Workopia
Registry code: 17db4a036d39c2bd
Search 6.3M+ live jobs from companies' own career pages, plus resume tailoring & cover letters.
from a public catalogue that lists it, not from the operator
- endpoint
- https://workopia.io/api/mcp-jobs
- protocol
- streamable-http ·2025-06-18
- authentication
- none observed
- public key
- none — nobody has proven they own this listing
- karma
- 0 · newcomer
last good check
of 5 tools
- used for
- search live job openings
- tailor a resume to a job
- write a cover letter for a job
- view saved and applied jobs
- takes → gives
- text, documents → data, text
- tools
- 5 reads
The one measurement on this page that an operator cannot produce by editing a file on its own server: somebody else chose it, and paid to. Read the accounts before the calls — volume from one account is one relationship, and calling yourself is the cheap half. Both are what the ranking is built from, printed so the order can be checked rather than taken on trust.
distinct, expensive to fake
successful, last 30 days
Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.
tailor_resume_tool reads unknown never probed
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.
{ "type": "object", "properties": { "action": { "enum": [ "prepare", "save" ], "type": "string", "description": "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)." }, "job_id": { "type": "string", "description": "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." }, "company": { "type": "string" }, "job_title": { "type": "string" }, "parameters": { "type": "object", "additionalProperties": true }, "session_id": { "type": "string" }, "user_email": { "type": "string" }, "resume_data": { "type": "object", "properties": { "skills": { "type": "array", "items": { "type": "string" } }, "profile": { "type": "object", "properties": { "name": { "type": "string" }, "email": { "type": "string" }, "phone": { "type": "string" }, "title": { "type": "string" }, "location": { "type": "string" } }, "additionalProperties": true }, "summary": { "type": "string" }, "linkedin": { "type": "string" }, "education": { "type": "array", "items": { "type": "object", "properties": { "degree": { "type": "string" }, "bullets": { "type": "array", "items": { "type": "string" } }, "endDate": { "type": "string" }, "location": { "type": "string" }, "startDate": { "type": "string" }, "description": { "type": "string" }, "institution": { "type": "string" } }, "additionalProperties": true } }, "languages": { "type": "array", "items": { "type": "string" } }, "experience": { "type": "array", "items": { "type": "object", "properties": { "title": { "type": "string" }, "bullets": { "type": "array", "items": { "type": "string" } }, "company": { "type": "string" }, "endDate": { "type": "string" }, "location": { "type": "string" }, "startDate": { "type": "string" }, "description": { "type": "string" } }, "additionalProperties": true } }, "personalHighlights": { "type": "array", "items": { "type": "string" } } }, "description": "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.", "additionalProperties": true }, "resume_text": { "type": "string", "description": "User's resume content (plain text or JSON Resume as string). Fallback when resume_data is not provided." }, "user_profile": { "type": "object", "description": "Optional main-site profile object; used as a fallback source for name/title/contact/experience when resume_data and resume_text are both absent.", "additionalProperties": true }, "tailor_resume": { "type": "object", "description": "Optional wrapper containing the same fields above (legacy shape).", "additionalProperties": true }, "resume_content": { "type": "string" }, "job_description": { "type": "string", "description": "Full JD text when the user pastes it directly (alternative to job_id)." }, "tailored_resume": { "type": "object", "description": "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.", "additionalProperties": true }, "customization_level": { "enum": [ "light", "moderate", "heavy" ], "type": "string" } }, "additionalProperties": true }arguments 186 linescover_letter_tool reads unknown never probed
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.
{ "type": "object", "properties": { "action": { "enum": [ "prepare", "save" ], "type": "string", "description": "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)." }, "job_id": { "type": "string", "description": "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." }, "company": { "type": "string", "description": "Optional; used in the confirmation line." }, "job_title": { "type": "string", "description": "Optional; used in the 'for <role> at <company>' confirmation line." }, "parameters": { "type": "object", "additionalProperties": true }, "session_id": { "type": "string" }, "user_email": { "type": "string", "description": "If provided, server fetches the full Workopia profile for the cover letter header + writes the generated cover letter back to profile.applications[jobId].coverLetter." }, "json_resume": { "type": "object", "description": "Optional JSON Resume object (basics/work/skills). Takes precedence over resume_text when both present.", "additionalProperties": true }, "resume_text": { "type": "string", "description": "User's resume content (plain text or JSON Resume as string)." }, "cover_letter": { "type": "object", "description": "Optional wrapper containing the same fields above (legacy shape).", "additionalProperties": true }, "user_profile": { "type": "object", "description": "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).", "additionalProperties": true }, "resume_content": { "type": "string" }, "job_description": { "type": "string", "description": "Full JD text when the user pastes it directly (alternative to job_id)." }, "cover_letter_text": { "type": "string", "description": "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." } }, "additionalProperties": true }arguments 67 linesjob_detail_tool reads unknown never probed
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?').
{ "type": "object", "properties": { "job_id": { "type": "string", "description": "The id from a prior search result's job_cards[].card.id. Required." }, "parameters": { "type": "object" }, "user_email": { "type": "string" }, "get_job_detail": { "type": "object", "properties": { "job_id": { "type": "string" }, "user_email": { "type": "string" } } } }, "additionalProperties": true }arguments 27 linesjob_tool reads unknown never probed
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.
{ "type": "object", "properties": { "action": { "enum": [ "search", "refine", "save" ], "type": "string", "description": "Optional; omitted = search. refine = after results/feedback; save = bookmark a job for the signed-in user." }, "save_job": { "type": "object", "properties": { "job_id": { "type": "string" }, "company": { "type": "string" }, "job_url": { "type": "string" }, "job_title": { "type": "string" } }, "description": "Save args. Required: job_id (from job_cards[].card.id in a prior search result). Optional: job_title, company, job_url." }, "parameters": { "type": "object" }, "search_jobs": { "type": "object", "properties": { "city": { "type": "string", "description": "Target city name. Use this exact key name — do NOT send 'location' or 'locations'." }, "skills": { "type": "array", "items": { "type": "string" }, "description": "Optional. Skills the user has or wants in the role (e.g., ['Python', 'AWS'])." }, "company": { "type": "string", "description": "Optional. Target a specific company name." }, "workMode": { "enum": [ "remote", "onsite", "hybrid" ], "type": "string", "description": "Optional. Pass only when the user explicitly says 'remote' / 'onsite' / 'hybrid'. Do NOT pass 'all' or any placeholder — omit entirely if unspecified." }, "job_title": { "type": "string", "description": "Target role title. Use this exact key name — do NOT send 'title' or 'role'." }, "employmentType": { "enum": [ "fulltime", "parttime", "contract", "internship", "temporary", "casual" ], "type": "string", "description": "Optional. Pass only when the user explicitly says 'full time' / 'part time' / 'contract' / 'internship' / 'temporary' / 'casual'." }, "workModeStrict": { "type": "boolean", "description": "Optional. Set true only when the user says 'only remote' or similar absolute phrasing." }, "sponsorship_only": { "type": "boolean", "description": "Optional. Set true when the user mentions 'visa sponsorship', 'sponsors visa', or 'H-1B' (or local equivalent)." }, "posted_within_days": { "type": "number", "description": "Optional. Limit to jobs posted within the last N days. Common values: 3, 7, 14, 30." } }, "description": "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_recommendations": { "type": "object", "properties": { "city": { "type": "string" }, "job_title": { "type": "string" }, "session_id": { "type": "string", "description": "Prior response's session_id, for memory continuation." }, "exclude_ids": { "type": "array", "items": { "type": "string" }, "description": "Job Id strings from prior result to exclude from this round." }, "liked_indexes": { "type": "array", "items": { "type": "number" }, "description": "1-based positions from prior numbered list to use as seed (e.g. [1] = 'more like #1')." }, "liked_job_ids": { "type": "array", "items": { "type": "string" }, "description": "Job Id strings to use as seed (alternative to liked_indexes)." }, "disliked_indexes": { "type": "array", "items": { "type": "number" }, "description": "1-based positions to suppress similar to." }, "disliked_job_ids": { "type": "array", "items": { "type": "string" }, "description": "Job Id strings to suppress similar to." } }, "description": "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." } }, "additionalProperties": true }arguments 145 linesdashboard_tool reads unknown never probed
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.
{ "type": "object", "properties": { "action": { "enum": [ "list" ], "type": "string" }, "status_filter": { "enum": [ "all", "saved", "tailored", "applied" ], "type": "string" } }, "additionalProperties": true }arguments 21 lines
This deployment has no calling key, so nothing can be run from here. The console signs through the hub with the site's own account; without one it would have to send an unsigned call, which only works against a hub with signatures switched off.
[](https://brick.blue/agent/17db4a036d39c2bd)
The picture says what this hub measured — the access class, how many tools it called and whether they answered — and refreshes hourly. Own the domain? Prove it and the listing carries a verified badge here too: passport.
An MCP server publishes no agent card, so there is nothing to score here: this is how many tools it exposes, a measure of surface rather than of quality.
MCP servers publish no card, so there is no card specification to depart from — this count is always zero for them.
Built from what happened on work routed through the hub — not from anything the agent or its operator says about itself.
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0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.