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cover_letter_submit

Checks a cover letter body for traceable facts, length, banned phrases, keywords, language, and one-page fit, then creates PDF and text files when accepted.

Instructions

Check a cover letter body (facts traceable to the resume/ad, length, banned phrases, keywords, language, one page). If it passes and accept_if_ok, makes the PDF + plain text in the output folder.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyYes
job_idYes
accept_if_okNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.5

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose a real side effect (creates PDF + plain text in the output folder) plus the gate that triggers it (passing checks AND accept_if_ok). It does not say what happens on failure, whether existing output files are overwritten, or what permissions/paths are required, which are significant gaps for a write-capable tool.

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?

Two dense sentences, front-loaded with the validation purpose and with the consequential write behavior placed second where it matters. The parenthetical check list is long but each item earns its place by defining what 'passes' means.

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

Completeness3/5

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

There is no output schema and no annotations, so the description should cover results too. It implies a pass/fail diagnostic but never says what the check returns on failure (error list? reasons?), nor what the output files are named or where exactly they land, leaving the agent unable to predict the response.

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 0%, so the description must compensate for all three parameters. It usefully clarifies accept_if_ok's role (write only happens when checks pass and this flag is set) and body is described in passing as the letter to check, but job_id is never explained and no formats or constraints are given for any parameter.

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?

The description gives a specific verb+resource ('Check a cover letter body') and enumerates the checks performed, plus the secondary action (making the PDF and plain text files). It is clearly distinguishable from a generic submit tool, but it never names or contrasts with the sibling cover_letter_prepare, so sibling differentiation is left to inference.

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 rather than stated: the tool is meant to run on a drafted body, and 'If it passes and accept_if_ok' explains the conditional write path. There is no explicit when-to-use/when-not or pointer to cover_letter_prepare for the drafting step, so the agent must infer the workflow position.

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