Workopia
Server Details
AI job search across 90+ countries (employer career pages + ATS feeds — Lever, Greenhouse, Workday), plus resume tailoring, cover letters, job detail & dashboard. Hosted; OAuth sign-in.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.5/5 across 5 of 5 tools scored.
Each tool has a clearly distinct purpose: job search (job_tool), job details (job_detail_tool), resume tailoring (tailor_resume_tool), cover letter writing (cover_letter_tool), and dashboard view (dashboard_tool). There is no overlap or ambiguity between these functions.
All tool names follow a consistent snake_case noun_tool pattern (e.g., job_tool, dashboard_tool, cover_letter_tool). Names are descriptive and uniform, making it easy to predict the tool's function from its name.
With 5 tools, the set is well-scoped for a job application assistant. It covers the essential workflows (search, view details, tailor resume, write cover letter, track activity) without unnecessary bloat or missing core functions.
The tool surface covers the main job search and application preparation workflows. However, there is a minor gap: no direct in-tool job application submission (only save and an external apply link). Additionally, user profile management is absent, but the dashboard partially addresses tracking.
Available Tools
5 toolscover_letter_toolADestructiveInspect
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:, 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.
| Name | Required | Description | Default |
|---|---|---|---|
| action | No | 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 | No | 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 | No | Optional; used in the confirmation line. | |
| job_title | No | Optional; used in the 'for <role> at <company>' confirmation line. | |
| parameters | No | ||
| session_id | No | ||
| user_email | No | 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 | No | Optional JSON Resume object (basics/work/skills). Takes precedence over resume_text when both present. | |
| resume_text | No | User's resume content (plain text or JSON Resume as string). | |
| cover_letter | No | Optional wrapper containing the same fields above (legacy shape). | |
| user_profile | No | 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). | |
| resume_content | No | ||
| job_description | No | Full JD text when the user pastes it directly (alternative to job_id). | |
| cover_letter_text | No | 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. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses the two-step process, that step 2 renders a PDF and saves to dashboard requiring sign-in, and that step 1 returns JD and background. Annotations show destructiveHint: true, and description confirms the save action. No contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is detailed and front-loaded with the two-step process and instructions. While somewhat verbose, every sentence serves a purpose and the structure with step numbers aids clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a two-step tool with 14 parameters, no output schema, and dependency on sibling tools, the description covers the workflow, param usage, and server actions comprehensively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 79%, high. Description adds meaning for action (step explanation), job_id (verbatim rule), cover_letter_text (step 2 only), and relations like user_email fetching profile. Exceeds baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it writes a cover letter for a specific job, with a two-step process. It distinguishes from siblings like tailor_resume_tool by outlining the unique workflow of prepare and save actions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Use whenever the user asks for a cover letter for a specific job.' Provides when-not by warning against placeholders and explaining alternative param usage. References sibling tools for job_id resolution.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
dashboard_toolARead-onlyInspect
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.
| Name | Required | Description | Default |
|---|---|---|---|
| action | No | ||
| status_filter | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds significant behavioral context: requires OAuth authentication, returns a secure web link, and explains default action and optional status filter. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is relatively concise, front-loading the main purpose and then adding necessary details (OAuth, default action, example queries, return type). A slight improvement would be to combine some sentences, but overall it is well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that there is no output schema, the description explains the return type (secure link). It covers authentication, parameters, and usage examples. It lacks details on error handling or edge cases, but for a simple read-only dashboard listing, it is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains the default action ('list') and the allowed values for status_filter ('all|saved|tailored|applied'). While this helps, it does not provide detailed semantics for each parameter, such as what 'tailored' means in context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool shows the signed-in user's Workopia dashboard with specific components (saved, tailored, applied jobs, latest resume). It distinguishes from sibling tools (cover_letter_tool, job_detail_tool, etc.) which cover specific sub-tasks. The verb 'show' and resource 'dashboard' are explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear when-to-use guidance with example user queries ('my applications', 'show my dashboard'). It mentions default action and optional filter. However, it does not explicitly state when not to use or list alternatives among sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
job_detail_toolARead-onlyInspect
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?').
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | No | The id from a prior search result's job_cards[].card.id. Required. | |
| parameters | No | ||
| user_email | No | ||
| get_job_detail | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate read-only and non-destructive. Description adds rich details: renders a structured markdown card with specific sections, and a next-step hint. It also explains the resolution process, which is beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Somewhat long but well-structured with purpose, usage, output behavior. Every sentence adds value, though could be slightly more concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, but description explains output format. It covers resolution of job_id and rendering details. Nested objects are clarified via description of the key parameter. Sufficient for the tool's purpose.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 25% (only job_id described, but that's the key param). Description provides extensive guidance on how to derive job_id from user references, compensating for missing schema descriptions. However, other parameters (parameters, user_email, get_job_detail) are not explained.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states 'Render the full job-detail card for a specific job' and specifies when to use: when user references a particular job. Distinguishes from sibling tools like job_tool by focusing on detail retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance on resolving job_id from user references (numeric, company, role) and when to use (after search results). Also specifies output format and follow-up hints.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
job_toolADestructiveInspect
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 = []) + 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 '), 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.
| Name | Required | Description | Default |
|---|---|---|---|
| action | No | Optional; omitted = search. refine = after results/feedback; save = bookmark a job for the signed-in user. | |
| save_job | No | Save args. Required: job_id (from job_cards[].card.id in a prior search result). Optional: job_title, company, job_url. | |
| parameters | No | ||
| search_jobs | No | 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 | No | 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. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds significant context beyond annotations, such as OAuth requirements for save, the refine protocol with three modes, output formatting rules, and common pitfalls like using real Job Id strings. It could be slightly more explicit about the destructive nature of save and refine, but overall it is transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is thorough but quite long; it includes many procedural details and examples. While every sentence is necessary, it could be structured more concisely or broken into sections for easier digestion. The front-loading is decent, but overall length affects conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of the tool (multiple actions, nested objects, no output schema), the description is extremely complete. It covers all actions, refine modes, output behavior, required follow-up, fallback logic, and interaction with sibling tools. No significant gaps are present.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
While the input schema provides parameter descriptions (80% coverage), the description adds substantial behavioral and contextual meaning, such as the distinction between indexed snapshot and legacy DB match, the refine mode details, and the requirement to pass exclude_ids and session_id. This goes well beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: searching jobs across 90+ countries with multiple filters. It also distinguishes between search, refine, and save actions, and explicitly differentiates from job_detail_tool for detail requests.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidelines on when to use each action (search, refine, save) and when not to (e.g., for details, call job_detail_tool). It also details the three refine modes and the protocol for each, leaving no ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
tailor_resume_toolADestructiveInspect
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:, 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: '. 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.
| Name | Required | Description | Default |
|---|---|---|---|
| action | No | 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 | No | 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 | No | ||
| job_title | No | ||
| parameters | No | ||
| session_id | No | ||
| user_email | No | ||
| resume_data | No | 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. | |
| resume_text | No | User's resume content (plain text or JSON Resume as string). Fallback when resume_data is not provided. | |
| user_profile | No | Optional main-site profile object; used as a fallback source for name/title/contact/experience when resume_data and resume_text are both absent. | |
| tailor_resume | No | Optional wrapper containing the same fields above (legacy shape). | |
| resume_content | No | ||
| job_description | No | Full JD text when the user pastes it directly (alternative to job_id). | |
| tailored_resume | No | 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. | |
| customization_level | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate destructiveHint=true, but description adds important context: STEP 2 saves PDF to dashboard requiring sign-in, and it warns against fabricating experience. Could add more about reversibility, but sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is lengthy but well-structured with numbered steps and clear sections. Every sentence adds value, though could be slightly more concise without losing information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given complexity (15 parameters, no output schema), the description covers the two-step process, parameter usage, and constraints. Complete enough for an AI agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is low (53%), but description compensates by explaining action enum, job_id resolution, preferred resume_data shape, and tailored_resume for step 2. Provides meaning beyond schema for several parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it tailors resumes to specific jobs with two distinct steps. It differentiates from sibling tools by explicitly mentioning not to use for general improvement (use resume_tool instead).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit when-to-use (specific job) and when-not-to-use (general improvement, directing to resume_tool). Also includes alternative tool names and detailed job_id resolution rules.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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