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check_claims

Read-only

Verify claims in cover letters, summaries, or LinkedIn text against a person's profile, returning problems to fix and warnings for unsupported skills or numbers before use.

Instructions

Check any text about the person against their profile before it goes anywhere: a cover letter, a summary, a LinkedIn text. build_letter and linkedin_profile run it too. Returns problems, which must be fixed (a skill marked X, or a familiarity skill not called familiarity in its sentence), and warnings for the person (a technology the profile doesn't show, or a number found nowhere in it). Fix them by removing or qualifying the claim, never by adding skills. Read-only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe draft: a cover letter, a summary, any text about the person.
profileNoPath of the profile folder. Leave it out to use the server's default (--profile or HONEST_CV_PROFILE).
keywordsNoKeywords of the job post, so its technologies are checked too.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.3.2
    • changedInput schema / properties / profile / description
      Previous value: -"Profile folder. Defaults to HONEST_CV_PROFILE."New value: +"Path of the profile folder. Leave it out to use the server's default (--profile or HONEST_CV_PROFILE)."
  2. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the trailing 'Read-only' is largely redundant; however the description adds substantial value beyond the annotations by disclosing the return structure (problems vs warnings), what each contains, and the required remediation policy ('remove or qualify, never add skills'). This behavioral guidance is not inferable from structured fields.

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-loaded with the purpose in the first clause, then usage, return shape, and remediation in a tight sequence. It is slightly dense with parenthetical examples but every sentence earns its place with 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?

With no output schema, the description correctly carries the burden of explaining the return value (problems and warnings, with examples of each). Combined with the remediation guidance and the pointer to sibling tools that reuse it, an agent has everything needed to call and act on this tool.

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?

Schema description coverage is 100%, so text, profile, and keywords are fully documented in the schema. The description reinforces the profile-relative nature of the check but adds no syntax or format detail for the individual parameters, so the baseline 3 applies.

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?

States a specific verb ('check') and resource ('any text about the person against their profile') and carves out scope by naming the related tools (build_letter, linkedin_profile) that invoke it internally. An agent can distinguish it from siblings like build_letter or verify_pdf without opening a 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?

Gives a clear trigger — 'before it goes anywhere' — and enumerates the artifact types it applies to (cover letter, summary, LinkedIn text), plus notes that build_letter and linkedin_profile run it automatically. It does not state explicit exclusions or name a competing tool to prefer instead, so it falls just short of 5.

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