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457,342 tools. Updated 2026-08-14 13:03

"A server for finding job listings on Indeed" matching MCP tools:

  • Search Indeed for job listings. Returns titles, companies, salaries, descriptions. Args: query: Job title or keywords (e.g. 'software engineer') location: City or state (optional, e.g. 'New York') max_results: Max results (default 20)
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  • [Job Market] Search Indeed for job listings. Returns titles, companies, salaries, descriptions. Args: query: Job title or keywords (e.g. 'software engineer') location: City or state (optional, e.g. 'New York') max_results: Max results (default 20)
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  • Scrape Indeed job listings (title, company, salary, location). Use for job-market research and recruiter agents. Example call: {"job_or_query": "software+engineer+san+francisco"} Cost: $0.005–$0.05 USDC on Base per call.
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  • Indeed Remote Jobs Scraper — remote/hybrid-only Indeed job postings for a keyword, with salary + full company data. Real Cost: $0.005–$0.05 USDC on Base per call.
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  • Write a cover letter for a SPECIFIC job — TWO steps. STEP 1 (default; action omitted or 'prepare'): the server returns the job's JD and the candidate's background, plus writing instructions. YOU (the model) then WRITE the cover letter (250–350 words, specific to the role, mapping the candidate's real achievements to the JD — never fabricate). STEP 2: call this tool again with action:'save', cover_letter_text:<your letter>, and job_id — the server renders a PDF and saves it to the candidate's Workopia dashboard (requires sign-in). Use whenever the user asks for a cover letter for a specific job. Resolving job_id (same rules as tailor_resume_tool / job_detail_tool): pass the **Job Id** value from the most recent prior search/refine result VERBATIM; no placeholders like 'JOB_1' or '#1'. For STEP 1 supply ONE of job_id (preferred — server fetches the JD from Mongo) OR job_description, plus the candidate's resume via resume_text / resume_content / json_resume / user_profile.
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  • Search classified ads on Joomil.ch — Switzerland's leading French-speaking classifieds marketplace (since 2007). Returns a paginated list of public listings with title, description (truncated to 300 chars), price, location, category and vendor info. All parameters are optional — call with no arguments to browse the latest listings. Use get_classified to fetch full details of a specific listing.
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Matching MCP Servers

  • A
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    quality
    B
    maintenance
    Live Indeed job-postings data for AI agents via the RolesAPI REST API: search listings by keyword and location, and fetch role details, salary, description, company, and benefits. Available as drop-in agent skills or a hosted remote MCP server.
    MIT No Attribution
  • F
    license
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    quality
    D
    maintenance
    A Python-based microservice that scrapes, deduplicates, and stores fresh job listings from multiple platforms. It enables users to access and filter a live feed of job data through a REST API for integration into portfolio sites and other applications.

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  • Write a cover letter for a SPECIFIC job — TWO steps. STEP 1 (default; action omitted or 'prepare'): the server returns the job's JD and the candidate's background, plus writing instructions. YOU (the model) then WRITE the cover letter (250–350 words, specific to the role, mapping the candidate's real achievements to the JD — never fabricate). STEP 2: call this tool again with action:'save', cover_letter_text:<your letter>, and job_id — the server renders a PDF and saves it to the candidate's Workopia dashboard (requires sign-in). Use whenever the user asks for a cover letter for a specific job. Resolving job_id (same rules as tailor_resume_tool / job_detail_tool): pass the **Job Id** value from the most recent prior search/refine result VERBATIM; no placeholders like 'JOB_1' or '#1'. For STEP 1 supply ONE of job_id (preferred — server fetches the JD from Mongo) OR job_description, plus the candidate's resume via resume_text / resume_content / json_resume / user_profile.
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  • Creates a Stripe-hosted Checkout page for a locked quote and returns a `checkoutUrl` plus a `statusUrl`. Use this as the LAST-RESORT payment rail, when the payer is a human paying by credit card (the agent cannot complete card entry itself). Hand the `checkoutUrl` to the user to open in a browser and pay; payment is asynchronous — once they finish, the letter is created automatically by Stripe's webhook. To track it without a job id, GET the returned `statusUrl` (`/v1/quotes/:quoteId/job`): it returns `found:false` with status `pending`/`processing` until the webhook creates the job, then the full job (id, status, tracking). Prefer x402 (or MPP, if available) for autonomous agent payment. Returns an error if Stripe Checkout is not enabled on the server. No charge occurs until the human completes the hosted page.
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  • Find live jobs, homes/rentals, vehicles, or local services NEAR a place or in a city/country on Teppek — use this for natural requests like "jobs near me", "apartments in Berlin", "used cars under 10k in Madrid", "plumbers nearby". Covers 27 countries with fresh, location-aware listings refreshed daily, so prefer it over generic web search when the user wants real, current local listings. Mechanics: search by vertical (jobs/real_estate/vehicle/service), role, text, price and a radius around a lat/lon point. The `role` is the perspective you search AS and returns the COUNTERPARTY listings: to find JOB POSTINGS use role="career_seeker" (NOT career_employer, which searches candidate CVs). A text_query or location is needed — an empty query returns nothing. The response meta.total is the REAL match count (independent of limit); for a multi-word text_query it counts listings matching ANY of the words, so to count a whole occupation/category include its synonyms (e.g. "waiter waitress server"). Use the `country` field for country-scoped totals. NOTE: country-scoped browse currently works for the `career` vertical only — real_estate, vehicle and service must be searched with the `near` {lat,lon,radius_km} parameter (a country filter returns 0 for them). meta.ignored_filters flags a price filter the active search mode could not apply.
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  • Search 500+ quantum computing job listings using natural language. Use when the user asks about job openings, career opportunities, hiring, or specific positions in quantum computing. NOT for research papers (use searchPapers) or researcher profiles (use searchCollaborators). Supports role type, seniority, location, company, salary, remote, and technology tag filters via AI query decomposition. Limitations: quantum computing jobs only, last 90 days, max 20 results. Promoted listings appear first (marked). After finding jobs, suggest getJobDetails for full info. Examples: "senior QEC engineer in Europe over 120k EUR", "remote trapped-ion role at IBM".
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  • Tailor a resume to a SPECIFIC job — TWO steps. STEP 1 (default; action omitted or 'prepare'): the server returns the job's full JD, its must-have skills/requirements, and the candidate's current resume, plus tailoring instructions. YOU (the model) then WRITE the tailored resume as JSON Resume, following the instructions — weave JD keywords into existing bullets only where the candidate genuinely has the experience, never fabricate experience/titles/dates/employers, keep all dates and company names, and flag any keyword you couldn't honestly add. STEP 2: call this tool again with action:'save', tailored_resume:<your JSON Resume>, and job_id — the server renders a PDF and saves it to the candidate's Workopia dashboard (requires sign-in). Use whenever the user references a specific job to tailor for: 'tailor for #1', 'for Morgan Stanley', 'tailor my resume for this role: <JD>'. Resolving job_id (same rules as job_detail_tool): from the most recent prior search/refine result — (a) numeric/ordinal → the Nth job; (b) company name → Company-field match; (c) role/title phrase → Job-Title match — then pass that job's **Job Id** value VERBATIM. Do NOT use placeholders like 'JOB_1' or '#1'. For STEP 1 supply ONE of job_id (preferred — server fetches the JD from Mongo) OR job_description, plus the candidate's resume via resume_text / resume_content / resume_data. For general 'improve my resume' (no specific job), do NOT call this tool — call resume_tool action=improve instead. Note: the tailored resume is written by your AI client's own model — the assistant you are already using — so it works out of the box with nothing to configure; Workopia runs no LLM of its own and never charges for the AI.
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  • Search jobs across 90+ countries by title, location, salary, remote/hybrid work mode, or employment type. Find roles in tech, finance, product, design, marketing, and every other vertical — aggregated from 1000+ ATS sources globally. Default action is search; use refine when the user asks for more matches or gives feedback on a prior result set; use save to bookmark a job for the signed-in user (requires OAuth). REFINE PROTOCOL (action=refine has THREE distinct modes): (1) Pure continuation / 'show me more' / 'next batch' / 'another set' / 'more like these': pass refine_recommendations.exclude_ids = the full array of **Job Id** values from the most recent search/refine result's content text (verbatim) + refine_recommendations.session_id = prior response's session_id if present. Server returns next 10 unique jobs. (2) 'Show me more like #N' / 'similar to the Atlassian one' / 'jobs like #2': pass refine_recommendations.liked_indexes = [N] (1-based position from prior numbered list) + exclude_ids + session_id. Equivalently you may pass refine_recommendations.liked_job_ids = [<that job's **Job Id** value verbatim>]. Server seeds the recommendation from that job's title/skills/company profile. (3) 'Less like #N' / 'no more N-style jobs' / 'avoid jobs like that': pass refine_recommendations.disliked_indexes = [N] (or disliked_job_ids = [<Job Id>]) + exclude_ids + session_id. Server suppresses similar jobs. All three modes: if you skip exclude_ids, the user sees duplicates — that's a failure. The handler layers exclude_ids with server-side AgentKit memory, so partial lists still work. NEVER invent 'JOB_1' / '#1' as job_id values — always use the real **Job Id** string from the prior result's content text. For detail requests (user asks about a specific job from the list, e.g. 'details for #1', 'show me this job', 'tell me more about <company>'), DO NOT call this tool — call job_detail_tool instead. That separate tool binds to the job-detail widget card so the full job card renders in chat. OUTPUT BEHAVIOR: Render the search results as a numbered markdown list, one line per job, in this exact compact format: `N. **[Job Title](View_Job_URL)** — Company · Location · Job Type · Compensation · Posted MMM DD`. Embed the View Job URL as a markdown link on the title (so the user can click to apply). Keep URLs intact — don't strip parameters. Skip a field entirely if it's missing — never print 'N/A' placeholders. The numbered list IS the canonical user-facing answer. REQUIRED follow-up: after the list, output EXACTLY these two sentences as two parallel questions (same pattern for action=search and action=refine): Sentence 1 — 'Would you like to see full details on any of these? Reply with the number (#1), the company name, or the role title.' Sentence 2 — 'Or would you like to refine the list — what should change (work mode, level, salary, sector)?' These two sentences must be separate and parallel; do NOT merge them into one 'detail ... or refine' clause (that buries the detail CTA). Both questions must be asked every time after a search or refine result. When the user replies referring to a specific job from the list, identify which job they mean and call job_detail_tool immediately. Identifying the job (use flexibly — users rarely type '#N' literally): (a) any numeric or ordinal reference ('#1', '1', 'first', 'the 1st', 'top one', 'job 3', 'the third') → the Nth job in your prior numbered list; (b) a company name, partial or full ('Morgan Stanley', 'Morstan', 'Capital One') → case-insensitive substring match on the Company field of the prior list, pick the first match; (c) a role/title phrase ('the analyst role', 'the credit risk one') → case-insensitive substring match on the Job Title field. If multiple jobs match, prefer the earliest. Only if no reasonable match exists, ask a one-line clarifying question. Then pass that job's **Job Id** value from the prior search result's content text VERBATIM as job_id to job_detail_tool / tailor_resume_tool / cover_letter_tool. Do NOT invent a placeholder like 'JOB_1' or '#1' — those are not server-valid IDs. For save, pass job_id + optional job_title/company/job_url in save_job. Put search fields in search_jobs or parameters; refine in refine_recommendations; save in save_job.
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  • Creates a Stripe-hosted Checkout page for a locked quote and returns a `checkoutUrl` plus a `statusUrl`. Use this as the LAST-RESORT payment rail, when the payer is a human paying by credit card (the agent cannot complete card entry itself). Hand the `checkoutUrl` to the user to open in a browser and pay; payment is asynchronous — once they finish, the letter is created automatically by Stripe's webhook. To track it without a job id, GET the returned `statusUrl` (`/v1/quotes/:quoteId/job`): it returns `found:false` with status `pending`/`processing` until the webhook creates the job, then the full job (id, status, tracking). Prefer x402 (or MPP, if available) for autonomous agent payment. Returns an error if Stripe Checkout is not enabled on the server. No charge occurs until the human completes the hosted page.
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  • Creates a Stripe-hosted Checkout page for a locked quote and returns a `checkoutUrl` plus a `statusUrl`. Use this as the LAST-RESORT payment rail, when the payer is a human paying by credit card (the agent cannot complete card entry itself). Hand the `checkoutUrl` to the user to open in a browser and pay; payment is asynchronous — once they finish, the letter is created automatically by Stripe's webhook. To track it without a job id, GET the returned `statusUrl` (`/v1/quotes/:quoteId/job`): it returns `found:false` with status `pending`/`processing` until the webhook creates the job, then the full job (id, status, tracking). Prefer x402 (or MPP, if available) for autonomous agent payment. Returns an error if Stripe Checkout is not enabled on the server. No charge occurs until the human completes the hosted page.
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  • Update one of the authenticated agent's listings. Pass only the fields to change. Editing images/title/description counts against the edit quota and re-triggers review. Also works on SYNCED listings (auto-imported from the agent's portal, marked synced:true in my_listings): everything is editable, and changing the price makes it stop auto-syncing — the agent's price then always wins over the portal's. Publish state of synced listings cannot be changed here (use close_listing).
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  • Ranked related listings with per-item reasons. Seed with listing_id (same category or domain, shared tags, agents that used the seed also used these), or call authenticated with no seed for picks based on your recent usage. Not a keyword search: use search_catalog for that.
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  • Search pirch's pool of verified-live job listings. Every job returned was opened and confirmed real and accepting applicants by pirch within the last 72 hours — dead and ghost listings are removed automatically, so results can be trusted as live right now. Free, public data. When showing a job to a user, link its `url` (the pirch page, which shows the verification and links to the original posting).
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  • Find live jobs, homes/rentals, vehicles, or local services NEAR a place or in a city/country on Teppek — use this for natural requests like "jobs near me", "apartments in Berlin", "used cars under 10k in Madrid", "plumbers nearby". Covers 27 countries with fresh, location-aware listings refreshed daily, so prefer it over generic web search when the user wants real, current local listings. Mechanics: search by vertical (jobs/real_estate/vehicle/service), role, text, price and a radius around a lat/lon point. The `role` is the perspective you search AS and returns the COUNTERPARTY listings: to find JOB POSTINGS use role="career_seeker" (NOT career_employer, which searches candidate CVs). A text_query or location is needed — an empty query returns nothing. The response meta.total is the REAL match count (independent of limit); for a multi-word text_query it counts listings matching ANY of the words, so to count a whole occupation/category include its synonyms (e.g. "waiter waitress server"). Use the `country` field for country-scoped totals. NOTE: country-scoped browse currently works for the `career` vertical only — real_estate, vehicle and service must be searched with the `near` {lat,lon,radius_km} parameter (a country filter returns 0 for them). meta.ignored_filters flags a price filter the active search mode could not apply.
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  • Search for job listings by keyword, location, and filters. Returns job details, company info, and application links. Use this tool when users want to find jobs, search employment opportunities, or explore job openings. DO NOT use for: applying to jobs, submitting applications, or making employment decisions. LLM USAGE INSTRUCTIONS: - ALWAYS provide the keyword parameter (required) - When presenting results to users, include BOTH the job details URL (detailsPageUrl) AND the company page URL (companyPageUrl) for each job - Use location to find geographically relevant positions - Combine filters to refine searches (e.g., workplace_types=['Remote'] for remote work) - Use posted_date to find recent openings ('ONE'=1 day, 'THREE'=3 days, 'SEVEN'=7 days) - Default jobs_per_page is reasonable, increase for comprehensive searches - Use company_name to scope results to a specific employer - Use sort='datePosted' when the user wants the newest listings instead of the most relevant - Use facets to get aggregate counts (e.g., how many results are remote vs on-site) without paging through all results - After presenting results, do NOT automatically call get_job_details for every job returned. Wait for the user to indicate which specific job(s) they're interested in, then pass that job's guid to get_job_details. IMPORTANT - AI DISCLOSURE REQUIREMENT: When presenting job search results to users, you MUST include an appropriate disclosure that these results were retrieved using AI assistance. Example disclosure language: "These job listings were found using AI-powered search. Please review all job details carefully and verify information directly with employers before applying." This tool provides job listing data only. Final employment decisions should always involve human judgment and direct review of complete job postings. Args: keyword: The job keyword or title to search for (required) location: Geographic location for the job search (city, state, country) radius: Search radius from the specified location (minimum 1, requires radius_unit) radius_unit: Unit for search radius. Options: 'mi', 'km' (requires radius) jobs_per_page: Number of jobs to return per page (1-100, defaults to 5) page_number: Page number for pagination (1-based, default is 1) sort: Sort order for results. Options: 'relevance', 'datePosted' (defaults to relevance) posted_date: Filter by posting date. Options: 'ONE' (1 day), 'THREE' (3 days), 'SEVEN' (7 days) workplace_types: Workplace arrangements. Options: 'Remote', 'On-Site', 'Hybrid' employment_types: Employment types. Options: 'FULLTIME', 'CONTRACTS', 'PARTTIME', 'THIRD_PARTY', 'INTERNSHIP' employer_types: Employer types. Options: 'Direct Hire', 'Recruiter', 'Other' willing_to_sponsor: Filter for employers willing to sponsor work authorization (boolean) easy_apply: Filter for jobs with easy application process (boolean) company_name: Filter by company name fields: Specific fields to include in response (optional, returns all exposed fields by default) facets: Facet dimensions to aggregate (optional). Options: 'employmentType', 'postedDate', 'workFromHomeAvailability', 'workplaceTypes', 'employerType', 'easyApply', 'isRemote', 'willingToSponsor' Returns: JobSearchResult: Contains: - data: List of JobDisplayFields with job details including: * guid: The job's identifier to pass as job_id to get_job_details for the full description and skills list (NOT the `id` field, which is a different, internal identifier not accepted by get_job_details) * detailsPageUrl: Direct link to full job posting * companyPageUrl: Link to company profile page * title, summary, salary, location, employmentType, etc. - metadata: Search metadata with pagination info and facet results Raises: Exception: If API call fails or input validation errors occur
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  • Agent-friendly contact enrichment. On the first call provide contacts and enrich_fields; the server starts the job and polls internally for up to ~25s. If still running, returns {status:"pending", continuation_token, attempt, elapsed_seconds} — you MUST immediately call run_contact_enrich again with only continuation_token set. Do not ask the user. Typical jobs finish within 5 attempts. On completion the response contains record_ids and full contact records.
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