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BACH-AI-Tools

LinkedIn Api8 MCP Server

search_jobs

Search LinkedIn jobs using keywords and filters like location, company, date posted, salary, job type, experience level, and more. Find relevant job listings quickly.

Instructions

Search Jobs

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordsYesExample value: golang
locationIdNoplease follow this link to find location id92000000
companyIdsNoplease follow this link to find company id
datePostedNoit could be one of these; anyTime, pastMonth, pastWeek, past24Hours
salaryNoit could be one of these; 40k+, 60k+, 80k+, 100k+, 120k+, 140k+, 160k+, 180k+, 200k+ Example: 80k+
jobTypeNoit could be one of these; fullTime, partTime, contract, internship Example: contract
experienceLevelNoit could be one of these; internship, associate, director, entryLevel, midSeniorLevel. executive example: executive
titleIdsNoplease follow this link to find title id by title
functionIdsNoplease follow this link to find function id
startNoit could be one of these; 0, 25, 50, 75, 100, etc. The maximum number of start is 975
industryIdsNoplease follow this link to find industry id
onsiteRemoteNoit could be one of these; onSite remote hybrid example: remote
sortNoit could be one of these; mostRelevant, mostRecent
Behavior1/5

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

With no annotations, the description carries full burden for behavioral disclosure. It states nothing about outputs, side effects, authentication needs, or pagination, which is critical for a search tool with 13 parameters.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

At two words, the description is overly terse at the expense of informativeness. Conciseness should not sacrifice necessary details; here it provides zero actionable information.

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

Completeness1/5

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

Given the tool's complexity (13 parameters, no output schema, no annotations), the description is completely inadequate. It does not address common user concerns like filtering behavior, result limits, or error handling.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although schema coverage is 100%, the description adds no meaning beyond what the schema already provides. Many parameter descriptions in the schema are vague (e.g., 'please follow this link'), and the tool description fails to clarify or summarize usage patterns.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Search Jobs' is a tautology, restating the tool name without adding any specific verb or resource scope. It fails to distinguish from sibling tools like 'search_jobs_v2' or 'get_company_jobs'.

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

Usage Guidelines1/5

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

No guidance is provided on when to use this tool versus alternatives. There is no mention of use cases, prerequisites, or exclusions, leaving the agent with no decision support.

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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