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score_against_jd

Score GitHub candidates against a job description with breakdowns by tech stack, experience, OSS signal, and leadership. Identifies gaps and generates personalized interview questions.

Instructions

Score GitHub candidates against a job description with per-dimension breakdown.

Unlike rank_candidates (keyword matching), this extracts structured requirements from the JD and scores each candidate on: tech stack match, experience level, OSS signal, and leadership signals. Returns dimension scores, gaps, and personalized interview questions.

Args: job_description: Full job description text usernames: GitHub usernames to evaluate top_n: Number of top candidates to return (default 10)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_descriptionYes
usernamesYes
top_nNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Install Server

TDQS

A4.2/5.0
Behavior3/5

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

No annotations provided, and the description does not mention safety traits (read-only, destructive, auth needs). However, it describes outputs and operation, which is adequate for a scoring tool.

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?

The description is concise with three purposeful sentences plus a structured args list. No redundant information.

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?

Given the tool's moderate complexity and the presence of an output schema (not shown), the description covers key aspects: purpose, differentiation, and return contents. Could mention prerequisites like having candidate profiles.

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?

Schema description coverage is 0%, but the description compensates by explaining each parameter's purpose (job description, usernames, top_n) beyond the schema's basic type and title.

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 scores candidates against a job description with per-dimension breakdown. It distinguishes itself from rank_candidates by contrasting keyword matching with structured requirement extraction.

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 explicitly provides when-to-use versus an alternative (rank_candidates), but does not cover exclusions or scenarios where this tool should not be used.

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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