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find_linkedin_candidates

Recruiter sourcing: find LinkedIn profiles matching a role, skills, location, experience band, and optional target companies. Returns name, headline, current title and company, profile URL, matched skills, and a match-confidence score. No login needed.

Input Schema

NameRequiredDescriptionDefault
skillsNoRequired skills (e.g. ["Python", "PyTorch"])
locationNoLocation filter (e.g. "Bangalore", "Remote")
maxYearsNoMaximum years of experience
minYearsNoMinimum years of experience
roleTitleYesRole to source for (e.g. "Senior Machine Learning Engineer")
maxResultsNoMax candidates (default 25)
targetCompaniesNoOnly return candidates currently at these companies

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full burden and does disclose 'No login needed' (auth context) and enumerates the returned fields. However, it omits rate limits, result freshness/caching, scraping behavior, and whether results are de-duplicated or how the match-confidence score is derived.

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?

Three tightly packed sentences, front-loaded with the use case and followed by criteria then return values. Little waste, though the return-field enumeration is slightly verbose.

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?

No output schema exists, but the description lists the return fields, and parameters are fully documented in the schema. Auth behavior is noted; the only real gaps are edge-case behavior and match-score semantics, which are minor for a filtered-search tool.

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 the schema already documents all seven parameters with examples and defaults. The description restates the same criteria without adding syntax or format detail beyond what the schema provides, 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 (find) plus resource (LinkedIn profiles) and frames the use case as recruiter sourcing, enumerating the matching criteria (role, skills, location, experience band, target companies). It does not, however, explicitly distinguish itself from close siblings like get_linkedin_profiles or search_linkedin_employees, leaving the agent to infer the difference.

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?

The opening 'Recruiter sourcing' label implies the scenario but gives no explicit when-to-use or when-not guidance, nor does it point to an alternative sibling. Usage is only loosely implied by the framing and criteria.

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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