x-ai-mcp

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions using 'Grok' (implying an external service or AI model) and that it 'Provides key points and analysis,' but lacks details on rate limits, authentication needs, error handling, or what 'find' entails (e.g., does it crawl replies?). For a tool with no annotations, this is insufficient to fully understand its behavior.

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, and the second provides essential parameter details in a clear 'Args:' section. Every sentence earns its place with no redundant or vague language, making it efficient for an agent 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 moderate complexity (summarization with an external service), no annotations, and an output schema (which covers return values), the description is partially complete. It explains the purpose and parameter semantics well but lacks behavioral context like error cases or limitations. With an output schema, it doesn't need to detail return values, but other gaps keep it at a baseline adequacy.

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 description adds meaningful semantics beyond the input schema. The schema has 1 parameter (tweet_id) with 0% description coverage, only providing a title. The description explains that tweet_id is 'ID of the first tweet in the thread (or any tweet in it),' clarifying its role and flexibility. This compensates well for the low schema coverage, though it doesn't detail format constraints (e.g., numeric vs. string).

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: 'Use Grok to find and summarize a tweet thread. Provides key points and analysis.' It specifies the verb ('find and summarize'), resource ('tweet thread'), and method ('Use Grok'), but doesn't explicitly differentiate from sibling tools like x_get_tweet or x_search_tweets, which might also retrieve tweet content. The mention of 'key points and analysis' adds specificity beyond basic retrieval.

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 provides minimal guidance on when to use this tool. It states the tool summarizes a tweet thread given a tweet ID, but doesn't specify when to choose it over alternatives like x_get_tweet (for single tweets) or x_search_tweets (for broader searches). No exclusions or prerequisites are mentioned, leaving the agent to infer usage from the purpose alone.

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