MCP server

GigHQ.ai Official

ai.gighq/gighq

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

Job-search tracker for AI agents: applications, stale follow-ups, skills gaps, H-1B lookup.

Website Source

At a glance

Type

MCP server

Publisher

gighq.ai (verified by the MCP Registry)

Status

Working at last check

Tools

34

Token cost

About 11,494 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http gighq https://mcp.gighq.ai/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.gighq]
url = "https://mcp.gighq.ai/mcp"

Gemini CLI

{"mcpServers":{"gighq":{"httpUrl":"https://mcp.gighq.ai/mcp"}}}

Cursor

{"mcpServers":{"gighq":{"url":"https://mcp.gighq.ai/mcp"}}}

VS Code

{"servers":{"gighq":{"type":"http","url":"https://mcp.gighq.ai/mcp"}}}

Antigravity

agy mcp add gighq https://mcp.gighq.ai/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"gighq","server_url":"https://mcp.gighq.ai/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"gighq","server_url":"https://mcp.gighq.ai/mcp"}

Tools

• add_career_entry ~1058 tokens
  Saves a new career journal entry to the user's GigHQ profile. Use this to document professional achievements, projects, skills acquired, or reflections tied to a specific role. Entry types: 1 = achievement — a concrete win, result, or impact (default) 2 = project — a specific initiative the use
• add_contact_interaction ~454 tokens
  Logs a touchpoint with one of the user's job-search contacts — a call, a LinkedIn message, a coffee chat, a referral conversation, etc. The entry appears in the contact's interaction timeline in GigHQ. **type is required.** Infer the best-fit type from what happened: • text_message — SMS/text •
• add_job_application_note ~503 tokens
  Adds a note to a job application's timeline. Use this to log any touchpoint or interaction — a call, a LinkedIn message, a referral conversation, a networking event, etc. **category is required.** Infer the best-fit category from the note content before calling. Use this mapping: • text_message —
• check_stale_applications ~158 tokens
  Returns job applications that haven't had a status update in a configurable number of days, along with a suggested follow-up action for each. Useful for surfacing applications the user may have forgotten about.
• create_job_application ~447 tokens
  Creates a new job application entry in GigHQ, or reuses an existing active one for the same company+job if the backend finds one — check `alreadyExist` in the response. Calling find_job_application first is still recommended when you want to inspect a possible match before deciding. New application
• delete_career_entry Deletes data · ~146 tokens
  Permanently deletes a career journal entry by UUID. This action cannot be undone — confirm with the user before calling. Use list_career_entries to find the UUID if you don't have it.
• delete_job_application_note Deletes data · ~193 tokens
  Permanently deletes a note from a job application's timeline. This action cannot be undone — confirm with the user before calling. Use list_job_application_notes to find the note UUID if you don't have it.
• find_job_application ~369 tokens
  Checks whether the user already has a saved job application for a SPECIFIC posting — call this before create_job_application to avoid creating a duplicate. Matches BOTH company name and job title (case-insensitive substring on each), so it can tell two different roles at the same company apart. sear
• generate_career_compass ~406 tokens
  Runs GigHQ's CareerCompass AI pipeline against the user's resume data to generate 3 candidate professional objective statements, a positioning statement, categorized skills, and a suggested target market (industries, company sizes, geographic locations). This is an EXPENSIVE, one-time 'career strate
• get_career_compass ~122 tokens
  Retrieves the user's saved CareerCompass data: professional objectives, positioning statement, categorized skills (technical/soft/management/other), and target market (industries, company size, geographic location). Cheap read — prefer this over generate_career_compass whenever the user just wants t
• get_career_journal_prompts ~300 tokens
  Returns a set of context-aware reflection questions to guide a career journaling session. Use this at the start of any journaling conversation to orient the discussion. The tool reads the user's GigHQ experience records and existing career entries to tailor the questions — surfacing the most releva
• get_company_details ~348 tokens
  Get detailed information about a company (applicants by status, Community Signal, H1B sponsorship status, employee count, website, etc.) using its UUID. The metrics window defaults to the last 30 days. Optional startDate and endDate parameters support other date ranges. Returns GigHQ's recorded com
• get_contact ~197 tokens
  Returns full details for one of the user's job-search contacts: profile info, outreach status, linked job applications, and the complete interaction history (calls, LinkedIn messages, coffee chats, etc.). Use this before drafting outreach or follow-up messages so the message reflects what has alrea
• get_job_application ~203 tokens
  Fetches the full details of a single job application by its UUID — status history, correspondence dates, job description, and related metadata. Do NOT use this tool when the user asks about notes ("do I have any notes", "show me my notes", "list notes"). Use list_job_application_notes for that. Ti
• get_job_details ~300 tokens
  Get detailed community metrics for a specific job listing (applicants by status, average days per status, Community Signal) using its UUID. The metrics window defaults to the last 30 days. When the user requests a longer period — e.g. "last 6 months", "last year", "since January" — compute startDat
• get_job_fit_evidence ~315 tokens
  Retrieves a saved GigHQ job application, candidate profile data, and the most relevant Career Journal evidence for assessing role fit. Career evidence is ranked by job keywords, competency coverage, evidence quality, recency, and diversity; the default is 20 entries, grouped by competency bucket (ca
• get_recent_applications ~123 tokens
  Retrieves a summary of the user's 10 most recent job applications, including status, company name, and correspondence dates. `totalCount` in the response reflects the full number of applications, not just the 10 returned — mention it if it's larger than the list shown, and point the user to search_j
• list_career_entries ~368 tokens
  Returns the user's career journal entries from GigHQ, newest first. Supports optional filtering by entry type and date range. Use this tool to: - Review what the user has already documented before starting a journaling session - Find entries to update or refine - Identify gaps (roles with few
• list_experiences ~162 tokens
  Returns the user's work experience records with their integer IDs. Use this tool BEFORE calling `add_career_entry` or `update_career_entry` to find the correct `experience_id` for the role the entry belongs to. Present the role list to the user and confirm which one they mean before saving. This i
• list_job_application_notes ~197 tokens
  Use this tool when the user asks about notes on a job application — "do I have any notes", "show me my notes", "what notes do I have", etc. Returns all user-authored notes for a job application ordered by date, including UUID, body text, category, and timestamp. Use the returned UUIDs in follow-up u
• lookup_h1b_sponsorship ~144 tokens
  Looks up a company by name and returns GigHQ's recorded H1B sponsorship and concurrent H1B flags. Returns unknown when a flag is unavailable. These results describe the data recorded in GigHQ.
• prepare_for_interview ~248 tokens
  Assembles everything needed to prepare for an interview on a saved GigHQ job application: the job and company context, the candidate's full profile, their Career Journal grouped by role (for STAR/CAR story material), which evidence best matches the role (to emphasize) and which competencies are weak
• recommend_target_roles ~334 tokens
  Assembles the user's profile, experience, career journal evidence (with a broad competency rollup), and saved CareerCompass data (if any) for recommending role titles to target. This is the inverse of get_job_fit_evidence: instead of evaluating fit for one specific job, it starts from the candidate'
• save_career_compass ~817 tokens
  Persists CareerCompass data to the user's GigHQ profile. This REPLACES the full saved record — any array field you omit (technical_skills, soft_skills, management_skills, target_industries, company_size, geographic_location, other_skills) is saved as empty, not left unchanged. Call get_career_compas
• search_companies ~104 tokens
  Search for companies by name to retrieve their unique identifiers (UUIDs).
• search_contacts ~360 tokens
  Searches the user's job-search contacts in GigHQ — recruiters, hiring managers, referrals, and other people they are networking with. Use this tool to: - Find a contact before drafting an outreach or follow-up message - See who the user already knows at a company before an interview or applicat
• search_job_applications ~514 tokens
  Search the user's job applications by company name, status, or both. Use this when the user asks about a specific company's application, wants to find a particular application to view or update its status, or wants to know how many applications are in a given status (e.g. "how many applications are
• toggle_job_application_interest ~452 tokens
  Use this tool when the user says "not interested", "no longer interested", "withdraw interest", "I'm out", "remove from pipeline", or any equivalent phrase. This is the ONLY correct tool for withdrawing interest — do not use update_job_application_status as a proxy. Deactivates an active applicatio
• update_career_entry ~445 tokens
  Updates an existing career journal entry. Only the fields provided will be changed — all other fields remain as-is. Use this to refine descriptions, add missing numbers to the metrics field, correct the entry date, or re-type an entry. This is the right tool to backfill quantification on entries sav
• update_contact_status ~214 tokens
  Updates a contact's outreach status in GigHQ. A status-change entry is automatically logged in the contact's interaction history. Status progression: not_contacted → reached_out → responded → in_conversation → connected. Use 'cold' when the thread has gone quiet. Find the contact's UUID with `searc
• update_job_application ~358 tokens
  Enriches or corrects an existing job application's saved posting details: source URL and/or job description. Use this when the user finds the JD later, wants to add the posting link, or wants to correct the saved description. Do NOT use this for status changes — use update_job_application_status. D
• update_job_application_note ~508 tokens
  Updates the body or category of an existing job application note. Both body and category are required — the backend replaces the full note on update. If the user only mentions updating the body and doesn't specify a category, infer the best-fit category from the note content using this mapping: •
• update_job_application_status ~528 tokens
  STOP — if the user says "not interested", "no longer interested", "withdraw interest", "remove from pipeline", or any equivalent: do NOT call this tool. Call toggle_job_application_interest instead. There is no "not interested" status here; attempting to use REJECTED or any other status as a proxy i
• weekly_job_search_summary ~99 tokens
  Generates a weekly job search health summary. Aggregates up to 30 active applications and surfaces status distribution, conversion rates, longest-open applications, pipeline clustering insights, and a suggested focus area for the coming week.

Browse

• Website monitoring
• Email
• SMS and text messages
• Social media

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