social-research-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 'scrape recent posts' and 'compute' metrics, implying data retrieval and processing, but does not disclose important traits such as rate limits, authentication needs, data privacy implications, or whether the operation is read-only or has side effects. This leaves significant gaps for an AI agent to understand the tool's 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 a single, efficient sentence that front-loads the core purpose ('Analyze a social media profile') and lists specific computed metrics. There is no wasted language, and it is appropriately sized for the tool's complexity.

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 of analyzing social media profiles with multiple metrics, no annotations, and no output schema, the description is moderately complete. It outlines what the tool does but lacks details on behavioral aspects and output format. For a tool with 3 parameters and no structured output information, it should provide more context to be fully helpful.

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 schema description coverage is 100%, meaning all parameters are well-documented in the schema itself. The description does not add any additional meaning beyond what the schema provides (e.g., it doesn't explain the implications of 'max_posts' on performance or accuracy). With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

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: 'Analyze a social media profile' with specific actions like 'scrape recent posts and compute engagement rate, posting frequency, content themes, peak hours, and sentiment.' It uses a specific verb ('analyze') and resource ('social media profile'), but it does not explicitly distinguish itself from sibling tools like 'analyze_sentiment' or 'timeline_stats,' which might have overlapping functionality.

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 no guidance on when to use this tool versus alternatives. With sibling tools like 'analyze_sentiment' (which might focus only on sentiment) and various 'scrape_' tools (which might only collect data without analysis), there is no mention of when this comprehensive analysis tool is preferred or when other tools might be more appropriate.

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