"A server for finding job opportunities" matching MCP tools:

• cover_letter_tool

  Workopia

  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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• listMatchingSlots

  mcp

  Matching time slots for a quoted job Bookable arrival-window slots for a quoted job, computed by the smart-assignment matching engine: each slot lists the technicians actually available to start then (skills, weekly availability, existing schedule, time off and travel all checked), with a per-technician match score. Use it to find and offer appointment times an agent or integration can then confirm (POST /job-requests/{id}/confirm with the slot's business_time.datetime). Same grid the end-customer's slot picker shows; slot width defaults to the business's arrival window — override via ?step_minutes (5–240). The job must be quoted first (the quote sets the visit duration the matcher schedules).

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• report_status

  Datalastic Vessel Tracking & Maritime Intelligence

  Check an async report job by report_id (from report_request or report_list). Returns its status: _PENDING_ or _IN_PROGRESS_ (still generating — wait a bit and check again) or _DONE_. When _DONE_, result_url is a download link for the result ZIP; hand it to the user. Links are time-limited — if one has expired, run report_status again for a fresh link. The server never downloads the file itself.

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• cover_letter_tool

  Workopia — Job Search

  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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• candidate.set_saved_job

  workorai

  Set whether a PUBLISHED job is in the candidate's saved list (idempotent desired-state, NOT a toggle — pass saved:true to bookmark, saved:false to remove). Returns NOT_FOUND for a missing or non-public job.

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• delete_youtube_report_job

  Hermoso

  Stop a YouTube bulk reporting job. IRREVERSIBLE IN A WAY THAT IS EASY TO MISS: the job IS the history — deleting it discards every daily CSV it has accumulated, and a replacement job starts over with only a 30-day backfill, so anything older than that is gone for good. Call WITHOUT confirm first: nothing is deleted and you get the real job read back from YouTube (its report type and when it was scheduled) to show the user. Then call again with confirm:true. Needs a connected YouTube channel.

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• get_corpus_coverage

  SRI — MCP Server Inspector

  No arguments. Returns how many MCP servers have been read at source level, and the share of them with each category of finding (credential access, network egress, install-time execution, prompt-injection surface). Use this to judge whether checking a specific server is worth it before you look one up. It reports aggregate counts only - no per-server findings, and no verdict about any individual server.

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• create_card_checkout

  PostAgent — Print and Mail

  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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• searchJobs

  qubitsok — Quantum Computing Jobs, Papers & Researchers

  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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• upload_to_ipfs

  AGI Alpha

  Upload JSON metadata to IPFS via Pinata and return the ipfs:// URI. Use this BEFORE calling create_job (upload the job spec) or request_job_completion (upload the completion proof). Requires a Pinata JWT — get one free at https://app.pinata.cloud/developers/api-keys. JOB SPEC FORMAT (use for create_job) — schema v2: { "name": "AGI Job · <title>", "description": "<summary> — <details>", "image": "https://ipfs.io/ipfs/Qmc13BByj8xKnpgQtwBereGJpEXtosLMLq6BCUjK3TtAd1", "attributes": [ { "trait_type": "Category", "value": "research | development | analysis | creative | other" }, { "trait_type": "Locale", "value": "en-US" } ], "properties": { "schema": "agijobmanager/job-spec/v2", "kind": "job-spec", "version": "1.0.0", "locale": "en-US", "title": "Short job title", "category": "research | development | analysis | creative | other", "summary": "One-line summary", "details": "Full description of what needs to be done", "tags": ["relevant", "tags"], "deliverables": ["Concrete thing to deliver"], "acceptanceCriteria": ["Criterion validators will check"], "requirements": ["Any skill or tool requirement"], "payoutAGIALPHA": null, "durationSeconds": null, "employer": null, "chainId": 1, "contract": "0xB3AAeb69b630f0299791679c063d68d6687481d1", "ensPreview": "—", "ensURI": null, "generatedAt": "<ISO timestamp>", "createdVia": "your-agent-name" } } Note: "schema" is a plain string tag (not a URL) identifying the format version so agents and validators know how to parse the properties object. COMPLETION FORMAT (use for request_job_completion): { "name": "AGI Job Completion · <job title>", "description": "Final completion package for Job <jobId>. This metadata JSON serves as the Job Completion URI and resolves to the final submitted deliverable via its 'image' field for public validator review.", "image": "ipfs://<CID of primary deliverable — any file type: PNG, TXT, PDF, JSON, etc. Not necessarily an image — this NFT metadata field points to your main deliverable>", "attributes": [ { "trait_type": "Kind", "value": "job-completion" }, { "trait_type": "Job ID", "value": "<jobId>" }, { "trait_type": "Category", "value": "<category>" }, { "trait_type": "Final Asset Type", "value": "<PNG | PDF | TXT | JSON | etc.>" }, { "trait_type": "Locale", "value": "en-US" }, { "trait_type": "Completion Standard", "value": "Public IPFS deliverables" } ], "properties": { "schema": "agijobmanager/job-completion/v1", "kind": "job-completion", "version": "1.0.0", "locale": "en-US", "title": "<job title>", "summary": "Brief description of what was submitted and how it satisfies the job spec.", "jobId": 0, "jobSpecURI": "ipfs://<CID of original job spec>", "jobSpecGatewayURI": "https://ipfs.io/ipfs/<CID of original job spec>", "finalDeliverables": [ { "name": "Primary deliverable", "uri": "ipfs://<CID>", "gatewayURI": "https://ipfs.io/ipfs/<CID>", "description": "What this file contains and how it satisfies the job spec" } ], "validatorNote": "Confirm the 'image' field resolves publicly and review against the job spec acceptance criteria.", "completionStatus": "submitted", "chainId": 1, "contract": "0xB3AAeb69b630f0299791679c063d68d6687481d1", "createdVia": "your-agent-name", "generatedAt": "<ISO timestamp>", "submissionType": "Job Completion URI" } }

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• tailor_resume_tool

  Workopia

  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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• job_tool

  Workopia

  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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• create_card_checkout

  PostAgent

  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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• create_card_checkout

  PostAgent

  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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• pull_results

  CatchAll

  Retrieve the results of a job. Can be called before completion for partial results, or after completion for the full set. Returns clustered, validated, and enriched web results. While job status is active, call this repeatedly (typically page=1) to refresh partial output. When job reaches completed, iterate all pages. If job fails, call once more to capture any partial output.

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• create_training_job

  rvc-training

  Creates a private NiceVois job and returns a one-time PUT upload URL. Do not ask for consent before the first attempt because NiceVois remembers the current standing account agreement. If the server returns CONSENT_REQUIRED, ask for one acceptance of the linked standing agreement and retry once with acceptsStandingAgreement=true. This allocates a private training slot but does not start GPU work until the audio is uploaded and start_training is called.

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• search_jobs

  Dice MCP

  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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• run_contact_enrich

  DataMerge MCP

  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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• submit_job_result

  Conductor Relay MCP

  Submit the result payload for a job the calling agent has claimed, completing the job workflow. Bearer token required. Safe to retry with the same idempotency_key.

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• invoicing.generate

  Admin Substitute

  Generate an invoice for a completed job. If Stripe Connect is active, automatically creates a payment link. Returns invoice number, total, and payment URL. Requires: job_id from jobs.list (job must be status=Completed). Next step: payments.send_link if needed.

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