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458,051 tools. Updated 2026-08-14 20:18

"Job search for senior software engineer positions" matching MCP tools:

  • 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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  • 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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  • Find compensation surveys that benchmark a specific job title or position (e.g. 'Software Engineer', 'CPA', 'Sr Acct', 'head of finance'). Combines literal title matching with embedding-based semantic similarity, so abbreviations and paraphrases work — 'CPA' returns 'Certified Public Accountant', 'Sr Acct' returns 'Senior Accountant'. Reports per position are ranked to surface broad-coverage surveys (general-industry, US scope) before niche cuts.
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  • [HR & Compensation] Search H1B visa salary data. Returns employer, job title, salary, location, and case status. Args: job_title: Job title to search (e.g. 'software engineer', 'data scientist') company: Company name (e.g. 'Google', 'Microsoft') location: City or state (e.g. 'San Francisco', 'CA')
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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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Matching MCP Servers

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    Enables Claude to function as a full-stack software engineer with comprehensive development capabilities including project creation, database management, frontend/backend development, testing, deployment, and DevOps operations across multiple frameworks and technologies.
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    A personal job-search assistant for Claude Desktop that searches real job boards, scores each job 0–100 for fit, and displays a ranked board for fast triage.
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Matching MCP Connectors

  • Hibrit iş ilanı arama MCP sunucusu — anahtarsız resmî ATS board API'leri (Greenhouse, Lever,…

  • Paid job search: 7 matched roles, each with a tailored CV and cover letter.

  • Search JobYap job postings by natural-language query. Matches job titles, falling back to significant keywords when the full phrase finds little. Returns result ids, titles and citable URLs for use with fetch. For structured filtering (location, company, remote, freshness) prefer search_jobs.
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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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  • AI-powered candidate screening and ranking for recruiters, hiring managers, ATS providers and recruitment AI agents. Ingests a job description and 1-50 candidate resumes, returning a ranked shortlist with score breakdowns across five weighted criteria: skills_match (tech stack and soft skills extracted from JD vs resume), experience_match (years vs seniority level inferred from JD), education_match (degree level + top-school detection), role_progression (Junior to Senior to Lead patterns), culture_fit_estimate (remote/hybrid, startup vs enterprise). Per candidate: overall_score 0-100, matched/missing skills, red_flags (job hopping, employment gaps, seniority mismatch), green_flags (long tenure, promotions), 3-5 interview questions, fit_summary. Diversity signals are first-name proxies ONLY with mandatory ethical WARNING. All processing is local -- no external API calls, instant response, privacy-preserving.
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  • Look up a MITRE ATT&CK threat group (intrusion set) or software entry by name or ID for authorized penetration testing and threat intelligence. Returns the group or software record: ATT&CK ID, display name, known aliases, type (group vs. software), description, and the techniques it uses with procedure-level context from public ATT&CK reporting. Accepts exact ATT&CK IDs (G0007 for threat groups, S0002 for software) or keyword/name search (e.g., "APT28", "Mimikatz", "Lazarus Group"). Equally useful for defenders building detection coverage around specific adversary tradecraft.
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  • Queries CNAE (National Classification of Economic Activities) from IBGE. CNAE is the official classification for economic activities in Brazil. Hierarchical structure: - Section (letter A-U): 21 main categories - Division (2 digits): 87 divisions - Group (3 digits): 285 groups - Class (4-5 digits): 673 classes - Subclass (7 digits): 1,332 subclasses Features: - Search by CNAE code - Search by activity description - List by hierarchical level - Show complete hierarchy Examples: - Search software: busca="software" - Specific code: codigo="6201-5/01" - View section: codigo="J" - List divisions: nivel="divisoes" Behavior: read-only and idempotent — a live GET against the public IBGE CNAE API. Returns Markdown.
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  • Get the subaccount summary: equity, freeCollateral, marginEnabled, open perpetual positions and asset positions.
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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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  • AI-powered candidate screening and ranking for recruiters, hiring managers, ATS providers and recruitment AI agents. Ingests a job description and 1-50 candidate resumes, returning a ranked shortlist with score breakdowns across five weighted criteria: skills_match (tech stack and soft skills extracted from JD vs resume), experience_match (years vs seniority level inferred from JD), education_match (degree level + top-school detection), role_progression (Junior to Senior to Lead patterns), culture_fit_estimate (remote/hybrid, startup vs enterprise). Per candidate: overall_score 0-100, matched/missing skills, red_flags (job hopping, employment gaps, seniority mismatch), green_flags (long tenure, promotions), 3-5 interview questions, fit_summary. Diversity signals are first-name proxies ONLY with mandatory ethical WARNING. All processing is local -- no external API calls, instant response, privacy-preserving.
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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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  • Searches FoundRole's published content by semantic similarity and returns the most relevant sources for a job-search question: career-guidance blog articles plus FoundRole site pages that describe the product's features (job tracker, Pro plan and pricing, H1B salary data, AI job search) and industry/sector career landings. Each article carries a title, url, summary, a content excerpt, publication date, and tags; each page carries a title, url, description, and its FAQ entries — enough material to answer the question and link the source. Three optional facets add further result groups: company returns FoundRole's employer profile pages matching that company name; job_title and location return the live job-listing landing pages for that role and place, with open-job counts. The facets describe what the user is asking about — a company mentioned only in passing does not need the company facet. Returns empty groups when nothing is relevant rather than padding with off-topic content. Results are the closest matches to the given question, not an index of the site's full coverage; questions about overall topic coverage are answered by knowledge_topics, which lists the blog's categories and tags with article counts. It does not search job listings; jobs_search covers live roles. Each response includes a system_instruction describing how to present the sources.
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  • Look up real H-1B base salaries for a job title from DOL LCA disclosures — a market wage benchmark backed by actual filed salaries (not estimates). Answers 'what do H-1B software engineers earn at company X / in city Y'. Filter by job title, and optionally by employer, city, and year. Returns salary statistics (count, min / median / average / max) plus a sample of individual records (employer, title, salary, location, dates).
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  • Retrieve a job's full description and associated skills by job ID. Use this tool when users want more detail about a specific job than search_jobs returns (e.g. after finding a job via search_jobs and wanting its full description and skills list). DO NOT use for: searching/discovering jobs by keyword (use search_jobs instead). LLM USAGE INSTRUCTIONS: - Only call this after the user has picked out a specific job from search_jobs results (or otherwise supplied a job's guid directly) — do not call it speculatively for every search result. - After returning job details, do NOT automatically call get_company. Instead, offer to look up the company (e.g. "Want me to pull up more info on the company?") and only call get_company, passing this same job_id, if the user says yes. Args: job_id: The unique identifier of the job to retrieve (required). This must be the job's `guid` field from a search_jobs result, NOT its `id` field — `id` is a different, internal identifier that this tool does not accept. Returns: JobDetails: Contains: - description: The full job description - skills: List of Skill objects associated with the job Raises: Exception: If the API call fails, the job is not found, or input validation errors occur
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  • Fetch one job posting by its source engine and native id, returned as { meta, job } with the same flat Job shape search_jobs yields. The id is the native upstream id as returned by that source in search results (NOT prefixed with the source name). Exception: for source="ats" the id is a composite "board:company:nativeId" string (e.g. "greenhouse:airbnb:7995153") so the lookup can round-trip to the right ATS board. Not every source supports single-job lookup — a search-only source returns a not_supported / not_found error.
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  • Get full detail for a Tuki solution: description, who it is for, capabilities, status and contact / CTA. Use after `list_solutions` or when the user asks about a specific Tuki product (WhatsApp Booking OS, boutique ticketing, rental inventory software, event post-sale, tailor-made tourism software).
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