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Search LinkedIn events

linkedin_search_events
Read-onlyIdempotent

Search LinkedIn events. 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

A3.6/5.0
Behavior4/5

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

Annotations already establish readOnly, idempotent, open-world, and non-destructive behavior, so the description's read-only sentence is largely redundant. What it genuinely adds is the cost model — 1 credit per result with a 1-credit minimum per request — which is operationally important information absent from 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.

Conciseness5/5

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

Three short sentences, purpose first, then safety, then cost — nothing extraneous and no wasted framing. The read-only sentence duplicates an annotation, but it costs only one short clause and does not bloat the definition.

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 simple three-parameter search with full schema coverage and no output schema, the description covers purpose, safety, and billing, which is most of what an agent needs. It omits any hint about what an event result contains or how pagination via start behaves, leaving a small gap.

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%, so keyword, count (1–10, default 10), and start are already fully documented; the baseline of 3 applies. The description adds no parameter-level meaning beyond noting that credits scale with results returned, which indirectly bears on choosing count.

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 names a specific verb and resource ("Search LinkedIn events"), which is enough to separate it from linkedin_search_posts, linkedin_search_jobs, and linkedin_search_all by resource type. It stops short of explicitly naming a sibling or contrasting scope, so it sits below a 5.

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, no exclusion of alternatives such as linkedin_search_all or linkedin_search_companies, and no stated prerequisite (e.g., that a keyword is required and results are capped at 10). An agent must infer the routing decision and the keyword-only interface entirely from the schema.

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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