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by io.github.CDCStream
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linkedin_search_posts

Keyword search of public LinkedIn posts — offset cursor, ceiling 50. Costs ~16 credits (0.8/result). Empty results and failures are never charged. Pass cache=true for a free 24h cache hit (default always fresh).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesSearch query or keywords (min 2 characters).
sortNorelevance (default, search-engine rank — dates can span years) or date (recency).
cacheNoSet true to serve from the 24h response cache (0 credits on hit). Default false — always fetch fresh.
limitNoMax items to return (default 20, max 50). Billed per result.
cursorNoPagination cursor. Leave empty for the first page; then pass the nextCursor value returned in the previous response (numeric offset, e.g. 20). A null nextCursor means the end of the list (max 50 posts).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / cache / description
      Previous value: -"Set true to serve from the 24h response cache (0 credits on hit). Default false — always fetch fresh. Envelope includes cached + cachedAt on hits."New value: +"Set true to serve from the 24h response cache (0 credits on hit). Default false — always fetch fresh."
    • changedInput schema / properties / sort / description
      Previous value: -"relevance or date. Default relevance."New value: +"relevance (default, search-engine rank — dates can span years) or date (recency)."
  2. Changed3 schema fields changed
    • changedInput schema / properties / cache / description
      Previous value: -"Set true to serve from the 24h response cache. Default false — always fetch fresh data."New value: +"Set true to serve from the 24h response cache (0 credits on hit). Default false — always fetch fresh. Envelope includes cached + cachedAt on hits."
    • changedInput schema / properties / limit / description
      Previous value: -"Max items to return. Default 20, max 50. Billed per result."New value: +"Max items to return (default 20, max 50). Billed per result."
    • changedInput schema / properties / q / description
      Previous value: -"Search query or keywords (min 2 chars)."New value: +"Search query or keywords (min 2 characters)."
  3. Changed1 schema field changed
    • addedInput schema / properties / cursor
      Added value: +{
+  "description": "Pagination cursor. Leave empty for the first page; then pass the nextCursor value returned in the previous response (numeric offset, e.g. 20). A null nextCursor means the end of the list (max 50 posts).",
+  "type": "string"
+}
  4. Changed1 schema field changed
    • changedInput schema / properties / cache / description
      Previous value: -"Responses are cached for 24 hours by default. Set false to bypass the cache and always fetch fresh data (default true)."New value: +"Set true to serve from the 24h response cache. Default false — always fetch fresh data."
  5. Changed1 schema field changed
    • addedInput schema / properties / cache
      Added value: +{
+  "description": "Responses are cached for 24 hours by default. Set false to bypass the cache and always fetch fresh data (default true).",
+  "type": "boolean"
+}
  6. Added

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description carries the full behavioral burden and delivers: credit cost (~16 credits, 0.8/result), no charge on empty results/failures, cache behavior with a 24h hit, default fresh fetching, offset cursor, and a ceiling of 50. This goes well beyond the schema and gives an agent a realistic model of cost and side-effect behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with high information density. The main purpose is front-loaded, followed by cost, failure handling, and cache guidance. No wasted words or redundant restatement of the schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a search tool with no output schema and no annotations, the description covers the key operational details: scope, cost, pagination mechanism, result ceiling, cache behavior, and failure-charge policy. An agent has enough information to select the tool, set parameters sensibly, and interpret likely outcomes.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

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 meaningful semantics beyond the schema: per-result billing for limit, the cache-default behavior, and the credit cost. It also reinforces cursor pagination. It does not add much about q or sort, but those are already well-described in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

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: 'Keyword search of public LinkedIn posts'. This clearly distinguishes the tool from sibling tools like linkedin_company_posts (company feed retrieval) and linkedin_post_details (single post). The scope ('public', 'keyword search') tells an agent exactly what this tool does and roughly when it applies.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context: use this for keyword-based searching of public LinkedIn posts. It does not explicitly name alternatives or exclusion criteria, but the phrase 'keyword search of public LinkedIn posts' strongly implies when this tool is relevant relative to siblings that fetch company posts or post details. The lack of explicit 'when not to use' guidance prevents a 5.

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

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