queryquarry

search_candidates

Read-only

Search the talent graph. Returns ANONYMOUS match cards — headline, skills (with skills_matched showing which of YOUR terms hit), seniority, location, availability, salary range, and a short snippet — but NO name, full resume, or contact. Call get_candidate (a metered reveal) for the deeper anonymous profile. Filter by skills, location, seniority, salary, experience, availability, and more; sort by recency (default) or skill_match. Always paginated (max 25 per page).

Input Schema

TableJSON Schema

Name	Required	Description
`page`	No	1-based page.
`sort`	No	Result order. When `skills` are supplied the default is skill_match: candidates matching MORE of your skills terms first (ties by most recently updated) — pure arithmetic over your own criteria, no fit scoring. Pass "recency" to order purely by recent updates; searches without `skills` are always recency-ordered.
`limit`	No	Results per page (max 25).
`skills`	No	Skills to match (any of). Case-insensitive partial match — use plain terms like "React", "Node", "Postgres"; they also match versioned/variant skills ("React 19", "Node.js", "PostgreSQL").
`location`	No	City with state ("Schaumburg, IL") or a 5-digit zip ("60133" — most precise). Geocoded locally; combine with radius_miles for distance search. Unresolvable text falls back to substring match.
`remote_ok`	No	Only candidates open to remote/hybrid.
`salary_max`	No
`salary_min`	No
`availability`	No
`radius_miles`	No	With location: include candidates within this many miles (e.g. 5, 10, 25, 50; max 100). Omit for exact-place matching.
`updated_since`	No	ISO timestamp — only resumes updated since.
`employment_types`	No	Engagement types to match (any of): full-time, part-time, contract, freelance, internship. Synonyms/variants are normalized.
`seniority_levels`	No	Seniority levels to match (any of): entry, junior, mid, senior, staff, principal, manager, director, executive.
`open_to_relocation`	No	Only candidates willing to relocate for the right role.
`work_authorization`	No	Filter by work authorization: "authorized" (no sponsorship needed) or "sponsorship-required".
`work_location_types`	No	Work-location preferences to match (any of): remote, hybrid, onsite.
`experience_years_max`	No
`experience_years_min`	No

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

Changed1 schema field changedJul 12, 2026

changedInput schema / properties / sort / description

Previous value: -"Result order. Default recency (most recently updated first). skill_match: candidates matching MORE of your `skills` terms first (ties by recency) — pure arithmetic over your own criteria, no fit scoring. Requires `skills`."New value: +"Result order. When `skills` are supplied the default is skill_match: candidates matching MORE of your skills terms first (ties by most recently updated) — pure arithmetic over your own criteria, no fit scoring. Pass \"recency\" to order purely by recent updates; searches without `skills` are always recency-ordered."

Changed1 schema field changedJul 12, 2026

addedInput schema / properties / sort

Added value: +{
+  "description": "Result order. Default recency (most recently updated first). skill_match: candidates matching MORE of your `skills` terms first (ties by recency) — pure arithmetic over your own criteria, no fit scoring. Requires `skills`.",
+  "enum": [
+    "recency",
+    "skill_match"
+  ],
+  "type": "string"
+}

First observedJul 5, 2026

TDQS

A4.7/5.0

Behavior5/5

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

The annotation readOnlyHint=true already indicates a safe read operation, but the description adds crucial behavioral context: results are anonymous, include no contact info, are paginated (max 25), and sorting is arithmetic over the user's criteria with recency as default. This goes well beyond the annotation and helps the agent set expectations.

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?

Two sentences, tightly packed with high-value information: purpose, output contents, explicit exclusions, alternative tool, filters, sorting, and pagination. No filler or repetition; the most important facts are front-loaded.

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?

With 18 parameters and no output schema, the description compensates by specifying the shape of the anonymous match card (headline, skills_matched, seniority, location, availability, salary range, snippet) and the always-paginated behavior. It also provides enough context about filtering and sorting to use the tool effectively, and points to get_candidate for deeper data.

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

Parameters3/5

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

The schema already covers 72% of parameters with meaningful descriptions, so the description's high-level filter list adds limited new parameter semantics. It reinforces the filter categories and notes pagination/sorting defaults, but does not materially improve on the schema's detailed parameter explanations.

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 opens with a specific verb+resource ('Search the talent graph') and clearly defines the scope: returns anonymous match cards with listed fields but no PII. It explicitly distinguishes from sibling tools by directing to get_candidate for the deeper anonymous profile, and the mention of filtering/sorting differentiates it from other candidate tools.

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

Usage Guidelines5/5

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

The description explicitly says to call get_candidate for a 'metered reveal' of the deeper profile, providing an alternative tool and the context for when to use it. It also covers sorting defaults and pagination, giving the agent clear guidance on behavior without needing to infer.

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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TDQS

A4/5.0

Disambiguation5/5

Each tool targets a distinct operation: subscription, candidate search, detailed view, contact management, stats, docs, new candidates, watchlist, outcome reporting, and saving. No two tools overlap in purpose; the descriptions clearly differentiate them.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., check_subscription, get_candidate, search_candidates). The verbs vary as needed for different actions, maintaining a predictable structure.

Tool Count5/5

With 11 tools, the set is well-scoped for a candidate sourcing platform. It covers search, detailed viewing, contact, watchlist management, subscription, and documentation without being excessive or too sparse.

Completeness4/5

Core workflows are covered: search, view, request contact, report outcome, save to watchlist. Minor gaps exist, such as lacking a remove-from-watchlist tool or a way to cancel a contact request, but these do not severely hinder the main use case.

search_candidates

Input Schema

Schema Changelog

TDQS

Glama MCP Gateway

TDQS

Resources