Skip to main content
Glama

Captapi

by io.github.CDCStream
OverviewDiscussionsAdmin

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

NameRequiredDescriptionDefault
qYesSearch query or keywords (min 2 characters).
sortNorelevance (default, search-engine rank — dates can span years) or date (found posts newest first).
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. Changed1 schema field changed
    • changedInput schema / properties / sort / description
      Previous value: -"relevance (default, search-engine rank — dates can span years) or date (recency)."New value: +"relevance (default, search-engine rank — dates can span years) or date (found posts newest first)."
  2. 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)."
  3. 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)."
  4. 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"
+}
  5. 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."
  6. 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"
+}
  7. Added

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations, the description carries the full burden, and it does well: it discloses the ~16 credit cost (0.8/result), that empty results and failures are never charged, the 50-item ceiling, and the cache=true free-24h behavior versus the always-fresh default. It omits auth requirements and rate limits, but the billing and caching disclosures are meaningful behavioral context.

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

Conciseness4/5

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

One tightly packed sentence front-loads the purpose, then stacks pagination, cost, and cache details with em-dashes. Every clause earns its place, though the density is slightly high for a single sentence.

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

Completeness4/5

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

For a search tool with full schema coverage and no output schema, the description covers cost, pagination ceiling/termination, and caching. It does not describe the shape of returned posts, but the essentials for calling the tool correctly are present.

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

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so q, sort, cache, limit, and cursor are all already documented in the schema with min/max and semantics. The description largely restates these (offset cursor, ceiling 50, cache=true for free hit), so it adds little beyond the baseline 3.

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

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb+resource ('Keyword search of public LinkedIn posts') with scope ('public') and a ceiling. It distinguishes itself reasonably from linkedin_post_details and linkedin_company_posts by being a keyword search over public posts, though it does not name those siblings explicitly.

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

Usage Guidelines3/5

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

Usage is only implied: an agent can infer this is the tool for keyword-based post discovery. The description offers cost and cache guidance but never states when to prefer this over sibling tools like linkedin_company_posts or linkedin_post_details, nor any when-not conditions.

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

All Captapi toolsInspect
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources