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moonie0201

ats-jobs-mcp

by moonie0201
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

ats-jobs-mcp

An MCP server that answers "what is this company hiring for, right now" by reading the company's own applicant-tracking system board API at call time — Greenhouse, Lever, Ashby, Recruitee, Rippling or Personio. Not an index that was crawled last night.

No HTML scraping, no headless browser, no proxies. Every request goes to an endpoint the vendor publishes for exactly this purpose.

Install

Requires uv and a free Apify token. Runs are billed to your Apify account — see Pricing.

{
  "mcpServers": {
    "ats-jobs": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/moonie0201/ats-jobs-mcp", "ats-jobs-mcp"],
      "env": { "APIFY_TOKEN": "apify_api_..." }
    }
  }
}

Client

Where that JSON goes

Claude Desktop

~/Library/Application Support/Claude/claude_desktop_config.json (macOS) · %APPDATA%\Claude\claude_desktop_config.json (Windows)

Cursor

~/.cursor/mcp.json, or .cursor/mcp.json in a project

Claude Code

claude mcp add ats-jobs --env APIFY_TOKEN=apify_api_... -- uvx --from git+https://github.com/moonie0201/ats-jobs-mcp ats-jobs-mcp

Related MCP server: recruiting-jobs-mcp

Tools

Tool

What it does

Cost

search_jobs(companies, title_contains?, remote_only?, max_jobs?)

Live job rows for the given companies. companies are provider:slug pairs (lever:palantir) or career-site URLs. Returns title, company, location, remote flag, structured salary, employment type, url, postedAt.

paid, see below

resolve_company(url_or_name)

Maps a company name or career-site URL to the provider:slug pair search_jobs wants.

free

list_supported_ats()

The six providers and the slug form each expects.

free

Filters are applied before billing, so a narrow query is cheap rather than a big response you post-filter. max_jobs is a hard spend cap.

Pricing

search_jobs starts a run of the paid Apify Actor acotr_moonie/ats-jobs-scraper at $0.002 per delivered job row, billed by Apify to the account behind your own APIFY_TOKEN. This server is a thin client: it never proxies your calls, never sees your token or your results, and no run is ever paid for on your behalf. The default max_jobs=50 caps a single call at about $0.10.

resolve_company and list_supported_ats cost nothing and start no run.

What it will not do

  • Salary is parsed, never inferred. Each salary carries a source field saying whether the number came from a structured provider field or a regex over prose.

  • No contact fields are emitted, and contact details in ad bodies are redacted by default.

  • Where a value cannot be determined the field is null. Nothing is guessed.

  • resolve_company searches a partial directory — 1,804 rows, 1,444 of them verified live at last probe. A name that does not resolve is not proof the company has no board; pass the career-site URL instead.

How it works

resolve_company reads the CC-BY ats-directory over jsDelivr and caches it under ~/.cache/ats-jobs-mcp/ for 24 hours. search_jobs calls the Apify Actor's run-sync-get-dataset-items endpoint with your token.

Links

Development

uv venv && uv pip install -e ".[dev]"
python -m pytest tests -q       # offline: builds the server, lists tools
python -m ats_jobs_mcp --list-tools

Disclosure

I built this, and the Actor behind search_jobs is paid. The adapter code and the company directory are open and free to use without it.

MIT licensed.

Available Tools

3 tools
list_supported_atsA

List the applicant-tracking systems this server can read, and the 'provider:slug' form search_jobs expects for each. No network, no cost.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the burden of behavioral disclosure. It usefully states 'No network, no cost,' signaling that this is a safe, local, read-only introspection call. It does not fully enumerate all behavioral details, but for a zero-parameter list operation this is strong disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence communicates the resource, the purpose, the relationship to a sibling tool, and the operational characteristics ('No network, no cost'). There is no redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple, zero-parameter introspection tool with an output schema, the description is complete. It tells the agent what the tool does, what it returns, how the result is used by search_jobs, and that it has no side effects. Nothing essential is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There are zero parameters, so parameter-semantics burden is minimal. The schema coverage is effectively 100%, and the description adds relevant context about what the returned provider slugs mean and how they relate to search_jobs.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description identifies a specific verb ('List'), a specific resource ('applicant-tracking systems this server can read'), and the useful detail that it exposes the 'provider:slug' form expected by search_jobs. This clearly distinguishes it from sibling tools like search_jobs and resolve_company.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly implies when to use this tool: before calling search_jobs, to learn which ATS providers are supported and what slug format to provide. It does not explicitly state when not to use it, but the context is clear and the sibling distinction is strong.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

resolve_companyA

Map a company name or career-site URL to the 'provider:slug' pair that search_jobs takes, using the public CC-BY ats-directory. A URL is read directly; a name is matched against the directory. Returns every match, with 'status' saying whether that board answered when last probed (ok / dead / unconfirmed). Free — no Apify run, no billing.

ParametersJSON Schema
NameRequiredDescriptionDefault
url_or_nameYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.6/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It does well: it names the data source (public CC-BY ats-directory), explains that URLs are read directly while names are matched, notes that every match is returned, defines status values, and clarifies there is no Apify run or billing. Minor gaps like no-match behavior are not covered, but the key behavioral traits are disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is four sentences, each earning its place: purpose, input-type handling, output/status semantics, and cost. It is front-loaded with the core purpose and contains no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given a single parameter, an output schema that can document return structure, and no annotations, the description provides sufficient context: what the tool does, how the input is interpreted, what matches include, and that it is free. Nothing essential for selecting or invoking the tool correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema only defines 'url_or_name' as a string with 0% schema description coverage, so the description must compensate. It does so thoroughly by specifying both accepted forms (company name or career-site URL) and how each is processed. This adds real meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Map') and a resource ('company name or career-site URL') to produce a clearly defined output ('provider:slug' pair). It also explicitly ties this to search_jobs, making the tool's role distinct from its siblings without needing to open schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly implies when to use this tool: before calling search_jobs, since it produces the pair search_jobs consumes. It also clarifies behavior for the two supported input types (URL vs. name). It does not explicitly state exclusions or compare against list_supported_ats, but the context is clear enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_jobsA

Fetch live job postings for named companies straight from their ATS board API. Companies are 'provider:slug' pairs (lever:palantir) or career-site URLs; use resolve_company first if you only have a name. Filters are applied before billing, so a narrow query is cheap. Billed to your own Apify account at $0.002 per returned job row: max_jobs is the hard spend cap.

ParametersJSON Schema
NameRequiredDescriptionDefault
max_jobsNo
companiesYes
remote_onlyNo
title_containsNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does a good job: it discloses billing per row, max_jobs as a hard spend cap, filter-before-billing semantics, and that data is fetched live from ATS APIs. It does not mention failure modes for invalid companies or rate limits, but the key operational trait—cost—is transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Four sentences, all earning their place: the core operation is front-loaded, followed by identifier format, cost behavior, and spend-cap warning. There is no filler or repetition of schema fields.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Output schema covers return values, so that omission is fine. The definition is strong on cost and company-identifier format, but because two parameters are undocumented in both the schema and description, and list_supported_ats is never referenced, the agent cannot fully plan all call variants.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It explains companies (provider:slug or career-site URL) and max_jobs (spend cap), but remote_only and title_contains are left completely unexplained, leaving the agent unable to know their exact behavior from the definition.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The opening sentence identifies a specific action (fetch live job postings) and a concrete resource (named companies' ATS board APIs). The provider:slug/URL detail and 'use resolve_company first' clearly differentiate it from its sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly instructs to call resolve_company first when only a name is available, and explains that narrow queries are cheaper because filters are applied before billing. However, no guidance covers when to use list_supported_ats or when search_jobs would not be the right tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 3 tool updatesv0.1.0
    • First observedlist_supported_ats
    • First observedresolve_company
    • First observedsearch_jobs
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.4/5.0

Scored across 3 tools

Disambiguation5/5

Each tool has a distinct responsibility: searching live jobs, listing supported ATS integrations, and resolving company names/URLs to provider-slug pairs. There is no overlap or ambiguity between them.

Naming Consistency5/5

All three tools use a consistent snake_case verb-first pattern: search_jobs, list_supported_ats, resolve_company. The naming clearly signals action and object.

Tool Count5/5

Three tools is an appropriate size for this server's focused purpose. Each tool is necessary and contributes a distinct step in the workflow from company name to actual job postings.

Completeness5/5

The tool set covers the full intended workflow: discover supported ATS platforms, resolve a company to the expected identifier, then search live job postings. No obvious missing operations or dead ends are apparent for the stated purpose.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

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