HireLayer MCP server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| HIRELAYER_API_KEY | Yes | Your HireLayer API key, copied from your dashboard after creating an account at hirelayer.co. | |
| HIRELAYER_BASE_URL | No | The base URL for the HireLayer API. | https://hirelayer.co |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| parse_resumeA | Parse a resume (PDF, DOC, DOCX, ODT, PPT, PPTX, ODP, XLS, RTF, TXT, JPG, PNG or BMP, under 4.5 MB) into structured JSON: contact details, work experience, education, languages, skills and the full resume text in info_resume.text. Pass a local file_path or a public file_url. Costs 1 HireLayer credit. |
| extract_job_criteriaA | Turn a job description (any language) into weighted matching criteria (matching_criteria[]), each with a weight from 1 to 3, a mandatory flag and a rationale. Feed the result to match_candidate. Costs 1 HireLayer credit. |
| match_candidateA | Score one candidate against a job: returns a score between 0 and 1 and an evaluation of each criterion. Use the criteria from extract_job_criteria and the resume text from parse_resume (info_resume.text). Costs 1 HireLayer credit. |
| rank_candidatesA | Rank up to 10 candidates against the same job description, from their resume texts. Costs 1 HireLayer credit per call. |
| resolve_skillsA | Map free-text skills in French or English (one skill, a compound string or a whole skills section) to skills of the HireLayer taxonomy, with their IDs. Costs 1 HireLayer credit. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 5 tools
parse_resume and extract_job_criteria clearly handle different input types, and resolve_skills targets a distinct taxonomy-mapping task. match_candidate and rank_candidates overlap somewhat (both score resumes against a job), but the single-vs-batch distinction and descriptions make them distinguishable.
All five tools follow a consistent verb_noun snake_case pattern (parse_resume, extract_job_criteria, match_candidate, rank_candidates, resolve_skills). No mixed conventions or vague verbs.
Five tools is well-scoped for a resume-matching pipeline. Each tool represents a distinct, necessary stage and none feel redundant or bloated.
The pipeline covers the core workflow: parse resumes, extract criteria, resolve skills, then match and rank candidates, with a coherent data flow between tools. Minor gaps exist around persisting/retrieving results or job/candidate management, but the stated matching purpose is well covered.