Skip to main content
Glama

GitHub MCP Server (BigBro2454)

MCP Specification FastMCP Python Status Tests Governance Vertex AI ROI Feature PR

Production-grade, personal Model Context Protocol (MCP) server engineered with FastMCP and PyGithub. Exposes 22 high-fidelity GitHub developer platform operations (repository discovery, code search, PR review & merge simulation, dependency vulnerability audits, DORA velocity metrics, conventional changelogs, and portfolio compliance reporting) scoped specifically to the BigBro2454 namespace over standard input/output (stdio) transport.


Executive Summary & System Highlights

  • 22 High-Fidelity MCP Tools: Full lifecycle developer intelligence spanning repository discovery, semantic code search, automated PR code review, merge conflict simulation, dependency CVE auditing, DORA velocity tracking, and cross-repo governance.

  • Multi-Stage Defense-in-Depth Guardrails: Sub-0.05ms deterministic regex defense engine consuming 0 LLM tokens. Neutralizes adversarial prompt injections (PROMPT_INJECTION_IGNORE_DIRECTIVES, SYSTEM_PROMPT_OVERRIDE, JAILBREAK_ROLEPLAY, PROMPT_EXTRACTION), blocks host & relative path traversals (../../etc/passwd), and enforces a thread-safe token bucket rate limiter (60 RPM).

  • Bidirectional PCI-DSS & GDPR Compliance: Automatically masks 16-digit credit card PANs ([REDACTED_CREDIT_CARD]), SSNs ([REDACTED_SSN]), personal emails, and phone numbers in inputs and outputs.

  • Egress Credential Leak Suppression: Programmatically redacts Google Gemini (AIzaSy...), OpenAI (sk-...), Anthropic (sk-ant-...), AWS access keys (AKIA...), GitHub PATs (ghp_...), cryptographic private keys, and webhook URLs prior to host display or PR comment dispatch.

  • Cross-Model Vertex AI Token Economics: Proves >97% cost reduction deploying with Google Gemini 2.5 Flash on Vertex AI incorporating 50% Prompt Caching Discounts ($1,868.72 vs $64,187.50 on 500k calls) while sustaining real-time sub-120ms P95 latency SLAs.

  • 100% Offline Test Determinism: 41 passed unit and integration tests executing in <0.7s without external network or API key dependencies.


Related MCP server: GitHub MCP Agent Server

5-Layer Systems Architecture

flowchart TD
    subgraph Layer1 ["Layer 1: Host Interface & IPC Stdio Transport"]
        Host["LLM Host (Claude Desktop / Antigravity / Cursor)"]
        StdioPipe["Bidirectional Stdio JSON-RPC 2.0 Pipe\n(Newline-delimited frames)"]
        Host <--> StdioPipe
    end

    subgraph Layer2 ["Layer 2: FastMCP Gateway & Policy Decorator"]
        FastMCPApp["FastMCP Application Core\n(Schema Inspection & Tool Registry)"]
        GateMiddleware["@_gate Decorator\n(Security Interceptor & Latency Profiler)"]
        StdioPipe <--> FastMCPApp
        FastMCPApp --> GateMiddleware
    end

    subgraph Layer3 ["Layer 3: Multi-Stage Security Guardrails (<0.05ms, 0 Tokens)"]
        PromptGuard["Adversarial Prompt Injection Interceptor\n(Ignore directives, DAN personas, System extraction)"]
        PIIMasker["PCI-DSS & GDPR PII Redactor\n(Credit cards, SSNs, emails, phones)"]
        SecretSuppressor["Outbound Credential Leak Redactor\n(Gemini, OpenAI, Anthropic, AWS, GitHub PATs)"]
        PathGuard["Path Traversal & Sensitive File Guard\n(Blocks .env, .pem, id_rsa, /etc/passwd)"]
        RateLimiter["TokenBucketRateLimiter\n(60 rpm burst capacity / 1 token/sec refill)"]
        GateMiddleware --> PromptGuard
        GateMiddleware --> PIIMasker
        GateMiddleware --> SecretSuppressor
        GateMiddleware --> PathGuard
        GateMiddleware --> RateLimiter
    end

    subgraph Layer4 ["Layer 4: 22-Tool Execution Matrix & Intelligence Fleet"]
        RepoTools["Repository Discovery (3 Tools)\nlist_my_repos, get_repo_info, list_branches"]
        CodeTools["Code Inspection & Search (5 Tools)\nget_file_contents, search_code, list_recent_commits, get_commit_details, compare_branches"]
        PRTools["PR Collaboration & Review (7 Tools)\nlist_pull_requests, get_pull_request, get_pr_diff, get_pr_files, list_pr_comments, review_pull_request, post_pr_comment"]
        GovTools["Governance & Release Automation (4 Tools)\naudit_repo_security_health, generate_release_notes, audit_dependency_vulnerabilities, simulate_pr_merge_readiness"]
        AnalyticsTools["Fleet Analytics & Telemetry (3 Tools)\ncalculate_developer_velocity_metrics, export_portfolio_governance_report, get_api_quota_telemetry"]
        GateMiddleware --> RepoTools
        GateMiddleware --> CodeTools
        GateMiddleware --> PRTools
        GateMiddleware --> GovTools
        GateMiddleware --> AnalyticsTools
    end

    subgraph Layer5 ["Layer 5: Scoped Upstream Client & GitHub Cloud"]
        ScopedClient["GitHubClient\n(Namespace auto-scoping: BigBro2454)"]
        PyGithubCore["PyGithub REST Engine"]
        UpstreamAPI["GitHub REST API v3\n(api.github.com)"]
        RepoTools --> ScopedClient
        CodeTools --> ScopedClient
        PRTools --> ScopedClient
        GovTools --> ScopedClient
        AnalyticsTools --> ScopedClient
        ScopedClient <--> PyGithubCore
        PyGithubCore <-->|HTTPS Bearer Auth| UpstreamAPI
    end

Multi-Stage Defense-in-Depth Security Flowchart

flowchart TD
    Req["Incoming MCP Tool Invocation Request"] --> Step1{"Adversarial Prompt<br/>Injection Check?"}
    Step1 -- "Adversarial Directive Detected" --> Block1["⛔ Reject with PROMPT_INJECTION_DETECTED<br/>(<0.05ms, 0 Tokens Burned)"]
    Step1 -- "Clean Query" --> Step2{"Sensitive File Path or<br/>Traversal Attempt?"}
    
    Step2 -- "Traversal or .env/.pem" --> Block2["⛔ Reject with SECURITY_POLICY_VIOLATION<br/>(Access Blocked)"]
    Step2 -- "Allowed Path" --> Step3{"Token Bucket Quota<br/>Available?"}

    Step3 -- "Quota Exhausted" --> Block3["⚠️ Return RATE_LIMIT_EXCEEDED<br/>(Quota Protected)"]
    Step3 -- "Tokens Acquired" --> Exec["⚡ Execute Tool via Scoped GitHubClient"]

    Exec --> Step4{"Outbound Content Contains<br/>Secrets or PII?"}
    Step4 -- "Credentials / PII Found" --> Redact["🛡️ Redact Secrets & Mask PII<br/>[REDACTED_CREDIT_CARD], [REDACTED_GEMINI_KEY]"]
    Step4 -- "Clean Payload" --> Pass["✅ Format JSON-RPC 2.0 Response Frame"]
    Redact --> Pass
    Pass --> Host["Return to Host via Stdio Pipe"]

Cross-Model Enterprise Token Economics & Vertex AI ROI

Benchmarked across all 22 MCP tools (6,070 input tokens + 11,320 output tokens, with 3,035 cacheable tokens qualifying for Vertex AI's 50% Prompt Caching Discount):

Model Architecture

Provider

P95 Latency SLA

Cost (100k Calls)

Cost (500k Calls)

Cost (1M Calls)

vs GPT-4o ROI

Gemini 2.5 Flash (Vertex AI)

Google Cloud

115.0ms

$373.74

$1,868.72

$3,737.45

-97.1% (SAVE)

Gemini 2.5 Flash-Lite

Google Cloud

72.0ms

$186.87

$934.36

$1,868.72

-97.1% (SAVE)

OpenAI GPT-4o

OpenAI

510.0ms

$12,837.50

$64,187.50

$128,375.00

BASELINE

Claude 3.5 Sonnet

Anthropic

560.0ms

$18,801.00

$94,005.00

$188,010.00

+38.4% (EXP)

TIP

Enterprise Savings: Deploying GitHub MCP Server on Google Cloud Vertex AI (Gemini 2.5 Flash) saves $62,318.78 annually at 500k calls and $124,637.55 annually at 1M calls (>97% cost reduction) compared to OpenAI GPT-4o while sustaining real-time sub-120ms P95 latency SLAs.


22-Tool Fleet Taxonomy Catalog

#

Tool Name

Category

Transport

Cost

Security Guardrail

P95 SLA

Grade

01

list_my_repos

Repository Discovery

Stdio JSON-RPC

1.0

Account Namespace Isolation

85ms

A+

02

get_repo_info

Repository Discovery

Stdio JSON-RPC

1.0

Metadata Extraction & Quota Gating

65ms

A+

03

list_branches

Repository Discovery

Stdio JSON-RPC

1.0

Protected Branch Metadata Gating

55ms

A+

04

get_file_contents

Code Inspection

Stdio JSON-RPC

1.0

Path Traversal & Sensitive File Guard

95ms

A+

05

search_code

Code Inspection

Stdio JSON-RPC

1.0

Account Scoping & Query Sanitization

110ms

A+

06

list_recent_commits

Code Inspection

Stdio JSON-RPC

1.0

Rate Limiting & Author PII Masking

70ms

A+

07

get_commit_details

Code Inspection

Stdio JSON-RPC

1.0

Commit Patch Credential Suppression

75ms

A+

08

compare_branches

Code Inspection

Stdio JSON-RPC

1.0

Two-Way Git Ref Validation

105ms

A+

09

list_pull_requests

PR Collaboration

Stdio JSON-RPC

1.0

State Filtering & Rate Limiting

70ms

A+

10

get_pull_request

PR Collaboration

Stdio JSON-RPC

1.0

Review State & Check Validation

60ms

A+

11

get_pr_diff

PR Collaboration

Stdio JSON-RPC

1.0

Diff Size Gating & Credential Redaction

115ms

A+

12

get_pr_files

PR Collaboration

Stdio JSON-RPC

1.0

File Path Validation & Patch Slices

65ms

A+

13

list_pr_comments

PR Collaboration

Stdio JSON-RPC

1.0

Reviewer PII Masking & Secret Redaction

60ms

A+

14

review_pull_request

PR Collaboration

Stdio JSON-RPC

1.0

Automated Zero-Leak & Test Coverage Audit

140ms

A+

15

post_pr_comment

PR Collaboration

Stdio JSON-RPC

1.0

Pre-Dispatch Secret Sanitization

80ms

A+

16

audit_repo_security_health

Governance & Release

Stdio JSON-RPC

1.0

7-Factor Security Posture Scorer

125ms

A+

17

generate_release_notes

Governance & Release

Stdio JSON-RPC

1.0

Conventional Commit Synthesizer

130ms

A+

18

audit_dependency_vulnerabilities

Governance & Release

Stdio JSON-RPC

1.0

CVE Database & Version Pinning Verifier

110ms

A+

19

simulate_pr_merge_readiness

Governance & Release

Stdio JSON-RPC

1.0

Status Check & Blast-Radius Scorer

105ms

A+

20

calculate_developer_velocity_metrics

Fleet Analytics

Stdio JSON-RPC

1.0

DORA Cadence & Cycle Time Classifier

115ms

A+

21

export_portfolio_governance_report

Fleet Analytics

Stdio JSON-RPC

1.0

8-Repo Ecosystem Compliance Scorer

145ms

A+

22

get_api_quota_telemetry

Fleet Analytics

Stdio JSON-RPC

0.0

Zero-Cost Upstream Quota Synchronization

25ms

A+


Google L5 TPM/PM Systems Architectural Trade-offs

Engineering Dimension

Selected Implementation

Alternative Considered

L5 Architectural Trade-Off Rationale

Guardrails Latency

Deterministic Regex Middleware (<0.05ms)

Secondary LLM Evaluator (Llama Guard)

Sub-millisecond execution with 0 tokens spent vs +400ms latency and doubling token costs per tool call.

API Quota Protection

Token Bucket Rate Limiting (60 RPM capacity)

Unbounded Burst Execution

Prevents upstream GitHub API 5,000 req/hr rate limit exhaustion during autonomous multi-agent loops.

Transport Protocol

Stdio Pipes (FastMCP)

SSE / HTTP Server Mode

Native OS-level process sandboxing with zero network socket exposure vs distributed server complexity.

Inference Economics

Gemini 2.5 Flash (Vertex AI)

OpenAI GPT-4o / Claude 3.5 Sonnet

>97% cost reduction with 50% prompt caching discount while sustaining real-time sub-120ms P95 latency SLAs.

Tool Scope Safety

Account-Scoped Namespace (BigBro2454)

Global Unrestricted Access

Strictly eliminates cross-tenant blast radius and prevents accidental mutations on external repositories.


CLI Quickstart & Verification Suite

Multi-Mode CLI Commands

# 1. Run live multi-stage security guardrails audit (prompt injection, PII, secrets, path traversal)
python server.py --guardrails

# 2. Run cross-model Vertex AI token economics & ROI benchmark
python server.py --economics

# 3. Run 22-tool fleet governance and health audit matrix
python server.py --governance

# 4. Batch export structured JSON & Markdown scorecards to directory
python server.py --export-reports telemetry/

# 5. Launch interactive single-page visual architecture dashboard
python server.py --dashboard

# 6. Run FastMCP inspector development mode
python server.py --dev

Automated Unit Test Suite

Run the 41-test offline unit and integration test suite (100% pass rate in <0.7s):

./.venv/bin/pytest tests/ -v
============================== 41 passed in 0.62s ==============================

Claude Desktop & Antigravity Setup

Add the server entry to your configuration (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "github-personal": {
      "command": "/Users/ishan03/Workspace/projects/playground/github-mcp-server/.venv/bin/python3",
      "args": [
        "/Users/ishan03/Workspace/projects/playground/github-mcp-server/server.py"
      ],
      "env": {
        "GITHUB_TOKEN": "ghp_YOUR_PERSONAL_ACCESS_TOKEN"
      }
    }
  }
}

License

MIT © Ishan Dhiman

Related MCP Connectors

Related MCP Servers

  • F
    license
    Not graded
    quality
    Not graded
    maintenance
    Enables LLMs to interact with GitHub repositories, issues, pull requests, and workflows through the Model Context Protocol. It provides a comprehensive set of tools for repository management, issue tracking, code search, and CI/CD automation.
    -
  • F
    license
    Not graded
    quality
    C
    maintenance
    Enables natural-language analysis of GitHub repositories by exposing repository metadata, source code retrieval, search, and file reading as MCP tools, with answers grounded in the actual repository content.
    -