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 timeline database, which adds some context about persistence. However, it doesn't address critical behavioral aspects like rate limits, authentication requirements, whether this is a read-only or write operation (though 'scrape' implies read), error handling, or platform-specific constraints.

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 appropriately sized with two sentences that are front-loaded with the core functionality. The first sentence clearly states the purpose, and the second adds important behavioral context about data persistence. There's no wasted verbiage, though it could be slightly more structured with explicit usage guidance.

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 (5 parameters, no output schema, no annotations), the description is incomplete. It doesn't explain what the tool returns, error conditions, authentication needs, or how it differs from sibling tools. While it mentions data persistence, it lacks sufficient context for an agent to understand the full behavioral implications of using this scraping tool.

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 thoroughly. The description adds minimal value beyond the schema by mentioning 'search by query, hashtag, or user timeline' which aligns with the query parameter description, but doesn't provide additional semantic context like format examples or usage patterns beyond what's in the structured fields.

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 ('scrape tweets') and resources ('from Twitter/X'), distinguishing it from siblings like scrape_facebook or timeline_query by specifying the target platform and action. It explicitly mentions what gets scraped (tweets) and the scope (search by query, hashtag, or user timeline).

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 like scrape_facebook, timeline_query, or timeline_search. It mentions that results are saved to a local database, but doesn't specify when this tool is preferred over direct querying tools or other scraping siblings, leaving usage context unclear.

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