Grey Ridge Jobs MCP

search_jobs

Fetch live, normalized job postings for one or more companies across multiple ATS platforms, with automatic platform and board token detection and a unified result schema.

Instructions

Fetch live, normalized open job postings for one or more companies across 7 ATS platforms (Greenhouse, Lever, Ashby, SmartRecruiters, Workday, Recruitee, Workable). Each company may be given as a bare slug ("stripe"), a domain ("stripe.com"), or an explicit "platform:token" ("greenhouse:stripe", "lever:palantir", "workday:nvidia.wd5:NVIDIAExternalCareerSite"). The ATS and board token are auto-detected for slugs/domains. Returns a unified schema: title, company, location, remote, employment_type, department, posted_at (ISO), salary, url, source_platform.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "companies": "<companies>"
    },
    "name": "search_jobs"
  }
}

Input Schema

Name	Required	Description
`companies`	Yes	Company slugs, domains, or "platform:token" strings. At least one required.
`maxPerCompany`	No	Cap postings returned per company (default 25, max 200).
`includeDescription`	No	Include the full job description text (heavier payload). Default false.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.0Oct 30, 2026

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations, the description carries the burden and discloses meaningful behavior: data is live and normalized, ATS and board tokens are auto-detected for slugs/domains, and includeDescription controls a heavier payload. It does not disclose rate limits, error behavior, or auth requirements, but it adds substantial context beyond the bare operation.

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 front-loaded with purpose and platform scope, followed by input format details and return schema summary. Every sentence earns its place, with no filler or repetition.

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

Completeness4/5

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

Given no output schema exists, the description usefully enumerates the unified return fields (title, company, location, remote, employment_type, department, posted_at, salary, url, source_platform). It omits pagination/rate-limit behavior and does not explicitly route away from sibling tools, but it is otherwise complete for an agent to call the tool correctly.

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?

Schema coverage is already 100%, so the baseline is 3, but the description adds useful meaning beyond the schema by giving concrete examples of each accepted company identifier format (bare slug, domain, platform:token) and explaining that ATS and token are auto-detected. It does not elaborate further on maxPerCompany or includeDescription beyond what the schema already says.

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 ('Fetch') and resource ('live, normalized open job postings'), scopes it to 'one or more companies across 7 ATS platforms', and lists the exact platforms. This makes the tool clearly distinguishable from siblings like search_hn_hiring and detect_ats without opening any schema.

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 provides clear context for when to use the tool: fetching live postings across ATS platforms for specified companies, with accepted input formats. It does not explicitly name alternatives or when-not-to-use cases, such as routing to search_hn_hiring or detect_ats, so it stops short of full usage guidance.

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

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