social_research

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

With no annotations provided, the description carries the full behavioral burden and does so exceptionally. It discloses read-only nature, file persistence (every response written to a file with path, output_dir, count, cursor), large-response handling (saved.inline false omits data), projection/truncation behavior (advertisers carries page-level facts once, shaping says what was dropped), error structure ('a structured error naming the missing or unknown params'), and the no-spend dry_run behavior. This is rich, accurate behavioral disclosure beyond any structured field.

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 long but dense, and every sentence earns its place for a tool of this complexity. It is well front-loaded: purpose first, then the two-step workflow, then response/error behavior, file persistence, and finally use cases and dry_run. It is slightly on the verbose side — the precise character counts and the detailed saved-field accounting could arguably be trimmed — but the structure is logical and the ordering is correct, making it a 4 rather than a 5.

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?

There is no output schema, so the description must carry the return-value explanation — and it does, thoroughly: data (JSON), advertisers (page-level facts carried once), shaping (what was dropped), saved (path, output_dir, count, cursor), and saved.inline semantics. Combined with nested-object parameters, zero annotations, and a companion discovery tool, all the gaps an agent would face are covered. Nothing essential for correct invocation is missing.

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%, setting a baseline of 3. The description adds meaningful value beyond the schema: concrete example values for platform ('meta_ad_library', 'instagram', 'tiktok', 'reddit', 'x', 'linkedin') and endpoint ('company_ads', 'profile', 'posts', 'search'), the size warning for fields="*" (~185,000 characters, a third signed CDN query strings), and the workflow context that params objects are built from list_research_sources output. It doesn't reach 5 only because per-endpoint parameter specifics are intentionally deferred to the companion tool.

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 the concrete scope (competitor ads, profiles, posts, comments, transcripts, hashtag/keyword search, subreddit/trend discovery). It clearly distinguishes itself from siblings by contrasting against the generation tools (image_generate, video_generate, audio_generate, transcribe) and explicitly routing the discovery task to list_research_sources. An agent can tell exactly what this tool does and what it does not do.

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 usage guidance is explicit and actionable: 'call list_research_sources FIRST to see the platforms, their endpoints, and each endpoint's params; then call this with platform, endpoint, and a params object'. It further states when to use it ('Use for competitor and market research, audience listening, and trend discovery') and what it is not for ('not posting and not private data'), plus the dry_run preview option. The companion tool is named and sequenced, leaving nothing to inference.

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