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Search LinkedIn groups

linkedin_search_groups
Read-onlyIdempotent

Search LinkedIn groups. Read-only: returns public data and changes nothing. Costs 1 credit per result returned, with a minimum of 1 credit per request.

Input Schema

NameRequiredDescriptionDefault
countYesNumber of results to return (1–10). Defaults to 10.
startNoNumber of results to skip. Defaults to 0.
keywordYesKeyword(s) to search LinkedIn for, e.g. "openai", "software engineer", "fintech".

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so the 'read-only, changes nothing' sentence largely restates structured data. The genuinely additive part is the billing behavior — 1 credit per result returned with a 1-credit minimum per request — which is a quota trait unavailable in any structured field and directly relevant to choosing a small count.

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?

Three short sentences with zero filler, and the identity statement is front-loaded ahead of the safety and cost clauses. Nothing repeats and no sentence is wasted.

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

Completeness3/5

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

For a simple read-only search with fully documented parameters and rich annotations, the definition is callable and the cost model is a useful addition. It stops short on return shape (no output schema exists, yet the agent is told nothing about the fields of a group result) and on pagination expectations when start is combined with the count cap of 10.

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 description coverage is 100%; keyword, count (1–10, default 10) and start are all documented with examples and bounds in the schema itself. The description adds no syntax, format or interaction detail (e.g., how start interacts with count for pagination), so baseline 3 applies.

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?

States a specific verb and resource ('Search LinkedIn groups'), which cleanly separates it from the many sibling search tools (linkedin_search_companies, linkedin_search_jobs, linkedin_search_posts, etc.) by resource noun. It does not, however, say anything about what a 'group' result contains or how this differs from a broader linkedin_search_all call.

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

Usage Guidelines2/5

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

There is no when-to-use guidance: nothing tells the agent when to pick groups over companies/schools/posts, nor when linkedin_search_all would be preferable. The only conditional information is the credit cost, which is pricing rather than selection guidance.

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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