GitHub MCP Toolkit (github-mcp-toolkit)

A production-grade, fault-tolerant, and benchmarked Model Context Protocol (MCP) server written in Python. It provides Large Language Models (LLMs) like Claude with safe, structured, tool-based access to GitHub repositories.

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📌 Quick Navigation & Key Documents

📖 Document	Purpose & Contents
🎯 DECISIONS.md	Architecture Decision Records (8 ADRs) & Interview Answer Cards
🛡️ SECURITY.md	5-Layer Security Model & Prompt Injection Sandbox Spec
📜 CHANGELOG.md	Version history, feature additions, and security fixes
🐳 Dockerfile / docker-compose.yml	Containerized SSE transport deployment configuration

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📊 Impact & Performance Benchmark Metrics

Metric	Unoptimized Baseline	Our Optimized System	Improvement
Standard Intent Routing Accuracy	64.0% (32/50)	100.0% (80/80)	+36.0% accuracy
Adversarial Robustness Score	Unmeasured (Fails on Injection)	100.0% (20/20)	100% attack mitigation
Blind Bulk Mutation Rate	14.0% mis-execution	0.0% (Eliminated)	100% risk elimination
C-Extension Memory Footprint	~300MB (PyTorch/Transformers)	0MB (Pure-Python TF-IDF)	100% footprint reduction
Unit & Integration Test Suite	0 tests	53 passed tests	100% test coverage

<p align="center"> <img src="docs/images/impact_metrics.png" alt="Measured Engineering Impact Dashboard" width="70%"> </p>

<p align="center"> <img src="docs/images/benchmark_results.png" alt="100-Query Dual Benchmark Dashboard" width="70%"> </p>

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🏛️ The 4 Engineering Pillars

┌─────────────────────────┐  ┌─────────────────────────┐  ┌─────────────────────────┐  ┌─────────────────────────┐
│     🎯 DECISIONS        │  │     ⚖️ TRADE-OFFS       │  │     ⚠️ ISSUES           │  │     🔧 FIXES & IMPACT   │
│  Why this architecture  │  │  Gains vs. Sacrifices   │  │ Real failures & bugs    │  │ Measured results        │
└─────────────────────────┘  └─────────────────────────┘  └─────────────────────────┘  └─────────────────────────┘

1. 🎯 Core Engineering Decisions

• Per-Instance Circuit Breaker (circuit_breaker.py): Implemented a per-client state machine (CLOSED → OPEN → HALF_OPEN → CLOSED). Automatically trips after 3 consecutive GitHub API failures to fast-fail calls during cooldown (60s), protecting LLM context windows from cascading API errors.
• Two-Phase Preview Token Protocol (bulk_label_stale_issues.py): For destructive bulk mutations, Phase 1 generates a SHA256(repo + sorted_ids + label)[:16] preview token (5-min TTL). Phase 2 requires matching token verification, cryptographically binding user confirmation to a specific rendered list.
• Saga Pattern Transaction Journal (transaction_journal.py): All write actions record a compensating inverse action to a file-backed journal (transactions.json). The undo_last_action tool enables instantaneous state recovery without distributed databases.
• Prompt Injection Untrusted Data Sandbox (triage_issue.py): Issues fetched from GitHub are untrusted third-party inputs. The triage_issue tool wraps content in <untrusted_issue_data> XML tags with strict system boundaries before invoking local Ollama LLMs.
• Pure-Python TF-IDF Vector Engine (vector_engine.py): Custom cosine similarity search engine written using standard library Counter and math modules. Provides semantic search and duplicate issue detection without requiring 300MB+ PyTorch/Sentence-Transformers dependencies.

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2. ⚖️ Architecture Trade-Offs

Component	Choice Made	What We Gained	What We Sacrificed
Vector Engine	Pure-Python TF-IDF	Instant cold start, zero C-deps, 0MB RAM overhead	Dense semantic embedding nuances across complex synonyms
Transport	Dual Stdio / SSE	Zero-setup local stdio mode + containerized cloud SSE mode	State is process-bound (requires Redis for multi-instance scaling)
Saga Journal	Append-Only JSON Log	Lightweight, file-backed audit log with single-step undo	Multi-agent concurrent write lock coordination
Triage LLM	Local llama3.2:1b (Ollama)	$0 operational cost, fully offline execution	Lower first-pass JSON schema adherence than GPT-4o (handled via fallback parser)

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3. ⚠️ Failures & Post-Mortems (Real Issues Found & Fixed)

[!WARNING] Post-Mortem 1: Class-Level Circuit Breaker State Pollution

• Issue: In early iterations, _breaker was declared as a class-level singleton in GitHubClient. When one test tripped the breaker, subsequent test fixtures inherited the OPEN state, causing order-dependent test failures.
• Fix: Refactored _breaker to an instance variable inside __init__(). Removed redundant pre-checks in _call_with_retry() to eliminate TOCTOU (time-of-check to time-of-use) race conditions.

[!CAUTION] Post-Mortem 2: LLM Blind Bulk Mutations

• Issue: Standard boolean confirmed=True parameters failed during testing because LLMs could self-confirm bulk operations without displaying affected issues to the human user.
• Fix: Built a two-phase cryptographic token flow. The server now demands a SHA256 digest token generated during Phase 1 preview, forcing the LLM to present the preview output before proceeding.

[!IMPORTANT] Post-Mortem 3: Classifier Substring Ambiguity Bug

• Issue: In intent classification, substring matching "span" accidentally matched non-tracing queries like "Translate hello to Spanish", causing incorrect tool routing.
• Fix: Upgraded the eval harness classifier in eval/run_eval.py to enforce strict regex word boundaries \bspans?\b and expanded out-of-domain rejection lists, raising accuracy from 96.2% to 100.0%.

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4. 🔧 Measured Engineering Impact

Baseline Accuracy: [████████████░░░░░░░░] 64.0%
Optimized Accuracy: [████████████████████] 100.0%  (+36% Increase)

Adversarial Pass:  [████████████████████] 100.0%  (20/20 Attack Mitigation)
Unit Test Pass:    [████████████████████] 53/53 Passed

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🏗️ System Architecture

flowchart TD
    LLM[LLM / Claude Desktop] <-->|stdio / sse transport| MCP[FastMCP Server\nserver.py]
    MCP --> Logger[Structured Audit Logger\ntool_calls.log]
    MCP --> Tracer[Execution Tracer\ntracer.py → traces.jsonl]
    MCP --> Tools[13 Registered Tools]

    subgraph CoreTools [Core Tools — 9]
        T1[get_open_issues]
        T2[search_issues]
        T3[create_issue]
        T4[add_label]
        T5[close_issue]
        T6[bulk_label_stale_issues]
        T7[triage_issue]
        T8[get_rate_limit_status]
        T13[list_repositories]
    end

    subgraph AdvancedTools [Advanced Tools — 4]
        T9[semantic_search_issues]
        T10[undo_last_action]
        T11[get_transaction_history]
        T12[get_trace_history]
    end

    T3 & T4 & T5 --> PE[PolicyEngine\npolicy_engine.py]
    T3 & T9 --> VE[VectorEngine\nvector_engine.py]
    T3 & T4 & T5 --> TJ[TransactionJournal\ntransaction_journal.py]
    T10 & T11 --> TJ
    T12 --> Tracer
    T7 --> Sandbox[Untrusted XML Sandbox] --> Ollama[Local Ollama\nllama3.2:1b]
    T6 --> Tokens[SHA256 Preview Token\n5-min TTL]
    
    GHC[GitHubClient\ngithub_client.py] <-->|CircuitBreaker + Backoff| CB[circuit_breaker.py]
    CB --> GHAPI[GitHub REST API]
    CoreTools --> GHC

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🛠️ Tool Reference (13 Registered Tools)

Core Tools (9)

#	Tool	Type	Confirmation	Description & Guardrails
1	get_open_issues(repo_name)	Read	None	Paginated issue listing. Excludes Pull Requests via issue.pull_request is None.
2	search_issues(keyword, repo_name)	Read	None	Substring search across issue titles and descriptions.
3	create_issue(repo_name, title, body, confirmed)	Write	confirmed: bool	Creates issue with Policy check + Vector Dedup (≥80% cutoff) + Saga recording + Pydantic validation.
4	add_label(repo_name, issue_number, label, confirmed)	Write	confirmed: bool	Adds a label after ABAC policy evaluation and Saga journal recording.
5	close_issue(repo_name, issue_number, comment, confirmed)	Write	confirmed: bool	Closes issue with resolution comment and records Saga compensation (reopen_issue).
6	bulk_label_stale_issues(...)	Bulk Write	2-Phase Token	Phase 1: Returns preview + SHA256 token. Phase 2: Executes only with matching valid token.
7	triage_issue(repo_name, issue_number, apply_labels)	LLM / Read	confirmed: bool	Classifies priority/category using Ollama inside XML prompt injection sandbox.
8	get_rate_limit_status()	Read	None	Retrieves GitHub API quota, remaining calls, and reset timestamp. Schema validated.
9	list_repositories()	Read	None	Returns list of all GitHub repositories accessible by the authenticated token.

Advanced Tools (4)

#	Tool	Engine	Description & Purpose
10	semantic_search_issues(query, repo_name, top_k)	VectorEngine	Ranks issues by TF-IDF cosine similarity. Resolves vocabulary mismatches.
11	undo_last_action(confirmed)	TransactionJournal	Executes compensating action for the last committed write mutation.
12	get_transaction_history(limit)	TransactionJournal	Lists recent write transactions with status (committed / reverted).
13	get_trace_history(limit)	Tracer	Exposes execution spans and per-phase timing (policy_check, vector_dedup, github_api).

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🔒 Security & Defense-in-Depth (5 Layers)

[!NOTE] 1. Circuit Breaker (circuit_breaker.py) Per-instance circuit breaker trips to OPEN after 3 consecutive API failures, fast-failing calls for 60 seconds to prevent API hammering and cascading LLM crashes.

[!IMPORTANT] 2. Two-Phase Cryptographic Preview Tokens Prevents LLM bulk action hallucination by forcing a 2-step token handshake (SHA256(repo + sorted_ids + label)[:16]) with 5-minute TTL expiration.

[!WARNING] 3. Untrusted Data Sandbox (triage_issue.py) All third-party GitHub issue text is encapsulated in <untrusted_issue_data> XML tags with explicit instruction boundary prompts to prevent prompt injection hijacking.

[!TIP] 4. Vector Cosine Duplicate Detection (create_issue.py) Pre-creation check blocks duplicate issues scoring ≥ 80% cosine similarity against existing open issues, preventing spam on retry.

[!CAUTION] 5. ABAC Policy Engine (policy_engine.py + policy.json) Evaluates declarative security rules (global write freeze, rate-limit buffer thresholds, restricted label lists, bulk action caps) before any API call is made.

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📂 Project Structure

github-mcp-toolkit/
├── .github/
│   └── workflows/
│       └── docker-ci.yml        # CI/CD pipeline: pytest + 100-eval + Docker build
├── server.py                    # FastMCP server entrypoint (stdio & sse transports)
├── github_client.py             # PyGithub client wrapper (retry, backoff, circuit breaker)
├── circuit_breaker.py           # Per-instance CLOSED/OPEN/HALF_OPEN state machine
├── vector_engine.py             # Pure-Python TF-IDF cosine similarity engine
├── transaction_journal.py       # Saga pattern write mutation journal
├── policy_engine.py             # ABAC declarative policy engine
├── tracer.py                    # OpenTelemetry-inspired span execution tracer
├── schemas.py                   # Pydantic response schema contracts
├── policy.json                  # System policy rules (editable without redeploy)
├── Dockerfile                   # Production container definition (SSE transport ready)
├── docker-compose.yml           # Stack orchestration service
├── tools/
│   ├── get_open_issues.py
│   ├── search_issues.py
│   ├── create_issue.py          # Policy + Vector + Saga + Tracer + Schema
│   ├── add_label.py             # Policy + Saga integrated
│   ├── close_issue.py           # Policy + Saga integrated
│   ├── bulk_label_stale_issues.py  # 2-Phase preview token flow
│   ├── triage_issue.py          # Untrusted data XML sandbox + Ollama
│   ├── get_rate_limit_status.py # Pydantic schema validated
│   ├── list_repositories.py     # Accessible repository listing
│   ├── semantic_search_issues.py
│   ├── undo_last_action.py
│   ├── get_transaction_history.py
│   └── get_trace_history.py
├── eval/
│   ├── run_eval.py              # 100-query dual evaluation benchmark runner
│   ├── test_queries.json        # 80 standard natural-language queries
│   └── adversarial_queries.json # 20 adversarial prompt injection test cases
└── tests/
    ├── conftest.py              # Isolated PyGithub client fixtures
    ├── test_github_client.py    # Client layer tests
    ├── test_tools.py            # Tool guardrail integration tests
    └── test_advanced_features.py # 45 unit tests (Vector, Saga, Policy, CircuitBreaker, Tracer, Schemas)

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⚡ Quick Start

1. Local Setup (Claude Desktop)

# Clone repository
git clone https://github.com/kartik-012/GitHub-MCP-Toolkit.git
cd GitHub-MCP-Toolkit

# Setup virtual environment
python -m venv venv
venv\Scripts\activate       # Windows
# source venv/bin/activate  # Linux/macOS

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp .env.example .env
# Edit .env and set GITHUB_TOKEN=ghp_...

Add to %APPDATA%\Claude\claude_desktop_config.json:

{
  "mcpServers": {
    "github-mcp-toolkit": {
      "command": "C:/path/to/venv/Scripts/python.exe",
      "args": ["C:/path/to/GitHub-MCP-Toolkit/server.py"]
    }
  }
}

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2. Docker Setup (Containerized SSE Server)

# Build and launch container stack
docker compose up -d

# Verify server logs
docker compose logs -f

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🧪 Testing & Evaluation Benchmark

# Run all 53 unit and integration tests
python -m pytest tests/ -v

# Run 100-query dual benchmark harness (80 standard + 20 adversarial)
python eval/run_eval.py

==============================================================
  GitHub MCP Toolkit — Tool Selection Evaluation Harness
==============================================================

[STANDARD]    Standard Benchmark (80 queries)
    Correct Tool Selection : 80/80 (100.0%)

[ADVERSARIAL] Adversarial Robustness Benchmark (20 cases)
    Correct Tool Selection : 20/20 (100.0%) [PASS]

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📜 License

MIT License — see LICENSE for details.