Excel MCP Server vs Hirejack MCP

get_job

Fetch one job posting by domain + jobId, or by HireJack detail URL. Returns title, company, location, salary, skills, seniority, posted date, visa/remote/education metadata, and (when available) the AI summary. Use after `search_jobs` when the user picks a specific role to discuss, or when the user pastes a HireJack job URL. Not for browsing or filtering postings (`search_jobs`) or personal fit scoring (`match_job`).

get_company_profile

Fetch HireJack's full hiring profile for a single company by domain. Returns: tech stack (top skills with counts), role distribution, seniority breakdown, location breakdown, hiring trend (% MoM), salary medians where disclosed, and an AI-generated hiring brief. Use this for queries like 'what is Stripe hiring?', 'what tech does Anthropic use?', or before comparing companies. Not for historical trends (`get_company_history`, Analyst) or the user's personal fit (`company_fit`).

search_companies

List HireJack's tracked companies with hiring volume. Filter by name/domain (`q`) or industry. Returns slim records with domain, name, industry, total open jobs, and engineering job count. Use this to find companies in a sector ('which fintech companies are hiring?') or to resolve a company name to its domain before calling get_company_profile. Not for deep per-company data (`get_company_profile`) or multi-axis segmentation by trend/skill/job-count (`find_companies`, Analyst).

get_market_pulse

Get HireJack's market-wide snapshot: total open tech jobs, tracked companies, remote share, top skills by demand, top companies by hiring volume, and trending skills (week-over-week movers). Use for high-level market questions like 'how is the tech job market right now?' or 'what skills are trending?'. Not for history or time-series (`get_market_history`, Analyst) or job-level search (`search_jobs`).

match_job

Score how well the authenticated user matches a specific job. Pro tier. Returns matchPct (0-100, the same score the website shows for this job), requirementsPct (how much of this posting's own requirement list the user meets), a dimension breakdown (skills, role, seniority, location, remote — plus experience when both the user's resume-derived years and the job's required years are known, and semantic when both the user's profile and the job carry embeddings), matched/missing/bonus skill lists, an experienceFit comparison, ATS-specific resume tips for the company's ATS, and a priorityScore that factors hiring velocity. Use for queries like 'how well do I match this job?' or 'should I apply?'. Not for company-level fit (`company_fit`) or discovering new jobs (`recommendations`).

company_fit

Score how well the authenticated user fits a company. Pro tier. Returns fitScore (0-100), 5-dimension breakdown (tech stack 40%, roles 25%, seniority 15%, location 10%, hiring trend 10%), matching skills, skill gaps, role alignment, and a hiring-momentum signal. Use for 'is Anthropic a good fit for me?' or 'compare these companies for me'. Not for scoring a specific job posting — use `match_job` for that.

recommendations

Get top job recommendations for the authenticated user, scored against their profile (skills, desired roles, seniority, location, remote preference) and hard-filtered by their remote/US-only/minimum-salary preferences. Pro tier. Returns jobs ranked by composite match score with per-job match details (default 10, `limit` up to 50). Use for 'show me jobs that match me' or 'what should I apply to this week?'. Not for unpersonalized filter searches (`search_jobs`) or scoring one known job (`match_job`).

salary_benchmark

Compare a salary against the live HireJack market for a role family + seniority slice. Pro tier. Returns precomputed P10/P25/P50/P75/P90 percentiles, the user's percentile ranking (when `salary` is supplied), and a career-ladder progression of medians by seniority. Use for 'am I paid well as a senior backend engineer?' or 'what should I ask for?'. Not for a specific job's posted range (`get_job`) or market-wide comp stats (`get_market_pulse`).

skill_gap

Analyze the gap between the authenticated user's current skills and what their desired roles require in the live market. Pro tier. Returns matchPct (0-100), the list of skills the user already has that map to their target roles, and the list of missing skills ranked by impact (how often the skill appears in target-role postings). Use for 'what should I learn next?' or 'how close am I to senior PM roles?'. Not for simulating the payoff of skills the user might LEARN — use `skill_impact` for that.

market_position

Compute the authenticated user's market position score (0-100). Pro tier. Weighted: 50% skill demand against current market, 20% seniority fit, 15% remote availability for the user's preference, 15% skill breadth. Returns score, matchingJobs count, salaryMedian for matching roles, per-skill demand breakdown, and the top companies whose tech stacks align best with the user's skills. Use for queries like 'how do I stack up against the market?' or 'what's my market position right now?'. Not for job-specific (`match_job`) or company-specific (`company_fit`) scores.

skill_impact

For each skill the authenticated user does NOT have, simulate adding it and compute the market impact. Pro tier. Returns a ranked list of skills with: newJobsUnlocked (jobs that previously didn't match but would after learning the skill), boostedJobs (existing matches that would score higher), companiesNeedingIt, relevantRoles, and a composite impactScore. Also returns profileCompleteness so the caller can suggest filling missing profile fields. Use for queries like 'what should I learn next?' or 'which skill would unlock the most jobs for me?'. Not for auditing the user's CURRENT skills against their target roles — use `skill_gap` for that.