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meskhetian

linkedin-applicants-mcp

by meskhetian

List / search applicants

applicants_list
Read-onlyIdempotent

List applicants from a local database with filters for job, rating, resume, and profile, plus full-text search over name, headline, and text. Paginate and sort results for quick review.

Instructions

Page through applicants stored locally, with filters and full-text search over name, headline, resume text and profile text (prefix matching, e.g. "kubern django"). Rows omit resume/profile bodies; use applicant_get for one applicant. Reads the local database only (no LinkedIn traffic).

Input Schema

NameRequiredDescriptionDefault
jobIdNo
limitNo
orderNodesc
offsetNo
ratingNo
searchNoFull-text query over name, headline, resume text, profile text
orderByNoappliedAt
hasDetailNoWhether the application page was fetched (contact info, screening answers)
hasResumeNo
hasProfileNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, so the description's added value is the local-only read (no LinkedIn traffic) and the omission of resume/profile bodies. This enriches the agent's understanding beyond annotations without contradicting them.

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, no fluff, with the core purpose front-loaded and the caveat about applicant_get placed right after. Every clause earns its place, making it efficient and easy to parse.

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 read-only list/search tool with 10 parameters and no output schema, the description covers the essential context: local data, paging, filters, search fields, and the alternative for full details. It doesn't enumerate every filter, but the schema provides enums and some descriptions. The description is sufficient for an agent to decide when to use it and how to structure a basic query.

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 only 20%, so the description must compensate. It explains the search parameter in detail (prefix matching, example) and mentions filters generally, but does not clarify the semantics of other parameters like rating, hasDetail, or orderBy. It partially compensates but leaves many parameters to be inferred from 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 states a specific verb (page through) and resource (applicants) with clear scope: filters and full-text search over defined fields. It also distinguishes itself from the sibling applicant_get by noting rows omit resume/profile bodies. This makes the tool's purpose unmistakable.

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

Usage Guidelines5/5

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

It explicitly directs the agent to applicant_get for a single applicant's full details and clarifies that it reads only the local database (no LinkedIn traffic), implying sync/fetch tools are for live data. This gives clear when-to-use and when-not-to-use guidance.

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