Search 6.3M+ live jobs from companies' own career pages, plus resume tailoring & cover letters.
Search 6.3M+ live job listings with resume tailoring and cover letter generation.
Captured live from the server via tools/list.
tailor_resume_tool
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.
Parameters (15)
job_idstring
ID of a job from a prior search/refine result. Use the **Job Id** value from the prior search result's content text VERBATIM. Server fetches full JD from Mongo.
job_descriptionstring
Full JD text when the user pastes it directly (alternative to job_id).
job_titlestring
companystring
resume_textstring
User's resume content (plain text or JSON Resume as string). Fallback when resume_data is not provided.
resume_contentstring
resume_dataobject
PREFERRED shape — structured resume per utils/tailor/types.ts ResumeTree. Server, widget, and main-site PDF template all consume this exact shape. Collect these fields from the user before calling when possible.
user_profileobject
Optional main-site profile object; used as a fallback source for name/title/contact/experience when resume_data and resume_text are both absent.
user_emailstring
session_idstring
customization_levelstring
actionstring
Omit or 'prepare' = STEP 1 (server returns JD + resume + instructions for you to tailor). 'save' = STEP 2 (pass tailored_resume; server renders a PDF and saves it to the dashboard; requires sign-in).
tailored_resumeobject
STEP 2 only: the tailored resume you generated, as a JSON Resume object (or a JSON string). The server renders it to PDF and stores it on profile.applications[job_id].resumeTailor.
tailor_resumeobject
Optional wrapper containing the same fields above (legacy shape).
parametersobject
cover_letter_tool
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.
Parameters (14)
job_idstring
ID of a job from a prior search/refine result. Use the **Job Id** value from the prior search result's content text VERBATIM. Server fetches full JD from Mongo.
job_descriptionstring
Full JD text when the user pastes it directly (alternative to job_id).
job_titlestring
Optional; used in the 'for <role> at <company>' confirmation line.
companystring
Optional; used in the confirmation line.
resume_textstring
User's resume content (plain text or JSON Resume as string).
resume_contentstring
json_resumeobject
Optional JSON Resume object (basics/work/skills). Takes precedence over resume_text when both present.
user_profileobject
Optional main-site profile object; used as a fallback source for summary/skills/experience and for the cover letter header (firstName, lastName, email, phone, city, country).
user_emailstring
If provided, server fetches the full Workopia profile for the cover letter header + writes the generated cover letter back to profile.applications[jobId].coverLetter.
session_idstring
actionstring
Omit or 'prepare' = STEP 1 (server returns JD + background + instructions for you to write). 'save' = STEP 2 (pass cover_letter_text; server renders a PDF and saves it to the dashboard; requires sign-in).
cover_letter_textstring
STEP 2 only: the cover letter you wrote (plain text). The server renders it to PDF and stores it on profile.applications[job_id].coverLetter.
cover_letterobject
Optional wrapper containing the same fields above (legacy shape).
parametersobject
job_tool
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 se
Parameters (5)
actionstring
Optional; omitted = search. refine = after results/feedback; save = bookmark a job for the signed-in user.
search_jobsobject
Search args. Required: city. Optional filters surface only when the user explicitly mentions them — omit otherwise. job_title+city uses indexed snapshot; company+city (optional job_title) uses legacy DB match.
refine_recommendationsobject
Refine args. Pass exclude_ids (array of Job Id strings from prior result) and session_id always. For 'more like #N': pass liked_indexes=[N] or liked_job_ids=[<Job Id>]. For 'less like #N': pass disliked_indexes=[N] or disliked_job_ids=[<Job Id>]. job_title/city optional — auto-filled from prior search via session memory.
save_jobobject
Save args. Required: job_id (from job_cards[].card.id in a prior search result). Optional: job_title, company, job_url.
parametersobject
job_detail_tool
Render the full job-detail card for a specific job the user asks about. Use this whenever the user references a particular job from a prior search result — by number (#1, '1', 'first', 'the 3rd one', 'job 3'), by company name (partial or full, e.g. 'Morgan Stanley', 'Morstan'), by role/title phrase ('the analyst role', 'the credit risk one'), or by any 'show me this job' / 'tell me more about X' / 'view this role' style request. Resolving job_id from user reference: identify the right job from the most recent prior search/refine result (the numbered list you generated): (a) numeric/ordinal → the Nth job; (b) company name → substring match on Company field; (c) role/title phrase → substring match on Job Title field. Then pass that job's **Job Id** value from the prior search result's content text VERBATIM as job_id. Do NOT use a placeholder like 'JOB_1', '#1', or any synthetic id — only the real **Job Id** string from the prior result is server-valid. Required: job_id. OUTPUT BEHAVIOR: Render the response as a structured markdown card with the job's title (linked to the apply URL), company, location, salary, employment type, work mode, must-have skills, key requirements, highlights, and summary. Follow it with a brief next-step hint (e.g. 'Want to save it, find similar roles, ask about the company, or tailor your resume for this role?').
Parameters (4)
job_idstring
The id from a prior search result's job_cards[].card.id. Required.
get_job_detailobject
user_emailstring
parametersobject
dashboard_tool
Show the signed-in user's Workopia dashboard (saved, tailored, and applied jobs + latest resume). Requires OAuth. Default action is list; optional status_filter (all | saved | tailored | applied). Use whenever the user asks to recall their Workopia activity: 'my applications', 'what jobs have I saved / applied to / tailored', 'show my dashboard', 'where did I leave off'. Returns a secure link to open the full dashboard on the web.
Job search, resume tailoring, cover letters & application tracking — available via the Model Context Protocol for Claude Code, Claude Desktop, Cursor, Windsurf, and other MCP clients.
Note: This is a hosted MCP server. Connect via the public endpoint below and sign in with Workopia (OAuth) — no API key to manage. All tools require a free Workopia sign-in; searching, job detail, resume tailoring, cover letters, and your dashboard are all scoped to your account. Resume tailoring and cover letters run on your own AI client's model — Workopia hosts no LLM and never charges for AI.
Endpoint
code
https://workopia.io/api/mcp-jobs
Transport: Streamable HTTP
Auth: OAuth 2.0 (Dynamic Client Registration + PKCE; handled automatically by the client)
On first tool use, Claude Code opens the Workopia OAuth flow in your browser; the token is then stored and refreshed automatically.
Tools
Tool
What it does
job_tool
Search millions of live jobs across 90+ countries (employer career pages + ATS feeds — Lever, Greenhouse, Workday). Save a job to your account (sign-in).
job_detail_tool
Full detail for a single job — salary, requirements, skills, company.
tailor_resume_tool
Tailor your resume to a specific job description.
cover_letter_tool
Draft a cover letter for a specific role.
dashboard_tool
Open your Workopia dashboard — saved, tailored & applied jobs + latest resume (sign-in).
Quick start (manual config)
Claude Desktop — add to claude_desktop_config.json:
Free at launch. A free Workopia sign-in (OAuth) is required to use the tools — searching, job detail, resume tailoring, cover letters, and your dashboard. Need a higher quota or a custom arrangement? Email shuang@heraai.one.
Report a bug / request a feature
Found something broken or want a tool improved? Open an issue — this repo is where we track bugs and feedback for the Workopia MCP server and Claude Code plugin. For account, billing, or anything private, email shuang@heraai.one.
