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Search hiring facts

hirelog_search
Read-onlyIdempotent

Search hiring facts. query is free text (any language). Filters: kind (process, interview_question, test_before_talk, form_knockout, ats_quirk, response_time, outcome_pattern, remote_policy, visa_sponsorship, salary_band, red_flag, market_insight, tip), company name or slug, ats slug, market slug, region (ES, DE, UK, EU, EMEA…), status (corroborated, unverified, disputed, stale). Returns notes with trust scores.

Input Schema

NameRequiredDescriptionDefault
atsNo
kindNo
limitNo
queryNo
marketNo
regionNo
statusNo
companyNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered. The description adds one useful behavioral fact ("Returns notes with trust scores") but omits default result count, pagination/limit behavior, and ordering semantics.

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

Conciseness4/5

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

Front-loads the one-line purpose, then packs the filter vocabulary into a compact block. The long inline `kind` enumeration is bulky but earns its space because the schema supplies no enums; a bulleted breakdown would scan slightly better.

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?

An output schema exists, so return-shape detail is unnecessary, and the description covers query semantics, every meaningful filter's accepted values, and what kind of records come back. For an all-optional faceted search this is sufficient to call it 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?

With 0% schema description coverage the description must carry the load, and it largely does: it enumerates all 13 `kind` values and 4 `status` values the schema does not declare as enums, and explains that `company`, `ats`, and `market` accept slugs. Only `limit` is left entirely unexplained.

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

Purpose4/5

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

Opens with a specific verb+resource ("Search hiring facts") and immediately enumerates the dimensions searched over, so the agent knows this is a faceted search rather than a single-entity lookup. It doesn't explicitly contrast itself with siblings like hirelog_company or hirelog_ats, which is the only thing keeping it from a 5.

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

Usage Guidelines3/5

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

Usage is implied by the filter list — the agent can infer it should pass a free-text query and/or narrow with company/kind/region/status. There is no explicit statement of when to prefer this over hirelog_company, hirelog_ats, or hirelog_market, and no note that all filters are optional with an empty query acting as a browse.

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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