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Analyze a GitHub developer

analyze_github_user

For someone in a role, use score_role_person instead (it also judges them against the role's criteria). Score a GitHub user's public work on 4 general dimensions (technical_depth, shipping_velocity, communication, gem_score; 0-10 each, total out of 40) with findings (what their work shows) and red flags, each with the GitHub links it rests on (claims without a source are dropped), and top skills. The analysis reads their own repos and their work on other people's projects (outside_work: commits and PRs by repo, last year). The scores are general: none is judged against a role, and where someone lives is never used. Returns a saved result if the user was analyzed in the last 30 days (free, cached=true); otherwise scrapes, scores and saves (takes 15-25s). Also returns activity: their GitHub contribution calendar for the last year, summarized (total, active weeks, streaks, busiest month, last active; null when GitHub has none). Set refresh=true only when the user asks for a fresh analysis.

Input Schema

NameRequiredDescriptionDefault
persistNoSave a new result to the candidates table
refreshNoIgnore a saved result and re-analyze
usernameYesGitHub username, e.g. 'shadcn'

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Beyond annotations, it discloses substantive behavior: 30-day caching with cached=true, 15-25s scrape time, dropped unsourced claims, outside_work scope over the last year, and the activity calendar return. These details materially affect invocation and expectation setting.

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 dense and front-loads the alternative tool before detailing scope and behavior. It is somewhat long, but each sentence carries relevant invocation or expectation-setting information.

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?

For a complex scoring tool with no output schema, the description explains what is returned, how claims are sourced, caching/latency behavior, scope limits, and activity output. It gives an agent enough context to call it correctly and set expectations.

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 100%, so the baseline is 3. The description adds useful meaning for refresh by restricting it to explicit fresh-analysis requests, while persist remains covered adequately by 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: score a GitHub user's public work on four named dimensions, with findings, red flags, top skills, and activity. It also explicitly distinguishes itself from score_role_person, so an agent can select it without opening the schema.

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 gives an explicit alternative for role-based evaluation (score_role_person) and a clear condition for refresh=true: only when the user asks for a fresh analysis. The cached-vs-scraped behavior is also stated, removing ambiguity about when the long-running path occurs.

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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