# HrFlow.ai MCP > HrFlow.ai is an advanced talent acquisition MCP that connects your agent to specialized hiring intelligence. It handles everything from ingesting unstructured data like resumes to running complex matching algorithms, allowing you to search candidates and jobs using semantic filters. You can automatically score applicants against specific job requirements or analyze a candidate's full career path with natural language queries. ## Overview - **Category:** ai-frontier - **Price:** Free - **Tags:** resume-parsing, talent-acquisition, semantic-search, candidate-scoring, hiring-automation ## Description Recruiting used to be slow, relying on keyword searches and manual review of PDFs. This MCP changes that. It lets your agent ingest raw resumes into structured profiles instantly, turning messy documents into usable data points. You can then search across large candidate databases or job listings using semantic filters, meaning you don't just find people with the right keywords; you find people who *do* the right work. Need to know if a candidate is good for a specific role? The MCP scores profiles against job descriptions automatically. If you want deeper insight into a single person, it can even analyze their entire career trajectory and answer questions about them in plain language. Connecting this MCP through Vinkius gives your agent access to industry-leading tools designed specifically to automate the most tedious parts of hiring. It’s built to handle the full lifecycle: from initial application parsing all the way through candidate scoring and job matching. ## Tools ### ask_profile Answers a natural language question about a specific candidate's stored profile details. ### list_boards Retrieves a list of all job boards currently managed within the HrFlow system. ### list_jobs Shows all current job postings that are stored in your configured HrFlow boards. ### list_profiles Retrieves a list of every candidate profile stored within the HrFlow system. ### list_sources Provides a comprehensive list detailing all sources where candidate profiles were originally found. ### parse_profile Takes an uploaded resume file and converts its content into a structured, searchable digital profile. ### score_profiles Compares candidate profiles against specific job requirements and generates a quantifiable score of fit. ### search_jobs Finds relevant open positions using semantic filters that understand context, not just keywords. ### search_profiles Searches the entire candidate database using advanced semantic filters to find profile matches quickly. ### unfold_profile Analyzes a candidate's career history and generates a detailed narrative of their professional path. ## Prompt Examples **Prompt:** ``` List the last 5 profiles added to my HrFlow source. ``` **Response:** ``` I'll fetch the latest profiles for you. ``` **Prompt:** ``` Ask profile key 'abc-123' if they have experience with Kubernetes. ``` **Response:** ``` I'll analyze the candidate's profile and answer your question. ``` **Prompt:** ``` Find jobs that match candidate 'john-doe-key'. ``` **Response:** ``` I'll use semantic search to find matching jobs for this candidate. ``` ## Capabilities ### Analyze Candidate Profiles Ask natural language questions about a specific candidate's stored profile data. ### Process Resumes into Data Takes an unstructured resume file and converts it into a clean, structured digital profile. ### Match Candidates to Jobs Scores multiple candidate profiles against the requirements of specific job descriptions. ### Search Job Markets Searches for open jobs using advanced semantic filters, finding matches beyond simple keyword hits. ### Deep Dive on Career History Analyzes a profile to map and describe the candidate's full career path and progression. ## Use Cases ### Filtering out the noise after high volume applications A Recruiter receives 500 resumes. Instead of reading all 500, they connect HrFlow.ai and use `score_profiles` against a master job description. The agent returns only the top 20 candidates who exceed an 85% fit score, letting them focus their time immediately. ### Determining if a candidate's background is relevant A Hiring Manager receives a profile for a senior developer. They use `unfold_profile` to map the career path. The agent doesn't just list jobs; it describes the trajectory, helping the manager see potential skill gaps or unexpected pivots. ### Finding niche expertise in a massive database The HR Ops team needs someone with specific experience (e.g., 'Quantum Computing'). They use `search_profiles` with semantic filters, bypassing keyword limitations and finding candidates who mention related concepts. ### Getting immediate answers on a candidate's skills A Recruiter finds an interesting profile but needs to confirm if the person knows 'Kubernetes'. They use `ask_profile` with natural language, and the agent analyzes the data and provides a direct answer in seconds. ## Benefits - Stop guessing if a candidate is right for the job. Use `score_profiles` to automatically compare profiles against specific job descriptions, giving you an immediate fit percentage. - Never manually type out data from resumes again. The `parse_profile` tool ingests any resume file and spits out clean, structured data ready for matching and searching. - Find talent faster by using semantic search. Instead of just listing skills, use `search_profiles` to find candidates who demonstrate the *behavior* you need. - Get instant career context. The `unfold_profile` tool builds a narrative map of a candidate's entire professional journey, perfect for executive roles. - Streamline your pipeline by automatically querying profiles. Use `ask_profile` to answer specific, complex questions about a candidate without needing full visibility into their history. ## How It Works The bottom line is, instead of spending hours on manual review and copy-pasting, your agent gets instant, structured intelligence about talent readiness. 1. First, use `parse_profile` to upload raw resumes or documents. This turns unstructured text into structured, machine-readable candidate profiles. 2. Next, your agent can select the necessary tools—like `search_profiles` or `score_profiles`—to run targeted queries against those newly structured records. 3. Finally, you receive actionable data: a list of matching jobs, a score indicating fit, or specific answers to your natural language questions. ## Frequently Asked Questions **How do I use HrFlow.ai to find profiles with specific technical skills?** You should use `search_profiles` with semantic filters. This allows you to search for concepts, like 'experience building microservices,' rather than just searching for the literal word 'microservice.' **Does HrFlow.ai handle non-English resumes?** The `parse_profile` tool is designed to ingest and structure various document types, including multiple languages. While best results come from clear formatting, it handles the conversion into structured data. **Can HrFlow.ai tell me why a candidate scored low?** Yes, after running `score_profiles`, your agent can be prompted to analyze the mismatch. It helps explain which specific job requirements were not met by the profile data. **What is the difference between list_jobs and search_jobs?** `list_jobs` shows you every job that is currently stored in your HrFlow boards. `search_jobs`, however, lets you actively find new or relevant jobs using semantic filters based on criteria. **Is HrFlow.ai only for US-based candidates?** No. The MCP is designed to handle global talent acquisition pipelines. Its tools are built around parsing and comparing diverse professional histories, regardless of geography.