social_research

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden and discloses read-only semantics, return shape (`data` with varying structure), error behavior for missing/unknown params, and the dry_run preview behavior. It does not cover rate limits or auth, but for a broad multi-endpoint tool this is notably transparent.

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 dense but well-structured, front-loading the core purpose before workflow, output, and use cases. There is a minor redundancy by repeating "read-only public data," but overall each sentence contributes meaningful 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 high complexity (multiple platforms, dynamic endpoints, no output schema, no annotations), the description is remarkably complete: it explains the discovery prerequisite, how to build params, the return envelope, error semantics, and dry_run. It covers the essential context an agent needs to invoke this tool correctly without additional lookups.

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 coverage is 100%, so the baseline is 3; the description adds value by explaining that `params` is an object built from the endpoint's required/optional params as listed by list_research_sources. It also gives concrete examples like `{"handle": "nike"}`, which helps the agent construct valid dynamic parameters beyond the schema's generic description.

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 opens with a specific verb and resource: "Pull read-only structured public data from social platforms and ad libraries," then enumerates concrete data types like competitor ads, profiles, posts, comments, and transcripts. It clearly distinguishes this from the sibling tools by framing it as data retrieval rather than generation or media manipulation.

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 gives an explicit two-step workflow: call list_research_sources FIRST to discover platforms/endpoints/params, then call this tool with `platform`, `endpoint`, and a `params` object. It also states use cases (competitor/market research, audience listening, trend discovery) and explicitly excludes posting and private data.

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