Create a new job hunt to start tracking and applying to jobs. A job hunt defines what jobs you want to find based on titles, locations, skills, salary, etc. You need at least one job hunt to use match_jobs or add_job_to_applications.
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306,631 tools. Last updated 2026-07-25 16:52
"A server for finding and applying to jobs based on a resume" matching MCP tools:
- MONITORING: Fetch Terraform deployment logs with pagination Fetches logs from a running or completed Terraform deployment job. For **completed jobs**: uses REST endpoint for instant retrieval (supports `tail` for server-side filtering). For **running jobs**: streams via SSE with timeout-based pagination. **PAGINATION** (running jobs only): Use `last_event_id` from the response to fetch more: 1. First call: `tflogs(session_id='...')` → get logs + `last_event_id` 2. Next call: `tflogs(session_id='...', last_event_id='...')` → get NEW logs only 3. Repeat until `complete: true` in response **RESPONSE FIELDS**: - `logs`: Array of log messages collected - `last_event_id`: Pass this back to get more logs (pagination cursor, SSE only) - `complete`: true if job finished, false if more logs may be available - `total_logs`: total log entries before tail truncation REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: job_id to target a specific deployment (use tfruns to discover IDs), timeout (default 50s, max 55s), last_event_id (for pagination), tail (return only last N entries) ⚠️ CONTEXT WARNING: Deploy logs can be hundreds of lines. Use tail: 50 for completed jobs to avoid blowing up the context window.Connector
- Download a completed Future Video Studio final render URL to a local file. Use this only after fvs_get_render_status or fvs_get_paid_render_status returns a final_video_url for a completed render. The tool performs an unauthenticated HTTPS GET to that signed URL and writes the response bytes to output_path on the MCP server's local filesystem. It does not call the FVS Agent API, spend wallet credits, require FVS_AGENT_API_KEY, cancel jobs, or modify remote render state. Side effects and constraints: output_path is a local filesystem path for the MCP server process, parent directories are created, existing files are not replaced unless overwrite is true, and large videos may take minutes to download. The request timeout is 600 seconds. Use a fresh status check to refresh expired signed URLs, and do not pass arbitrary or untrusted URLs.Connector
- List applications across all accessible jobs. Supports filtering by candidate, job, stage, status, AI score range, and date ranges. Use for pipeline analytics, sync jobs, and ATS dashboards. Avoid include=candidate or include=cv.text on large pages (each embeds heavy nested data); if the response exceeds the budget the tool returns isError:true with error_code=response_too_large and retry hints. Each application embeds its current `stage` (IdName) directly in the response — this is sufficient for rendering kanban/pipeline views; you DO NOT need to call hires_get_job to fetch workflow_stages separately when rendering a pipeline.Connector
- Generate a one-click signed PriceTik checkout URL for a hotel stay. Stay-based form (preferred for agents): pass providerPropertyId + checkIn/checkOut + guests/rooms (+ optional roomCode) and the server resolves the best live rate itself. Or pass a rateKey from pricetik_hotel_price_check directly; it auto-refreshes near expiry or when rateType is "RECHECK". URLs are pricetik.com/go/ affiliate redirects — pass to the user's browser unchanged, never fetch server-side. On 410 RateKeyExpired, re-call pricetik_hotel_price_check for a fresh rateKey. On 422 PackagingDenied, the rate is package-only and can't be booked hotel-only. currentBest surfaces nightlyRate, totalStayCost, currency, paymentType, rateType, freeCancellation only — no margin fields. When a stored loyalty program matches the property's chain, the response carries loyaltyApplied ({ program, memberId }) and the URL is member-stamped — tell the user which program linked.Connector
- 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.Connector
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- Alicense-qualityBmaintenanceMCP server enabling Claude Code to capture screenshots of Windows applications through WSL2, allowing AI to diagnose UI errors and verify layouts.Last updatedMIT
- Alicense-qualityDmaintenanceA-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.Last updated34MIT
Matching MCP Connectors
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连板网A股复盘数据: 连板天梯/题材/情绪周期/龙虎榜游资/个股涨停史 (A-share daily review, free read-only)
- 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.Connector
- Render an asset's reference image in the channel's art style — the visual anchor that keeps a character/environment looking identical across every shot. EVERY character, environment, and object asset needs one before generate_voiceover (the server enforces this; fire the jobs for all assets, then one await_jobs). Async — the job writes the image onto the asset row: await_jobs(project_id), then list_assets and view_image the file_path to check likeness.Connector
- Check the latest Axint package and optionally apply the upgrade while preserving the current agent thread. Use: call when axint.status shows a stale server; not for app dependency upgrades. Inputs: apply defaults false; targetVersion selects the install, while reinstallXcode and writeReport matter only when applying. Effects: destructive when apply=true: can run package installs, refresh Xcode wiring, and write .axint/upgrade; may use npm.Connector
- 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.Connector
- Calculate recommended RAM and ARC sizing for a ZFS storage pool based on workload type, pool size, deduplication status, and L2ARC cache size. Computes minimum and recommended RAM in gigabytes, ARC target size, and dedup table overhead. Accounts for workload-specific IO patterns: NAS (sequential, 1GB/TB), database (random, 2GB/TB), virtualization (mixed, 1.5GB/TB). Deduplication adds approximately 5GB per TB for the DDT. L2ARC index requires 1GB RAM per 10GB of L2ARC. Essential for TrueNAS, FreeNAS, and custom ZFS server builds.Connector
- 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.Connector
- Search fleet tools and servers by natural-language description. Returns ranked matches with brief summaries and the server each tool belongs to. Use scope "servers" to find which server handles a workflow; use the default scope "tools" to find specific tools. Call cyanheads_describe on a result name to get install snippets and the connection URL.Connector
- Submit a public product URL for price tracking. Waits up to ~25s server-side; fast shops return status "completed" with product in one call. Slow jobs return status "running" with job_id — poll get_job_status. On failure, returns a structured error object with fields error.code, error.message, error.http_status, error.retry_recommended, and error.retry_after_seconds.Connector
- Step 2: get the exact payment to RENT a plan for N days (USDC via x402 on Base + card availability). This hosted server holds NO funds and cannot provision — it returns everything needed to pay elsewhere. The `provision` field spells out the three ways to actually get the server. Read it and act on it.Connector
- 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.Connector
- 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.Connector
- THE resume/orientation tool: one call returns every pipeline step's state (script -> scan -> reference_images -> voices -> voiceover -> style_templates -> storyboard -> segment_assets -> scenes -> export), any running jobs, and a next_action telling you exactly what to do next. Call this when picking up an existing project, after any await_jobs, or whenever you're unsure where a video stands — never guess pipeline position. Every step carries a web_url — the page in the Framesail web app where your user can see that step's output. Share it whenever you present a step's results ("review the storyboard here: ...") so they can inspect and edit in the browser; the app and this API act on the same live state.Connector
- Poll this between steps: returns active + recently-finished AI jobs (scope by project_id, or style_id for style analysis), plus per-segment- asset render statuses for projects. A step is done when its jobs reach status=complete (or error, with a user-readable message). NOTE: finished jobs drop out of `jobs` after ~30s — a short list does NOT mean work was lost; judge render batches by `segment_assets` statuses (or get_pipeline_progress), never by counting jobs. Prefer await_jobs over polling this in a tight loop.Connector
- Block (server-side) until the scope has no pending/running jobs, or the timeout passes — use this instead of polling get_workflow_status yourself. Returns {done, jobs}. If done=false the work is still running: just call await_jobs again (a 3-5 minute storyboard takes a few consecutive calls). Keep timeout_seconds <= 50 so the client doesn't time out the tool call.Connector