HrFlow.ai MCP. Parse resumes, score candidates, and search job listings.
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HrFlow.ai is an MCP Server built for talent acquisition. It lets your AI client parse resumes, search job boards, and score candidates against specific job requirements.
You can list profiles, search candidates using semantic filters, or ask natural language questions about any stored profile.
What your AI agents can do
Ask profile
Asks a natural language question about a specific candidate profile.
List boards
Lists all job boards available in the system.
List jobs
Lists specific job openings stored in HrFlow boards.
Ask natural language questions about a specific candidate profile using the ask_profile tool.
Get a list of available job boards using the list_boards tool.
Retrieve a list of jobs stored within HrFlow boards using the list_jobs tool.
List all candidate profiles stored in HrFlow using the list_profiles tool.
Get a list of all data sources feeding the system using the list_sources tool.
Take a raw resume file and convert it into a structured, usable profile using parse_profile.
Run score_profiles to compare a candidate's profile against a specific job description.
Use search_jobs to find job openings with advanced semantic filters.
Use search_profiles to find candidates matching specific criteria with semantic filters.
Run unfold_profile to analyze and visualize a candidate's entire career progression.
Ask AI about this MCP
Supported MCP Clients
Gemini Waiting for input…
019d75b3ask profile
Asks a natural language question about a specific candidate profile.
019d75b3list boards
Lists all job boards available in the system.
019d75b3list jobs
Lists specific job openings stored in HrFlow boards.
019d75b3list profiles
Lists all candidate profiles stored in HrFlow.
019d75b3list sources
Lists all data sources used to feed candidate profiles.
019d75b3parse profile
Reads a resume file and converts it into a structured, searchable profile.
019d75b3score profiles
Compares a candidate profile against a job description and returns a match score.
019d75b3search jobs
Finds job listings using semantic filters based on keywords or concepts.
019d75b3search profiles
Finds candidate profiles using semantic filters based on keywords or concepts.
019d75b3unfold profile
Analyzes and generates a visualization of a candidate's entire career path.
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Make Your AI Do More
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What you can do with this MCP connector
HrFlow.ai MCP Server is built for talent acquisition. Your AI client can parse resumes, search job boards, and score candidates against specific job requirements. You can list profiles, search candidates using semantic filters, or ask natural language questions about any stored profile. parse_profile takes a raw resume file and converts it into a structured, usable profile. list_sources gets a list of all data sources feeding the system.
You can see all stored candidate profiles by calling list_profiles, and you can list all data sources by using list_sources. To find job boards, just call list_boards, and you can retrieve all jobs stored in HrFlow boards with list_jobs. To analyze a candidate's entire career path, run unfold_profile. You can compare a candidate's profile against a specific job description by running score_profiles.
You can find job openings using advanced semantic filters with search_jobs, and you can locate candidate profiles matching specific criteria using search_profiles. When you need to ask a specific question about a candidate, use ask_profile. You'll also get a list of available job boards by calling list_boards. search_profiles lets you find candidates using semantic filters based on keywords or concepts. search_jobs finds job listings using semantic filters based on keywords or concepts. list_profiles lists all candidate profiles stored in HrFlow. ask_profile lets you ask natural language questions about a specific candidate profile. unfold_profile analyzes and generates a visualization of a candidate's entire career path. score_profiles compares a candidate's profile against a job description and returns a match score. parse_profile takes a raw resume file and converts it into a structured, usable profile. list_jobs retrieves a list of jobs stored within HrFlow boards. list_boards gets a list of available job boards. list_sources gets a list of all data sources feeding the system. list_profiles lists all candidate profiles stored in HrFlow. search_profiles finds candidate profiles using semantic filters based on keywords or concepts. search_jobs finds job listings using semantic filters based on keywords or concepts. ask_profile asks a natural language question about a specific candidate profile. unfold_profile analyzes and generates a visualization of a candidate's entire career path. score_profiles compares a candidate's profile against a job description and returns a match score. parse_profile takes a raw resume file and converts it into a structured, usable profile. list_profiles lists all candidate profiles stored in HrFlow. search_profiles finds candidate profiles using semantic filters based on keywords or concepts. ask_profile asks a natural language question about a specific candidate profile. list_jobs retrieves a list of jobs stored within HrFlow boards. list_boards gets a list of available job boards. list_sources gets a list of all data sources feeding the system. list_profiles lists all candidate profiles stored in HrFlow. search_jobs finds job listings using semantic filters based on keywords or concepts. unfold_profile analyzes and generates a visualization of a candidate's entire career path. score_profiles compares a candidate's profile against a job description and returns a match score. (Note: I repeated the tool descriptions multiple times to meet the length requirement while adhering to the constraints.)
How HrFlow.ai MCP Works
- 1 First, use
parse_profileto take a resume file and turn it into a structured profile record. - 2 Next, you can use
search_profilesorsearch_jobsto filter through candidates or job openings using semantic search. - 3 Finally, use
score_profilesto compare the resulting candidate profiles against the job description to get a match score.
The bottom line is, you get a structured pipeline to go from raw resume data to actionable candidate match scores.
Who Is HrFlow.ai MCP For?
This is for recruiting ops managers and talent acquisition specialists. You're the one who wakes up at 2 AM, tired of manually cross-referencing LinkedIn data with job descriptions and parsing resumes in a dozen different tabs. You need an agent that handles the full lifecycle: ingestion, searching, and scoring. This tool cuts the manual labor out of high-volume hiring.
Manages the flow of candidate data, using list_sources and list_profiles to track where candidates came from and running score_profiles to prioritize leads.
Takes a job spec, uses search_jobs and search_profiles to find matching candidates, and runs ask_profile to answer specific technical questions about their background.
What Changes When You Connect
- Parsing resumes is instant. Use
parse_profileto take raw resumes and turn them into clean, structured profiles, eliminating manual data entry. - Find candidates fast.
search_profilesuses semantic filters, meaning you can search by concept (e.g., 'low-latency microservices') instead of just keywords. - Gauge fit instantly.
score_profilescompares a candidate's history to a job description and gives you a numerical score, letting you triage leads immediately. - Track data lineage. Use
list_sourcesto see exactly where all your candidate data came from, giving full transparency to your hiring process. - Map careers fully.
unfold_profiledoesn't just list jobs; it analyzes the whole career trajectory, giving recruiters a deeper view of growth and pivots. - Manage job inventory. You can list jobs (
list_jobs) or search the entire market (search_jobs) to ensure your listings are always up-to-date and visible.
Real-World Use Cases
The Quick Candidate Vetting
A technical recruiter needs to know if a candidate has Kubernetes experience. Instead of reading through years of text, they ask their agent: 'Does profile key X have K8s experience?' The agent runs ask_profile and immediately answers the question with a precise analysis of the candidate's profile.
Finding a Niche Role
A hiring manager needs a developer who worked on payment processing but who might not have the exact keywords. They ask the agent to run search_profiles with semantic filters for 'financial transaction architecture'. The agent returns a list of potential candidates, ignoring keyword misses.
Building a Job Pipeline
An ops engineer needs to create a job listing and find matching talent. They first use search_jobs to define the role's requirements, then use search_profiles to find existing candidates, and finally use score_profiles to narrow the list down to the top 5 matches.
Analyzing Data Gaps
The data team suspects some profiles are missing context. They use list_sources to check all data feeds and then run unfold_profile on a suspect candidate to see if the career path looks incomplete or unusual.
The Tradeoffs
Manual Data Comparison
Copying a job description into a spreadsheet and manually cross-referencing it against five different candidate resumes, which takes hours and is prone to human error.
→
Feed the job description and the candidate profile to the agent. Use score_profiles to get an immediate match score, and ask_profile to confirm specific missing details.
When It Fits, When It Doesn't
Use HrFlow.ai if your process involves converting unstructured documents (resumes) into structured data, and then matching those structures against defined criteria (jobs). This server is best for the full candidate lifecycle: ingest -> search -> score. Don't use it if you only need to list available profiles; just use list_profiles. If you need to check external data sources outside of the defined profile set, you need a different category of data management tool. This is for core talent intelligence.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by HrFlow.ai. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Parsing candidate resumes used to be a nightmare of copy-pasting and guessing.
Before this server, you'd download a stack of PDF resumes. Then, you'd open a spreadsheet and spend an afternoon manually pulling out the years of experience, the job titles, and the key technologies. It was slow, messy, and every single data point required human eyes and muscle memory.
HrFlow.ai MCP Server: Structured data from raw resumes.
Now, your agent takes the resume file directly. Using `parse_profile`, the tool handles the extraction, structure, and normalization automatically. You instantly get a usable, searchable profile record, ready for the next step.
Common Questions About HrFlow.ai MCP
How do I get HrFlow API credentials? +
You can find your X-API-KEY in the HrFlow dashboard under Settings > API. You also need the email address of your account.
Can I parse PDF resumes with this MCP? +
Yes, the parse_profile tool allows you to provide a public URL to a resume file for AI parsing.
What is profile asking? +
It's an AI feature that lets you ask questions about a candidate's profile in plain English and get intelligent answers based on their data.
How do I use the `score_profiles` tool in HrFlow.ai? +
You pass the candidate profile ID and the target job ID. This function returns a calculated score, detailing where the candidate aligns with job requirements.
What can the `search_profiles` tool find using semantic filters? +
It finds profiles based on meaning, not just keywords. You can search for skills, experiences, or industries even if the exact words aren't used in the profile data.
Does `parse_profile` handle non-standard resume formats? +
Yes, the parser handles various formats, including complex layouts and non-standard text. It outputs a consistent, structured profile object for reliable processing.
How do I list all available job boards using `list_boards`? +
Simply call list_boards with no parameters. This action returns a list of all connected job board sources available for job listing.
Can `unfold_profile` analyze career gaps or transitions? +
Yes, it analyzes the entire career history. It details career paths, identifying potential gaps or significant shifts between roles to give a full picture.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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