x-ai-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 mentions 'real-time X data' and 'insights with citations', which adds some context, but it doesn't cover critical aspects like rate limits, authentication needs, data freshness, or potential costs. For a tool that likely involves API calls and AI processing, this is a significant gap in 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 well-structured and concise, with two sentences that efficiently convey the tool's purpose and parameter usage. The first sentence explains the core functionality, and the second provides parameter details with examples, with no wasted words. It's appropriately sized for a single-parameter tool.

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 complexity (involving AI analysis and real-time data), no annotations, and an output schema present, the description is moderately complete. It covers the purpose and parameter semantics adequately, but lacks details on behavioral traits like performance or limitations. The output schema likely handles return values, so the description doesn't need to explain those, but overall it could be more comprehensive.

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, which has 0% coverage. It explains that the 'topic' parameter is for analysis (e.g., 'AI agents', 'Claude Code', 'Bitcoin'), providing concrete examples that clarify usage. However, it doesn't detail constraints like topic length or format, so it's not a perfect 5.

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 AI to analyze what X is saying about a topic right now.' It specifies the verb (analyze), resource (X posts about a topic), and method (Grok AI). However, it doesn't explicitly differentiate from sibling tools like 'x_search_tweets' or 'x_summarize_thread', which might offer similar functionality, so it doesn't reach a perfect 5.

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 stating 'right now' and 'real-time X data', suggesting it's for current analysis, but it doesn't provide explicit guidance on when to use this tool versus alternatives like 'x_search_tweets' or 'x_summarize_thread'. There's no mention of prerequisites, exclusions, or specific contexts, leaving usage somewhat ambiguous.

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