codepet-mcp-server
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@codepet-mcp-server What am I working with?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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 build2. 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 inspectOpens 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 |
| Detect languages, frameworks, structure, and dependencies |
| Recent commits, diffs, branch info, activity patterns |
| Compiler errors, lint warnings, error→fix tracking |
Resources
URI | Description |
| Cached project fingerprint |
| Pet name, level, streak, skills |
| Contextual learning missions |
Phase 2 — Learning & Skill Mapping
Tools
Tool | Description |
| Read file with language detection, line ranges, and metadata |
| Aggregated learning progress: error patterns, streaks, skill tree XP, recommendations |
Resources
URI | Description |
| 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 |
| LLM-powered daily summary with stats, narrative, and in-character pet reaction |
Resources
URI | Description |
| Today's daily summary (call |
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
@EnvironmentObjectthroughout 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 buildAdd 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 dataRoadmap
Phase 1:scan_project,get_git_context,get_diagnosticstools + resources✅Phase 2:get_file_content,get_learning_contexttools + 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 toolsauto_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
Scored across 8 tools
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.
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.
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.
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.
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