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Maestro Career Studio

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by seinun-ai
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Store Cover Letter

store_cover_letter
DestructiveIdempotent

Stores a user-written cover letter for a job application and renders its PDF, replacing the application's existing letter and PDF without an LLM call.

Instructions

Store a cover letter the caller wrote for an application and render its PDF, with no LLM call (generate_cover_letter has the app's configured model write it). It replaces the application's cover letter and that letter's PDF (an application holds one); the new one shows on the job's Apply step and in list_qa_entries. The text is stored as the user's own letter; the server does not verify its facts. As with a generated letter, en and em dashes become a hyphen in a range ("2020-2023") and a comma elsewhere, and one opening a line ("— Jane") is dropped; the rest is stored as written, line breaks and paragraphs included, up to 20000 characters. The PDF puts the base resume's contact block above the letter, in the engine of the application's resume template.

Returns the stored entry {id, application_id, kind, answer, model_used,
pdf_path, render_note, render_error, created_at}; model_used names the
agent. render_error non-null means the letter is stored and its PDF could
not be made (pdf_path is null). render_note is non-null only when a TeX-less
backend rendered a LaTeX template through a Typst one.

Input Schema

NameRequiredDescriptionDefault
textYes
application_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.12.0

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the destructiveHint and idempotentHint annotations, the description explains what is replaced, that the letter is stored as the user's own text without fact verification, the dash-normalization behavior, line-break preservation, the 20000-character limit, and how the PDF is assembled. It also explains render_error and render_note, which are important behavioral details not present in annotations.

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?

The description is front-loaded with purpose and the key alternative, then layers on operational details. It is long and dense for a two-parameter tool, and the render_note explanation is niche, but most sentences carry useful information for a destructive replacement operation.

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?

There is no output schema, so the description's return-value details are essential and present: it lists the returned entry fields and explains render_error and render_note semantics. Combined with replacement behavior, PDF rendering details, and storage constraints, it gives an agent enough context to call the tool 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 adds substantial meaning for text: it is the caller's own letter, stored with specific normalization and a 20000-character cap, with line breaks and paragraphs preserved. It only implicitly describes application_id through the phrase 'for an application' and does not state its format or required status, though it is required in the schema.

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 states a specific verb and resource: store a caller-written cover letter and render its PDF. It explicitly distinguishes this from generate_cover_letter by noting that no LLM call is involved here, while generate_cover_letter uses the configured model.

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?

It clearly establishes the use case: the caller wrote the letter, not the model. It names the alternative generate_cover_letter and explains its different behavior, and it warns that this replaces the application's existing cover letter and PDF.

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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