strands-mcp-cli agent with a natural language prompt.' It specifies the action (invoke) and resource (agent), though it doesn't explicitly distinguish it from sibling tools like 'greet' or 'mcp_client' beyond mentioning 'complex queries' and 'conversational response.'

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 provides clear context for when to use this tool: 'for complex queries that require reasoning across multiple tools or when you need a conversational response from the agent.' It gives specific scenarios but does not explicitly state when not to use it or name alternatives among siblings.

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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