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felipfr

LinkedIn MCP Server

by felipfr

search_jobs

Search LinkedIn jobs using keywords, location, company, salary, experience level, and other filters to find relevant opportunities tailored to your criteria.

Instructions

Search LinkedIn jobs with advanced filters and criteria

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
companyNoCompany name
datePostedNoDate posted filter
experienceLevelNoExperience level
functionIdNoFunction ID
industryIdNoIndustry ID
jobTypeNoJob type (full-time, part-time, etc.)
keywordsNoJob search keywords
locationNoJob location
salaryNoSalary range

Implementation Reference

  • src/server.ts:105-122 (registration)
    Registers the 'search-jobs' MCP tool with input schema from linkedinApiSchemas and a handler that logs the request, calls clientService.searchJobs, and returns formatted response.
    this.server.tool(
      'search-jobs',
      'Search for LinkedIn job postings based on various criteria',
      linkedinApiSchemas.searchJobs,
      async (params) => {
        this.logger.info('Executing LinkedIn Job Search', {
          keywords: params.keywords,
          location: params.location
        })
        try {
          const jobs = await this.clientService.searchJobs(params)
          return this.createResourceResponse(jobs)
        } catch (error) {
          this.logger.error('LinkedIn Job Search Failed', error)
          throw error
        }
      }
    )
  • Core handler function that builds query parameters from input and makes authenticated GET request to LinkedIn /jobs/search endpoint using shared makeRequest method.
    public async searchJobs(params: SearchJobsParams): Promise<SearchJobsResult> {
      const queryParams = new URLSearchParams()
    
      const paramMapping: Record<string, string | undefined> = {
        keywords: params.keywords,
        location: params.location
      }
    
      Object.entries(paramMapping)
        .filter(([_, value]) => value !== undefined)
        .forEach(([key, value]) => queryParams.append(key, value as string))
    
      this.appendArrayParams(queryParams, {
        'company-name': params.companies,
        'job-type': params.jobType
      })
    
      return this.makeRequest<SearchJobsResult>('get', `/jobs/search?${queryParams.toString()}`)
    }
  • Zod schema defining the input parameters for the search-jobs tool, used in MCP tool registration.
    searchJobs: {
      companies: z.array(z.string()).optional().describe('Filter by companies'),
      jobType: z.array(z.string()).optional().describe('Filter by job type (e.g., Full-Time, Contract)'),
      keywords: z.string().optional().describe('Keywords to search for in job postings'),
      location: z.string().optional().describe('Filter by location')
    },
  • TypeScript interface defining the SearchJobsParams for type safety in the handler.
    export interface SearchJobsParams {
      keywords?: string
      location?: string
      companies?: string[]
      jobType?: string[]
    }
  • TypeScript interface defining the expected SearchJobsResult from LinkedIn API.
    export interface SearchJobsResult {
      jobs: {
        id: string
        title: string
        companyName: string
        location: string
        description?: string
        listedAt: number
        expireAt?: number
      }[]
      paging: {
        count: number
        start: number
        total: number
      }
    }
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. While 'search' implies a read-only operation, the description doesn't address important behavioral aspects: whether authentication is required, rate limits, pagination behavior, result format, or what 'advanced filters' specifically means. For a search tool with 9 parameters and no annotation coverage, this is insufficient.

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, efficient sentence that immediately communicates the core functionality. Every word earns its place: 'Search' (action), 'LinkedIn jobs' (resource), 'with advanced filters and criteria' (capability). There's no wasted verbiage or redundant information.

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 search tool with 9 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what constitutes a successful search, what the return format looks like, whether authentication is required, or how results are structured. The agent would need to guess about important behavioral aspects despite the clear purpose statement.

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 9 parameters with clear descriptions. The description adds 'advanced filters and criteria' which conceptually maps to the parameters but provides no additional semantic context beyond what's in the schema. This meets the baseline for high schema coverage.

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?

The description clearly states the verb ('search') and resource ('LinkedIn jobs'), making the purpose immediately understandable. It adds 'with advanced filters and criteria' which provides useful context about capabilities. However, it doesn't explicitly differentiate this from sibling tools like 'get_job' or 'get_saved_jobs', which would require more specific distinction.

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?

The description provides no guidance on when to use this tool versus alternatives like 'get_job' (for retrieving a specific job) or 'get_saved_jobs' (for retrieving saved jobs). There's no mention of prerequisites (e.g., whether authentication is required) or typical use cases. The agent must infer usage from the tool name alone.

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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