nl_ask_postRoute a natural-language social-data question to the right lookup when you do not yet know the typed tool — prefer typed tools once the operation is known. Accepts a natural-language query.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Natural-language question to route to a public API lookup. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds marginal context beyond these — that it routes to a 'public API lookup' — which clarifies the dispatch nature and open-world return. No contradiction with annotations; the added behavioral value is modest.
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?
Two sentences with the core routing purpose and the 'prefer typed tools' guidance front-loaded. Minimal waste, though the phrase 'Accepts a natural-language query' is slightly redundant given the parameter description.
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?
For a router dispatching across ~200 social-data tools, the description gives the essential decision rule but leaves gaps: behavior when routing fails or is ambiguous, and the boundary against web_ask_run. The output is open-world and unspecified, so an agent lacks full expectations, but the single fully-described parameter mitigates this.
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%, and the single 'query' parameter is already described in the schema as 'Natural-language question to route to a public API lookup.' The description echoes this nearly verbatim without adding syntax, format, or example details. Baseline 3 applies since the schema carries the load.
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?
States a specific verb ('Route'), resource (natural-language social-data question → lookup), and scope ('social-data'). It clearly positions itself against the hundreds of typed sibling tools. However, it does not disambiguate from web_ask_run, the closest NL-routing sibling, so it earns a 4 rather than a 5.
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?
Provides an explicit routing rule: 'prefer typed tools once the operation is known', telling the agent when NOT to use this tool. This is actionable guidance against a large typed-tool surface. It omits mention of web_ask_run as an alternative for non-social questions, leaving a gap, so not a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool is clearly scoped to a specific platform and action (e.g., facebook_post_get vs instagram_post_get). Descriptions explicitly differentiate similar tools across platforms, and within-a-platform tools like tiktok_search_videos_list vs tiktok_search_hashtag_list have clear disambiguation notes.
All 167 tools follow a strict `platform_resource_action` pattern (e.g., youtube_video_comments_list). No mixing of styles—snake_case throughout, with consistent verb ordering (get, list, search, etc.).
The server has 167 tools, which is far beyond the typical well-scoped range of 3-15. While the broad multi-platform scope justifies many tools, this extreme number makes the tool surface overwhelming and difficult for an agent to navigate efficiently.
The tool set covers a wide range of platforms and operations including profile retrieval, post/video fetching, comments, search, transcripts, and ad library access. Minor gaps exist (e.g., no Facebook events or LinkedIn messaging), but the surface is comprehensive for a read-only data aggregation use case.