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 states the tool 'Returns positive/negative/neutral breakdown,' which gives some output context, but lacks details on permissions, rate limits, data sources (beyond 'timeline data'), error handling, or whether it's read-only/destructive. For a tool with no annotations, 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 concise and front-loaded: the first sentence states the core purpose, followed by input options and return value. Every sentence earns its place, with no redundant information. However, it could be slightly more structured (e.g., separating input and output details more clearly).

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 3 parameters with full schema coverage but no annotations and no output schema, the description is moderately complete. It covers the basic purpose, input methods, and return type, but lacks behavioral context (e.g., how sentiment is calculated, data scope limitations). For a tool with no output schema, more detail on the return structure would be beneficial.

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 all three parameters (query, platforms, texts) with clear descriptions. The description adds marginal value by explaining the relationship between 'query' and 'texts' as alternative inputs, but doesn't provide additional semantics beyond what's in the schema (e.g., format details or constraints). Baseline 3 is appropriate when schema does the heavy lifting.

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: 'Run sentiment analysis on posts' with the specific action 'analyze' and resource 'posts'. It distinguishes from sibling tools like 'analyze_profile' or 'timeline_stats' by focusing on sentiment rather than profile analysis or statistical trends. However, it doesn't explicitly differentiate from all siblings (e.g., 'compare' might also involve analysis).

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 implied usage guidance by mentioning two input methods: 'Provide a query to search timeline data, or pass texts directly.' This suggests when to use each approach. However, it doesn't explicitly state when to choose this tool over alternatives like 'timeline_query' or 'timeline_search', nor does it mention prerequisites or exclusions (e.g., whether it works with all sibling scrapers).

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