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LinkedIn MCP Zero

LinkedIn MCP Zero

by SanthaKumar-K-2004
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

smart_match_jobs

Read-onlyIdempotent

Match a saved resume to jobs using your own LLM for deep fit reasoning and clear explanations, helping you prioritize roles worth pursuing.

Instructions

AI-powered job matching using the client's LLM for deep reasoning and fit explanation.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "resume_id": "<resume_id>"
    },
    "name": "smart_match_jobs"
  }
}

Input Schema

NameRequiredDescriptionDefault
resume_idYesInternal integer ID of saved candidate resume in local database

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.4.8

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already state read-only, idempotent, non-destructive, and open-world behavior. The description usefully adds that matching uses the client's LLM for deep reasoning and fit explanations, but it omits cost, latency, or LLM-configuration requirements.

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?

A single front-loaded sentence with no filler; every phrase conveys mechanism or output. It is concise and well structured, even if completeness is lacking elsewhere.

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

Completeness2/5

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

For a tool that depends on an external LLM and competes with many job-search and matching siblings, the description does not explain result scope, ranking, limits, or when to choose it. Output schema and annotations reduce the burden, but key invocation context is still missing.

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?

There is only one parameter and schema coverage is 100%, with resume_id fully described as an internal integer ID. The description adds no parameter-level meaning beyond the schema, so the baseline of 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?

Names a specific action (job matching) and resource (jobs), plus mechanism and output (LLM reasoning, fit explanation). It does not distinguish itself from closely related siblings such as match_jobs_to_resume or personalized_job_hunt, so it is clear but not fully differentiated.

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?

Provides no when-to-use guidance, exclusions, or named alternatives. An agent must infer from the name that it matches a saved resume to jobs, and must guess how it differs from sibling tools like match_jobs_to_resume.

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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