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Casius999

decroche-mcp

by Casius999

ats_screener_brief

Simulates ATS parsing to produce plain text, a scoring rubric, and keyword requirements from a job offer, enabling preview of AI screening.

Instructions

Build a screener simulation kit for Claude.

Produces the plain text the machine sees after ATS parsing, a fixed scoring rubric, and deterministic keyword requirements extracted from the offer. Claude then plays the AI screener on this exact text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
json_resumeYesJSONResume-compatible dict (output of cv.parse).
offer_textYesRaw job offer text or job description.
ats_idYesTarget ATS identifier.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
machine_view_textYes
rubricYes
requirementsYes
ats_idYes
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the outputs (plain text, rubric, keywords) but does not mention side effects, idempotency, authentication, or rate limits. The behavior is inferred as read-only, but not explicitly stated.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, no redundant information, front-loaded with the primary action, and each sentence adds essential context without waste.

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?

With an output schema present, the description does not need to detail return values. It outlines the key outputs and hints at the workflow. It could mention that cv_parse should be used first, but the json_resume parameter description hints at it. Overall adequate.

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 coverage is 100% and the schema descriptions are clear. The overall description adds context by linking json_resume to cv.parse and explaining the tool's purpose, but does not add parameter-specific details beyond what the schema provides.

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 uses a specific verb-object pair 'Build a screener simulation kit', and clearly distinguishes from siblings like ats_parse_sim or ats_score_report by describing the output as a complete simulation kit including plain text, rubric, and keywords.

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?

The description implies usage for simulating ATS screening but does not explicitly state when to use this tool versus alternatives, nor does it mention exclusions or prerequisites beyond the inputs.

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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