LinkedIn MCP Zero

by SanthaKumar-K-2004

Overview Schema Source Related Servers Discussions Admin

generate_cover_letter

Read-onlyIdempotent

Creates a tailored cover letter of up to 200 words from a LinkedIn job ID or URL and a resume ID, using your LLM to match the candidate's experience to the role.

Instructions

Generate a tailored, compelling cover letter (max 200 words) using the client's LLM.

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": {
      "job_id": "<job_id>",
      "resume_id": "<resume_id>"
    },
    "name": "generate_cover_letter"
  }
}

Input Schema

Name	Required	Description	Default
`job_id`	Yes	LinkedIn numerical job ID (e.g. '4481120122') or job URL
`resume_id`	Yes	Internal integer ID of candidate resume

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.4.8Nov 4, 2026

TDQS

B3.2/5.0

Behavior3/5

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

Annotations already declare readOnlyHint/idempotent/non-destructive and openWorld, so the safety profile is covered. The description adds the genuinely useful detail that generation is performed by the client's LLM and is capped at 200 words, which explains why an LLM client is invoked at all. It does not say whether the letter is persisted, returned only, or how long generation takes.

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?

A single front-loaded sentence with the constraint placed before the agent commits to a call. 'Compelling' is mild marketing filler but costs almost nothing.

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?

An output schema exists, so return values need not be explained. However, for a generation tool with openWorld and LLM usage, the description omits whether the letter is saved, whether repeated calls with the same IDs yield the same text, and when to prefer it over tailor_resume.

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% and both parameters are documented in-schema (job ID or URL, internal resume integer ID). The description adds no further meaning about either parameter, 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?

Specific verb 'Generate' plus concrete resource 'cover letter' with two qualifiers (tailored, max 200 words) that tell an agent exactly what it will get. It does not explicitly differentiate itself from the sibling 'tailor_resume', so it stops short of 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?

No when-to-use or when-not-to-use guidance is given, and no prerequisites (e.g. that a real job_id and an existing resume_id are needed). With 'tailor_resume' and 'analyze_resume' among 33 siblings, the agent must infer which tool produces which artifact.

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