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nishtobehonest

Resume Tailor MCP Server

tailor_resume

Takes a job description and LaTeX resume, returns structured diff with keywords, gap summary, bullet changes, skills changes, and a guardrails report.

Instructions

Tailor a LaTeX resume to match a job description.

Sends both inputs to Claude with a constrained system prompt that enforces:

  • No hallucinated facts or metrics

  • LaTeX formatting preserved exactly

  • Only experience bullets and skills section modified

  • Structured JSON output with before/after diffs and a guardrails report

Args: job_description: Full text of the job posting (paste directly) resume_content: Full LaTeX source of the resume (paste directly)

Returns: JSON string with keys: jd_keywords, gap_summary, bullet_changes, skills_changes, guardrails_report

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
resume_contentYes
job_descriptionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Since no annotations are provided, the description fully covers behavioral traits: it explains the internal process (sends inputs to Claude with a constrained system prompt), lists specific constraints (no hallucination, preserve LaTeX, only modify certain sections), and describes the output format.

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 moderately long but well-structured: a summary sentence, bulleted constraints, then parameter and return descriptions. It is front-loaded with purpose. The 'Args:' and 'Returns:' sections add clarity without being verbose.

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?

The description covers purpose, behavior, parameters, and return format. It references an output schema ('returns JSON string with keys...'). Given the tool's complexity (2 params, no annotations, no nested objects), the description is comprehensive.

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

Parameters5/5

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

With 0% schema description coverage, the description compensates by fully explaining both parameters: 'job_description' (full text of job posting, paste directly) and 'resume_content' (full LaTeX source). This adds meaning beyond 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 clearly states the tool 'Tailor a LaTeX resume to match a job description', specifying a clear verb ('tailor') and resource ('LaTeX resume'). It distinguishes itself from the only sibling 'hello', which is unrelated.

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?

The description explains when to use the tool (to tailor a resume) but does not provide explicit guidance on when not to use it or mention alternatives. The context is clear enough for an agent to decide.

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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