technical-impact-analyst
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| GITHUB_TOKEN | Yes | GitHub personal access token with repo, read:user, and read:org scopes | |
| GITHUB_USERNAME | Yes | Your GitHub username |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| logging | {} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| extensions | {
"io.modelcontextprotocol/ui": {}
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| get_contribution_metricsA | Retrieve raw GitHub contribution metrics filtered by time period. Returns aggregated data including commits, PRs, reviews, and code changes. Useful for understanding contribution volume and patterns. |
| analyze_karpathy_alignmentA | Analyze GitHub contributions against the Andrej Karpathy Skills framework. Returns scores (1-5) for five skill dimensions:
Also detects "First Principles" indicators (Karpathy's emphasis on understanding things from scratch rather than relying on heavy abstractions). |
| get_architecture_impactA | Identify contribution types (Feature, Refactor, Bug Fix) and assess their impact on codebase health. For each PR, returns:
|
| generate_weekly_impact_summaryA | Generate an executive weekly impact summary for stakeholders. Consolidates the week's activities into a structured report including:
|
| scan_first_principlesA | Scan contributions for first-principles thinking patterns. Analyzes PRs looking for:
Returns:
|
| detect_attention_to_detailA | Advanced attention-to-detail analysis with bonus scoring. Checks:
Returns bonus points, anti-pattern flags (red/yellow), and checklist. |
| generate_client_reportA | Generate a client-facing delivery report translating technical contributions into business value. Designed for freelancers, MEI, and consultants who need to communicate value to non-technical stakeholders. Returns a formatted Markdown report with:
|
| export_evolution_dataA | Export Karpathy skill evolution data for Spider Chart visualization. Analyzes the last N weeks and returns scores for each dimension per week, plus trend analysis (improving/declining/stable). Designed to feed the external Next.js Karpathy Dashboard. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 8 tools
Most tools have distinct purposes: metrics retrieval, skills alignment, architecture impact, first-principles scanning, attention-to-detail checking, and report generation. The two report generators (weekly summary vs client report) could be confused, but their descriptions clarify different audiences and content.
All tool names follow a verb_noun pattern (export_, get_, analyze_, generate_, scan_, detect_). There is some variety in verbs (export, get, analyze, generate, scan, detect) but no mixing of naming conventions like camelCase or inconsistent styles, making the set predictable.
Eight tools is well-scoped for a technical impact analysis server. Each tool covers a distinct aspect of analysis or reporting, and the count is neither too thin nor overloaded.
The tool surface covers the full workflow from raw metrics retrieval to specialized Karpathy skill analysis and report generation. Minor gaps exist, such as lacking a tool for directly comparing historical periods beyond export_evolution_data, but the core capabilities are present and well-integrated.