Agile Team MCP Server

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 discloses the basic behavior (sending prompts and returning responses) but lacks critical details such as rate limits, authentication needs, error handling, response formats beyond 'List of responses', or whether this is a read-only or mutating operation. For a tool with no annotation coverage, this is insufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, followed by structured Args and Returns sections. Each sentence earns its place by providing necessary information without redundancy, making it efficient and well-organized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations and no output schema, the description covers parameters well but lacks behavioral context (e.g., how responses are structured, error cases). It is complete enough for basic use but misses details needed for robust agent interaction, especially for a tool with multiple parameters and no structured output definition.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by explaining both parameters: 'text' as 'The prompt text to send to the models' and 'models_prefixed_by_provider' with format details and a default value. This adds essential meaning beyond the bare schema.

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

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

The description clearly states the specific action ('Send a text prompt to multiple LLM models') and resource ('return their responses'), distinguishing it from siblings like prompt_from_file_tool (which uses file input) and list_models_tool (which lists models rather than sending prompts). The verb 'send' and scope 'multiple LLM models' are precise.

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

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

The description implies usage by specifying default behavior ('If None, defaults to ["openai:gpt-4o-mini"]'), but it does not explicitly state when to use this tool versus alternatives like prompt_from_file_tool or persona tools. No exclusions or prerequisites are mentioned, leaving some ambiguity.

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