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queryquarry

by io.github.QueryQuarry

request_contact

Reach out to one candidate. You identify yourself (your name + company, from your account); the candidate is notified and decides. If they accept, you receive an EMAIL with their contact, their message, and a unique verification code — their identity stays private until they choose to share it. METERED (counts toward your contact limit) and requires a prior get_candidate for this resume. One active offer per candidate; respect declines. get_contact also shows the status any time.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
messageYesYour pitch to the candidate: the role and why them. Your account email is AUTOMATICALLY attached and shown to them when they express interest, so you don't need to include it. You may optionally add other ways to connect (an application form link, your LinkedIn, etc.). Shown in-app, not your raw query.
resume_idYes
candidate_idYes

Schema Changelog

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

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations only give hints (readOnly=false, openWorld=true, idempotent=false, destructive=false), so the description carries the full burden. It discloses the asynchronous notification flow, email delivery on acceptance, privacy protection, metering, and the one-active-offer rule. No contradiction with annotations.

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?

Dense but well-structured: the opening sentence states purpose, followed by workflow, side effects, and alternatives. Every sentence adds value—privacy, metering, prerequisites, and limits—with no fluff or repetition.

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 no output schema, the description explains the asynchronous outcome (email with contact info and verification code) and directs status checks to get_contact. It covers prerequisites, limits, privacy, and the decision process, making the tool's behavior predictable for an agent.

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 description coverage is low (33%, only message), so the description must compensate. It clarifies that candidate_id refers to the single candidate being contacted and that resume_id comes from a prior get_candidate call. The message parameter is fully specified in the schema, providing adequate semantics for all parameters.

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 and object ('Reach out to one candidate') and clearly explains the request workflow. It distinguishes itself from sibling tools by noting get_contact shows status, making it clear this tool initiates the contact request rather than checking its status.

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?

Provides explicit usage context: requires a prior get_candidate for this resume, metered (counts toward limit), one active offer per candidate, and respect declines. It also points to get_contact as the status-checking alternative, giving the agent clear guidance on when to use each tool.

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

All queryquarry toolsInspect
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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.

Resources