Agent.ai MCP Server

search_linkedin_jobs

Find relevant job postings on LinkedIn by specifying keywords, location, and filters like job type, experience level, and remote options.

Instructions

Search for job postings on LinkedIn by keyword, location, and filters.

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": {
      "keyword": "<keyword>"
    },
    "name": "search_linkedin_jobs"
  }
}

Input Schema

Name	Required	Description
`keyword`	Yes	Search keyword for job titles or skills.
`location`	No	City or location to search in.
`country`	No	Country code (e.g., 'US', 'FR', 'UK').
`job_type`	No	Type of employment.
`experience_level`	No	Required experience level.
`remote`	No	Work location type.
`time_range`	No	When the job was posted.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0May 27, 2026

TDQS

B3.4/5.0

Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It does not mention whether the tool is read-only, requires authentication, or has rate limits. Only states the search action, leaving safety and behavior unclear.

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?

The description is a single sentence of 12 words, front-loaded with verb and resource. No redundant information, every word earns its place.

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?

The tool has 7 parameters with enums but no output schema. The description does not explain return format, pagination, error handling, or result limits. For a search tool, this is insufficiently complete.

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 parameters. The description adds no extra meaning beyond listing keyword, location, and filters. Baseline 3 is appropriate.

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 clearly states the verb 'Search', the resource 'job postings on LinkedIn', and the scope 'by keyword, location, and filters'. This distinguishes it from siblings like search_linkedin_people or get_linkedin_job_posting.

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 description implies usage for searching jobs but does not provide when-to-use guidance, exclusions, or alternatives. It is adequate for a simple search tool but lacks explicit context.

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

Deploy Server