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Generate Cover Letter

generate_cover_letter
Destructive

Generates a tailored cover letter for a job application in a requested tone using your API key, replacing any prior AI-generated entry.

Instructions

Maestro's AI writes a cover letter for an application in the requested tone, with the user's API key (with none set it returns an error). This replaces the application's prior generated cover-letter entry. Refused (409) when the user's writer setting (get_setup_status) says the agent app writes; store_cover_letter stores a letter the caller wrote, with no LLM call.

Input Schema

NameRequiredDescriptionDefault
toneYes
application_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.11.1

TDQS

A4.1/5.0
Behavior5/5

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

Adds substantial traits beyond the annotations: it errors when no API key is set, it overwrites the prior generated entry (concretely defining what destructiveHint covers), and it returns 409 when the writer setting conflicts. These are exactly the preconditions an agent needs before invoking.

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?

Three sentences, front-loaded with the action, then prerequisites, then failure modes and the sibling contrast. Dense but every clause carries information; the parenthetical error note is slightly parenthetical-heavy but not wasteful.

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

Completeness4/5

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

No output schema exists, and the description implies a stored letter but doesn't say what the call returns (letter body, id, or entry reference). Otherwise preconditions, side effects, and refusal behavior are all covered for a two-param generation tool.

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

Parameters2/5

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

Schema coverage is 0% and both params are bare 'string' fields, yet the description only gestures at 'tone' without listing valid tones or noting format, and says nothing about application_id beyond context. With the schema doing nothing, the description needed to carry this and largely did not.

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 and resource ('writes a cover letter for an application') plus the key qualifier 'Maestro's AI', immediately separating it from store_cover_letter. An agent can tell what it produces and that generation is LLM-backed.

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?

Names the alternative (store_cover_letter, 'no LLM call') and the condition that blocks this tool (writer setting says agent app writes, checked via get_setup_status), plus the missing-API-key prerequisite. It stops short of saying which situation positively favors this tool, but the contrast is clear enough to route correctly.

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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