twitterapi-io-mcp

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes what the tool returns ('Full details including path, parameters, cURL example, and code snippets'), which is crucial for understanding output behavior. However, it lacks details on potential errors (e.g., if the endpoint name is invalid) or performance aspects like rate limits, leaving some behavioral context uncovered.

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 well-structured and front-loaded with the core purpose, followed by usage guidelines, return details, and examples. Every sentence earns its place by providing essential information without redundancy, making it efficient and easy to parse.

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 tool's low complexity (1 parameter), high schema coverage (100%), and the presence of an output schema (which handles return value documentation), the description is complete. It covers purpose, usage, returns, and examples, addressing all necessary contextual elements without overloading with redundant details from structured fields.

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?

The input schema has 100% description coverage, so the baseline is 3. The description adds value by providing common endpoint examples ('get_user_info', 'tweet_advanced_search', etc.), which helps users understand valid parameter values beyond the schema's generic examples. This enhances semantic clarity, though it doesn't fully explain parameter constraints or formats beyond what's implied.

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 with a specific verb ('Get complete documentation') and resource ('for a specific TwitterAPI.io endpoint'). It distinguishes from sibling tools like 'list_twitterapi_endpoints' (which lists endpoints) and 'search_twitterapi_docs' (which searches documentation) by focusing on retrieving detailed documentation for a known endpoint.

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 includes an explicit 'USE THIS WHEN' section that specifies when to use this tool ('You know the exact endpoint name') and implies alternatives by referencing sibling tools like 'search_twitterapi_docs' for cases where the endpoint name is not known. This provides clear guidance on tool selection.

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