AI HR Management Toolkit MCP server

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• analyze_resume_comprehensivewrite action

  Run a comprehensive algorithmic analysis on a resume in a single call. Accepts raw text or a file (base64-encoded PDF/DOCX/TXT/MD or URL). Runs the full 5-node pipeline (Ingestion \u2192 Sanitization \u2192 Tokenization \u2192 Classification \u2192 Serialization) and returns: pipeline confidence scores, classified entities by type, categorized skills with proficiency estimates, structured experien

• assess_candidate

  Assess a resume against recruiter-defined criteria. Supports 8 criteria axes: Education, Experience, Skills, Certifications, Knowledge Stack, Competitions, Thresholds, and Job Qualification. Returns per-axis scores, weighted overall score, and a pass/review/reject decision.

• ats-pipeline-summary

  Generate a concise ATS pipeline summary showing candidate counts by stage and key hiring metrics.

• ats_dashboard_stats

  Generate comprehensive ATS dashboard statistics and hiring health report. Provide the full ATS state (candidates, jobs, interviews, offers). Returns key metrics, pipeline health, hiring velocity, and actionable insights.

• ats_generate_demo_data

  Generate a full set of realistic demo data for the ATS (Applicant Tracking System). Returns a complete ATSState with sample jobs, candidates at various pipeline stages, scheduled interviews, and offers. Useful for testing, demonstrations, or populating an empty ATS instance.

• ats_interview_feedback

  Manage interview feedback in the ATS. Actions: submit (add feedback to completed interview), get (retrieve feedback for an interview), update (modify existing feedback), list_pending (interviews awaiting feedback), list_completed (interviews with feedback, optionally filtered by candidate), analyze (aggregate feedback for a candidate across all interviews), summary (hiring signal summary for a job

• ats_manage_candidates

  Manage candidates in the ATS pipeline. Supports add, update, delete, move between stages, bulk move, and list/filter operations. Pass the current candidates record and an action to perform. Returns the updated candidates and a summary.

• ats_manage_jobs

  Manage job postings in the ATS. Actions: create (new job posting), update (edit fields), delete, list (with optional status/department filter), search (by keyword in title/description), close, reopen. Pass current jobs record and an action.

• ats_manage_notes

  Manage candidate notes in the ATS. Actions: add (create note on candidate), update (edit note content), list (get all notes for a candidate), delete (remove a note), search (find notes by keyword across one or all candidates), bulk_add (add notes to multiple candidates). Pass the current candidates record.

• ats_manage_offers

  Manage offers in the ATS. Actions: create (validate & structure), update_status (draft\u2192pending-approval\u2192approved\u2192sent\u2192accepted/declined), delete (remove offer), list (all offers with optional filters), compare (side-by-side offer comparison), validate (check for issues). Returns structured offer data.

• ats_pipeline_analytics

  Analyze the ATS hiring pipeline. Given candidates and optional pipeline stage config, returns stage distribution, conversion rates between stages, average time-in-stage, and bottleneck identification. Useful for hiring funnel analysis.

• ats_schedule_interview

  Full CRUD for interviews. Actions: create (validate & schedule with conflict detection), update (reschedule/modify), delete (permanent removal), list (filter by candidateId/jobId/status), get (single interview by id). Pass existing interviews record for conflict checks.

• ats_search

  Global search across the ATS. Actions: search (keyword search across candidates, jobs, interviews, offers \u2014 scoped optionally), filter_candidates (structured filter by stage/job/tags/score), get_entity (retrieve a single entity by type+id). Pass the full ATS state.

• batch_parse_resumes

  Parse multiple resume files at once and run the full algorithmic pipeline on each. Returns raw text, pipeline analysis, keywords, entities, and confidence scores for each file. The LLM client should interpret and structure the results.

• classify_entitieswrite action

  Run Named Entity Recognition on resume text. Extracts 12 entity types (PERSON, ORGANIZATION, DATE, SKILL, LOCATION, EMAIL, PHONE, URL, EDUCATION_DEGREE, CERTIFICATION, JOB_TITLE, METRIC) with per-entity confidence scores (0-1) and domain-aware disambiguation (e.g., Java the language vs Java the island). Returns classified entities grouped by type, confidence statistics, and ambiguity analysis. 100

• compare-candidates

  Parse and compare multiple candidates side-by-side for a position, ranking them by fit score.

• compute_similarity

  Compare a resume against a job description using cosine similarity, Jaccard index, TF-IDF overlap, and skill matching. Returns a computed fit tier (strong/moderate/weak/poor), per-skill gap analysis with categories, and actionable gap recommendations. No AI calls \u2014 all scoring is algorithmic.

• content

  Resume text, file content (base64), or URL to fetch

• criteria

  JSON array of evaluation criteria (e.g. required skills, experience years)

• detect_patterns

  Detect and structure date ranges, metrics, sections, and work experience from resume text. Returns structured experience entries with titles, organizations, technologies, and achievements extracted algorithmically using NER, date patterns, and TF-IDF. Also detects career progression trajectory. No AI calls.

• export_results

  Export parsed resume results to a specified format (JSON or CSV text). Accepts an array of structured resume results and returns formatted output.

• extract_experience

  Extract work experience from resume text using algorithmic date/metrics extraction combined with AI structuring. Uses pattern matching for metrics, date ranges, and section detection before AI refinement.

• extract_experience_structured

  Extract and structure work experience from resume text using algorithmic analysis only (no AI). Uses date range detection, metric extraction, NER entity classification (job titles, organizations, skills), and heuristic block splitting to produce structured experience entries. Each entry includes detected title, organization, date range, duration estimate, associated metrics/achievements, and techn

• extract_keywords

  Extract keywords from resume text using TF-IDF analysis, then overlay entity classification (NER) and skill categorization. Returns ranked keywords enriched with entity type, skill category, and confidence scores. No AI calls \u2014 all computation is algorithmic.

• extract_skills

  Extract skills from resume text using algorithmic keyword analysis combined with AI categorization. Uses TF-IDF keyword extraction and section detection before AI refinement.

• extract_skills_structured

  Extract and categorize skills from resume text using algorithmic analysis only (no AI). Combines NER entity classification (with disambiguation), TF-IDF keyword ranking, section detection, and frequency-based proficiency estimation. Returns skills organized by 13 categories (programming_language, framework, database, devops_cloud, ml_ai, design, methodology, tool, soft_skill, testing, security, we

• inspect_pipelinewrite action

  Run the full 5-node atomic deconstruction pipeline (Ingestion \u2192 Sanitization \u2192 Tokenization \u2192 Classification \u2192 Serialization) on resume text. Returns stage-by-stage metrics, confidence scores, entity classification with disambiguation, data quality assessment, and assumption audit. Use this to understand HOW the parser processes a resume and WHERE confidence is low.

• jobDescription

  Job description text to score candidates against

• jobId

  Optional job ID to filter pipeline stats to a single opening

• manage_candidates

  Manage and analyze candidates: rank by fit, filter by criteria, recommend pipeline stage changes, compare candidates side-by-side, or get a summary. Operates on candidate data passed in \u2014 does not access browser storage.

• match_job

  Match a resume against a job description using cosine similarity, Jaccard index, TF-IDF keyword analysis, and AI assessment. Returns algorithmic scores combined with AI-refined compatibility analysis.

• parse-and-assess-candidate

  Parse a resume and assess the candidate's fit for a specific role using criteria-based scoring.

• parse_resume

  Parse a resume file (PDF, DOCX, TXT, MD) or URL and extract text with algorithmic pre-analysis including keyword extraction, metrics detection, section identification, and experience estimation.

• resumes

  Newline-separated list of resume URLs or base64-encoded file contents

• role

  Job title or role the candidate is being assessed for

• send_emailwrite action

  Send parsed resume results via email using SMTP. Requires SMTP configuration (host, port, user, pass) and recipient email. Sends an HTML summary of all results.

• summarize_resume

  Generate a recruiter-focused summary using algorithmic pre-analysis (TF-IDF keywords, metrics extraction, section detection, experience estimation) combined with AI synthesis.

• Code scan89 source files scanned25/25
• –Live reliabilityno gateway calls yet and no remote to proben/a
• –Tool poisoningtools not inspected (local package is not executed); not countedn/a
• Auth qualitylocal package, no credentials required12/15
• Maintenancelast push 175 days ago8/15
• Maintainer identityregistry namespace matches repository owner; GitHub account older than a year8/10

Safety score

82/100

higher than 63% of 48,255 servers

Tools

37

the typical server has 7

Tools that change things

11%

typical: 0%. Fewer write tools means less an agent can break

GitHub stars

1

more than 65% of 28,688 servers

AI HR Management Toolkit: common questions

Is AI HR Management Toolkit MCP server safe?

Mostly: it is graded B (82/100). Read the AI HR Management Toolkit safety report

How do I install AI HR Management Toolkit?

It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.

Does AI HR Management Toolkit need an API key?

Not as far as the registry entry and our scan can tell: no credentials are declared or required.

Is AI HR Management Toolkit maintained?

The last commit was 213 days ago (2026-04-05). The latest release is v3.0.5.

How does AI HR Management Toolkit compare with other MCP servers?

safety score 82/100 (higher than 63% of 48,255 servers); tools 37 (the typical server has 7); tools that change things 11% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.