social-research-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 the types of comparisons and metrics but lacks critical details such as time range defaults, data freshness, rate limits, authentication requirements, or output format expectations. This is inadequate for a tool with 2 parameters and no output schema.

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, well-structured sentence that efficiently conveys the tool's core functionality without unnecessary words. It is appropriately sized and front-loaded with essential information.

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 comparing multiple items across platforms and metrics, with no annotations and no output schema, the description is insufficient. It lacks details on behavioral aspects (e.g., time ranges, data sources), output structure, and error handling, leaving significant gaps for an AI agent to use the tool effectively.

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 100%, so the schema already documents both parameters thoroughly. The description adds minimal value by mentioning '2+ profiles, hashtags, or topics' and 'engagement, sentiment, and volume metrics', which aligns with but doesn't significantly expand upon the schema's details for 'items' and 'metric'.

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 specific verbs ('compare side-by-side') and resources ('profiles, hashtags, or topics'), and explicitly distinguishes it from siblings by focusing on comparative analysis rather than individual analysis (like analyze_profile) or scraping operations.

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 context by specifying what can be compared (profiles, hashtags, topics) and metrics (engagement, sentiment, volume), but does not explicitly state when to use this tool versus alternatives like analyze_sentiment or timeline_stats, nor does it provide exclusion criteria.

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