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 that results are saved to a local database, which implies persistence and potential data storage implications, but doesn't cover critical aspects like rate limits, authentication needs, error handling, or whether the operation is idempotent. For a scraping tool with no annotation coverage, this leaves significant gaps in understanding 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 concise and front-loaded with the core purpose in the first sentence. The second sentence adds necessary context about result persistence. Both sentences earn their place, though it could be slightly more structured (e.g., separating usage from outcomes).

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 a scraping tool with no annotations and no output schema, the description is incomplete. It lacks details on return values, error conditions, performance characteristics, and how it interacts with the timeline database. For a tool that performs data extraction and storage, more context is needed to use it 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 all parameters. The description adds minimal value by listing the search methods (profile URL, company URL, keyword), which aligns with the schema but doesn't provide additional syntax, format details, or usage examples beyond what's in the parameter descriptions. Baseline 3 is appropriate when the 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: 'Scrape posts from LinkedIn' with specific resources (posts) and methods (profile URL, company URL, or keyword). It distinguishes from siblings like 'scrape_facebook' by specifying LinkedIn, but doesn't explicitly differentiate from other LinkedIn-related tools like 'analyze_profile' beyond the scraping action.

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. It mentions that 'Results are saved to the local timeline database,' but doesn't explain when to choose this over other scraping tools (e.g., 'scrape_twitter') or analysis tools (e.g., 'analyze_profile'), nor does it specify prerequisites or exclusions for usage.

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