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 (reading from file, sending to models, returning responses) but lacks critical details: it doesn't specify authentication needs, rate limits, error handling, or whether the operation is read-only or has side effects. For a tool that interacts with external LLM models, this is a significant gap in behavioral transparency.

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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by structured sections for Args and Returns. Every sentence adds value, with no redundant information. It could be slightly more concise by integrating the default value into the main description, but overall it's efficient.

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 the complexity (interacting with multiple LLM models), no annotations, no output schema, and 0% schema coverage, the description is moderately complete. It covers the basic operation and parameters but lacks details on authentication, error handling, response format, or model-specific behaviors. This is adequate for a simple tool but has clear gaps for reliable agent use.

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?

Schema description coverage is 0%, so the description must compensate. It effectively adds meaning for both parameters: 'file_path' is explained as 'Path to the file containing the prompt text', and 'models_prefixed_by_provider' is detailed with format examples and a default value. This covers the semantics well, though it doesn't specify file format constraints or model availability.

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 tool's purpose: 'Read a prompt from a file and send it to multiple LLM models.' This specifies the verb (read and send), resource (prompt from file), and target (multiple LLM models). It distinguishes from siblings like 'prompt_tool' (likely sends a direct prompt) and 'prompt_from_file2file_tool' (likely outputs to file). However, it doesn't explicitly differentiate from all siblings, such as persona tools.

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 when you have a prompt in a file and want to test it across models, but it doesn't explicitly state when to use this tool versus alternatives like 'prompt_tool' (for direct prompts) or 'prompt_from_file2file_tool' (for file output). It mentions a default model, which provides some context, but lacks explicit guidance on prerequisites or exclusions.

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