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Codepet MCP Server

A local MCP server that captures coding context, tracks developer sessions, and powers daily insights. Compatible with Claude Code, Cursor, VS Code, Windsurf, and any MCP-compliant client.

Built by MURROR.


Quick Start

1. Install

cd codepet-mcp-server
npm install
npm run build

2. Connect to Your Editor

Claude Code — add to ~/.claude/claude_desktop_config.json:

{
  "mcpServers": {
    "codepet": {
      "command": "node",
      "args": ["/full/path/to/codepet-mcp-server/dist/index.js"]
    }
  }
}

Cursor — add to .cursor/mcp.json in your project root:

{
  "servers": {
    "codepet": {
      "command": "node",
      "args": ["/full/path/to/codepet-mcp-server/dist/index.js"]
    }
  }
}

VS Code / Windsurf — add to .vscode/mcp.json in your project root:

{
  "servers": {
    "codepet": {
      "command": "node",
      "args": ["/full/path/to/codepet-mcp-server/dist/index.js"]
    }
  }
}

Replace /full/path/to/ with the actual path on your machine.

3. Verify It Works

Once connected, ask your AI assistant:

"Scan my project and tell me what I'm working with"

If Codepet responds with your project structure, languages, and dependencies — you're all set.

4. Test with Inspector (Optional)

npm run inspect

Opens the MCP Inspector — a browser-based UI to test all tools and resources interactively.


Related MCP server: Vibgrate AI Context

Features Overview

What

How

Scan your project structure

Ask AI: "What am I working with?"

See recent git activity

Ask AI: "What did I commit today?"

Check for build errors

Ask AI: "Are there any compiler errors?"

Read any file with metadata

Ask AI: "Show me src/index.ts"

Track skill progress

Ask AI: "How are my skills progressing?"

Generate daily summary

Ask AI: "Generate my daily coding summary"


Phase 1 — Context Capture

Tools

Tool

Description

scan_project

Detect languages, frameworks, structure, and dependencies

get_git_context

Recent commits, diffs, branch info, activity patterns

get_diagnostics

Compiler errors, lint warnings, error→fix tracking

Resources

URI

Description

codepet://project/summary

Cached project fingerprint

codepet://user/profile

Pet name, level, streak, skills

codepet://missions/active

Contextual learning missions

Phase 2 — Learning & Skill Mapping

Tools

Tool

Description

get_file_content

Read file with language detection, line ranges, and metadata

get_learning_context

Aggregated learning progress: error patterns, streaks, skill tree XP, recommendations

Resources

URI

Description

codepet://skills/progress

Skill tree progress mapped from coding activity (16 skills, 4 kingdoms)

Skill Tree Mapping

Coding activity is automatically mapped to Codepet's 16 skills across 4 kingdoms:

Kingdom

Tier

Skills

The Molten Forge

1 — Foundations

Prompt Clarity, Error Reading, Tool Basics, Code Judgment

The Frozen Spire

2 — Context & Structure

Context Setting, AI Rules Files, Documentation, Project Structure

The Eternal Garden

3 — Advanced

Tool Switching, Scope Mgmt, Design System, Prompt Iteration

The Mystic Grove

4 — Expert

User Personas, Context Windows, AI Architecture, Second Brain

Each skill has 5 levels. XP is earned automatically based on what you do — reading files, fixing errors, exploring projects, and using different tools all contribute to different skills.

Phase 3 — Daily Summaries + macOS App Integration

Tools

Tool

Description

generate_daily_summary

LLM-powered daily summary with stats, narrative, and in-character pet reaction

Resources

URI

Description

codepet://summary/today

Today's daily summary (call generate_daily_summary to create)

macOS App Integration

The Codepet macOS app reads ~/.codepet/ data via MCPBridgeService.swift:

  • Polls every 30s for new events, summaries, and skill progress

  • Merges real coding XP into the app's progression system (with double-count prevention)

  • Updates pet mood and energy based on daily coding activity

  • Available as @EnvironmentObject throughout the SwiftUI view hierarchy

Session Logger

Every tool call is automatically logged to ~/.codepet/ as JSON files, organized by date. This data powers daily summaries and skill tracking.

Setup

cd codepet-mcp-server
npm install
npm run build

Add to Claude Code

// ~/.claude/claude_desktop_config.json
{
  "mcpServers": {
    "codepet": {
      "command": "node",
      "args": ["/path/to/codepet-mcp-server/dist/index.js"]
    }
  }
}

Add to Cursor / VS Code

// .cursor/mcp.json or .vscode/mcp.json
{
  "servers": {
    "codepet": {
      "command": "node",
      "args": ["/path/to/codepet-mcp-server/dist/index.js"]
    }
  }
}

Usage

Once connected, the AI client can call tools naturally:

"Scan my project and tell me what I'm working with" → calls scan_project

"What did I commit today?" → calls get_git_context

"Are there any build errors?" → calls get_diagnostics

"Show me the contents of src/index.ts" → calls get_file_content

"How am I progressing on my skills?" → calls get_learning_context

Data Storage

All data is stored locally in ~/.codepet/:

~/.codepet/
├── events/
│   └── 2026-04-07.json    # Today's tool call events
├── summaries/
│   └── 2026-04-07.json    # Generated daily summaries
└── profile.json            # User profile + cached project data

Roadmap

  • Phase 1: scan_project, get_git_context, get_diagnostics tools + resources ✅

  • Phase 2: get_file_content, get_learning_context tools + skill tree mapping ✅

  • Phase 3: Daily summary generation (LLM-powered), Codepet macOS app integration ✅

  • Future: SQLite backend (better-sqlite3), real-time file watching, pet evolution


Troubleshooting

"Server not found" or tools don't appear: Make sure you ran npm run build and the path in your config points to dist/index.js (not src/index.ts). The path must be absolute.

No data in ~/.codepet/: The server only writes data when tools are called. Ask your AI to run scan_project or get_git_context to generate the first events.

Daily summary is empty: Run generate_daily_summary — it creates the summary on demand. It needs at least a few tool calls logged in today's events file to produce meaningful output.

macOS app doesn't show MCP data: The macOS app polls ~/.codepet/ every 30 seconds. Make sure the MCP server has been used at least once today so event files exist. Check the Xcode console for [MCPBridge] log messages.

Permission errors on ~/.codepet/: The server creates this directory automatically. If it fails, create it manually: mkdir -p ~/.codepet/events ~/.codepet/summaries


License

MIT — see the root LICENSE file.

Available Tools

8 tools
auto_fixA

Attempt to automatically fix a coding error detected by the diagnostic watcher.

Byte will try a pattern-based fix first (fast, no AI needed). If the error is too complex, Byte returns a structured AI fix request that can be sent to Claude for an intelligent fix.

Modes:

  • "suggest": Show the fix without applying it (default, safe)

  • "apply": Apply the fix directly to the file (use with care)

  • "apply_all": Fix all auto-fixable errors in the project

Inputs: mode, file, line.

generate_daily_summaryB

Generate an LLM-powered daily coding summary with pet reaction. Aggregates session events, computes stats, and produces a natural language summary.

Inputs: date, force.

get_diagnosticsA

Run project diagnostics to capture compiler errors, lint warnings, and test failures. Tracks error patterns over time to identify struggle points and learning opportunities.

Inputs: path (required), toolchain, command.

get_file_contentA

Read a file's contents with language detection, line numbers, and metadata. Supports reading specific line ranges.

Inputs: path (required), startLine, endLine, projectRoot.

get_git_contextA

Get git repository context including recent commits, branch info, diff stats, and activity patterns. Powers daily summaries of what the user built.

Inputs: path (required), since, maxCommits.

get_learning_contextB

Get aggregated learning progress: error patterns, language activity, skill tree progress, streaks, and personalized recommendations.

Inputs: days.

scan_projectA

Scan a project directory to detect languages, frameworks, structure, and dependencies. Returns a project fingerprint used for context-aware coding assistance and skill tracking.

Inputs: path (required), maxDepth.

watch_diagnosticsA

Real-time diagnostic watcher — monitors your project for errors as you code. Start the watcher, query current errors, or get a full scan.

Actions:

  • "start": Begin watching a project directory for file changes

  • "stop": Stop the watcher

  • "status": Get current watcher state and recent errors

  • "scan": Run a one-time full diagnostic scan (no persistent watching)

Works with: TypeScript, Swift, Python, ESLint, and any language with a CLI linter. Platform-agnostic: works with any editor that saves to disk.

Inputs: action (required), path, file.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    A3.8/5.0

    Scored across 8 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose: auto-fixing errors, generating summaries, running diagnostics, reading files, getting git context, learning progress, project scanning, and watching diagnostics. No overlap or ambiguity.

    Naming Consistency5/5

    All tool names use consistent snake_case with a verb_noun pattern (e.g., auto_fix, get_diagnostics, scan_project). No mixed conventions or vague names.

    Tool Count5/5

    8 tools is well-scoped for a coding assistant with pet features. Each tool serves a specific function without redundancy, covering diagnostics, file access, git, learning, and automation.

    Completeness4/5

    The tool set covers core functionalities: diagnostics, file reading, git context, learning progress, project scanning, and auto-fix. Minor gaps like user interaction or manual notes, but overall sufficient for the domain.

    Maintenance

    ActivityInactive
    ResponsivenessNo recent community issues

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