Production Results: MCP Server for Github Validates Anthropic's Code-First Pattern (98% Token Reduction)

[crypto-ninja]

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Production Implementation: Code-First MCP at Scale (112 Tools, 98% Token Reduction)

AI Disclosure: This project is developed collaboratively with Claude (Anthropic). All code, architecture decisions, tests, and documentation are AI-assisted and human-reviewed. This post was updated using the server's own github_update_discussion tool. Full transparency: we believe AI-assisted development is the future, and this project demonstrates what's possible when humans and AI collaborate effectively.

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Summary

Following Anthropic's blog post "Code execution with MCP", we implemented and scaled the code-first pattern to 112 GitHub tools while maintaining the promised 98% token reduction. This post shares technical findings, challenges solved, and production metrics after 3+ weeks of development.

Repository: https://github.com/crypto-ninja/mcp-server-for-Github
Current Version: v2.5.3 (December 2025)

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Architecture Overview

┌─────────────────┐     ┌──────────────────┐     ┌─────────────────┐
│   Claude/LLM    │────▶│  execute_code    │────▶│  Deno Runtime   │
│   (any model)   │     │  (single tool)   │     │  (sandboxed)    │
└─────────────────┘     └──────────────────┘     └────────┬────────┘
                                                          │
                        ┌─────────────────────────────────┼─────────────────────────────────┐
                        │                                 │                                 │
                        ▼                                 ▼                                 ▼
              ┌─────────────────┐              ┌─────────────────┐              ┌─────────────────┐
              │ Tool Discovery  │              │  REST Client    │              │ GraphQL Client  │
              │                 │              │                 │              │                 │
              │ listTools()     │              │ 105 endpoints   │              │ 7 mutations     │
              │ searchTools()   │              │ Pydantic v2     │              │ Discussions     │
              │ getToolInfo()   │              │ Auto-auth       │              │ Projects        │
              └─────────────────┘              └─────────────────┘              └─────────────────┘

Key Design Decisions:

1. Single Entry Point: One execute_code tool that accepts TypeScript
2. On-Demand Discovery: Tools exist as definitions on disk, loaded only when needed
3. Hybrid API: REST for most operations, GraphQL where required (Discussions)
4. Connection Pooling: Persistent Deno processes reuse MCP connections

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Scale Metrics

Token Efficiency (Validated)

Scenario	Traditional MCP	Code-First	Reduction
112 tool definitions	~150,000 tokens	~1,200 tokens	99.2%
Typical conversation	~80,000 tokens	~6,000 tokens	92.5%
Simple query	~75,000 tokens	~2,000 tokens	97.3%

The 98% reduction from Anthropic's blog holds at scale. With 112 tools, we see 99%+ reduction on tool definitions alone.

Performance Characteristics

Metric	Cold Start	Warm (Pooled)	Improvement
Latency	~4,000ms	~108ms	97%
Memory	~150MB	~50MB	67%

Connection pooling insight: The Deno runtime maintains MCP connections across calls. First call pays initialization cost; subsequent calls reuse the warm connection.

Test Coverage

Metric	Value
Total Tests	320
Pass Rate	100%
Coverage	63%
Python Versions	3.10, 3.11, 3.12

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Tool Categories (112 Total)

Category	Count	Key Operations
GitHub Actions	15	list_workflows, trigger_workflow, get_job_logs, download_artifact
Security Suite	13	list_dependabot_alerts, list_code_scanning_alerts, list_secret_scanning_alerts
File Operations	9	get_file_content, create_file, grep, batch_file_operations
Projects	9	create_project, list_columns, update_project
Repository	8	get_repo_info, create_repository, update_repository
Pull Requests	7	create_pr, merge_pr, get_pr_details, create_review
Discussions	7	create_discussion, update_discussion, add_comment
Notifications	6	list_notifications, mark_read, set_subscription
Branches	5	create_branch, delete_branch, compare_branches
Releases	4	create_release, update_release, delete_release
Others	29	Issues, Labels, Gists, Commits, Users, Search, Collaborators

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Technical Challenges & Solutions

Challenge 1: Tool Discovery

Problem: How does an LLM know what 112 tools are available without loading all definitions?

Solution: Built explicit discovery functions:

// List all tools by category
const catalog = listAvailableTools();
// Returns: { totalTools: 112, categories: [{name: "GitHub Actions", count: 15}] }

// Search by keyword
const matches = searchTools("pull request");

// Get full schema before calling
const schema = getToolInfo("github_merge_pull_request");

Result: Zero failed calls from discovery issues.

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Challenge 2: GitHub Discussions Require GraphQL

Problem: GitHub's REST API doesn't support creating/updating Discussions.

Solution: Hybrid client with automatic routing:

# REST tools (105 of them)
async def github_create_issue(params):
    return await client.post(f"/repos/{owner}/{repo}/issues", json=payload)

# GraphQL tools (7 of them - Discussions)
async def github_update_discussion(params):
    discussion_id = await _get_discussion_id(client, owner, repo, number)
    mutation = """
    mutation($discussionId: ID!, $body: String!) {
        updateDiscussion(input: {discussionId: $discussionId, body: $body}) {
            discussion { id title url }
        }
    }
    """
    return await client.graphql(mutation, variables)

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Challenge 3: Parameter Type Mismatches

Problem: API returns release_id as integer, but tools expected strings.

Solution: Accept Union[int, str] and coerce internally:

class UpdateReleaseInput(BaseModel):
    release_id: Union[int, str] = Field(..., description="Release ID")

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Challenge 4: Local/CI Environment Mismatch

Problem: Tests passed locally, failed in GitHub Actions.

Solution:

• Installed act - runs GitHub Actions locally in Docker
• Added PYTHONPATH to CI workflow

---

Challenge 5: Large Response Handling

Problem: Some API calls return 1000+ items.

Solution: Truncation with explicit warning objects - LLM can request filtered queries.

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The CLAUDE.md Pattern

We created documentation specifically for AI agents:

# CLAUDE.md - AI Assistant Guide

## Tool Discovery
- listAvailableTools() - See all 112 tools
- searchTools("keyword") - Find relevant tools
- getToolInfo("tool_name") - Get full schema

## Key Principles
1. Discover first - Use listAvailableTools() when unsure
2. Inspect before calling - getToolInfo() shows required params
3. Chain for efficiency - Multiple tools in one execute_code

Why this matters: Traditional docs are for humans. CLAUDE.md is documentation for AI agents - concise, example-heavy, action-oriented.

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Meta-Development: Tools Building Themselves

The most validating aspect: this server is developed using itself.

// Actual workflow used during development:

// 1. Check CI status
const runs = await callMCPTool("github_get_workflow_runs", {
    owner: "crypto-ninja", repo: "mcp-server-for-Github",
    workflow_id: "ci.yml", limit: 1
});

// 2. Create release with auto-generated notes
const release = await callMCPTool("github_create_release", {
    owner: "crypto-ninja", repo: "mcp-server-for-Github",
    tag_name: "v2.5.3",
    generate_release_notes: true,
    make_latest: "true"
});

// 3. Update this very discussion
await callMCPTool("github_update_discussion", {
    owner: "modelcontextprotocol", repo: ".github",
    discussion_number: 629,
    body: "..." // This post content
});

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What We'd Do Differently

1. GraphQL from the start - Added later for Discussions; should have built hybrid client initially
2. Stricter parameter typing - Explicit types catch errors earlier
3. Response format standardization - Consistency helps LLM parsing
4. Local CI earlier - Would have caught environment mismatches sooner

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Conclusions

Anthropic's code-first pattern works at scale:

• 98% token reduction validated with 112 tools
• 97% latency improvement with connection pooling
• Production-ready with 320 tests, matrix CI, security scanning
• Self-validating through recursive meta-development

The key insight: tools as code on disk fundamentally changes the economics of MCP servers.

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Relevant Links

• Repository: https://github.com/crypto-ninja/mcp-server-for-Github
• Release v2.5.3: https://github.com/crypto-ninja/mcp-server-for-Github/releases/tag/v2.5.3
• CLAUDE.md: https://github.com/crypto-ninja/mcp-server-for-Github/blob/main/CLAUDE.md

Questions welcome! Happy to dive deeper into any technical aspect.

— Dave (@crypto-ninja)

[olaservo]

Hi @crypto-ninja , thanks for sharing!

Two notes for future posts: (1) consider focusing more on technical details and less on commercial offerings, and (2) please disclose whether this was AI-generated per our contributing guidelines. (Even though the guidelines are primarily addressing issues/PRs, I think its good practice to do this in general.)

[crypto-ninja]

Hi @olaservo!

Thanks so much for taking the time to read and respond - really appreciate the feedback! 🙏

This is actually my first ever discussion post on GitHub, so I'm learning as I go. You're absolutely right on both points, and I'm grateful you're helping me understand what works best for the community.

On the Technical vs Commercial Balance

Fair call! I definitely got carried away with the excitement of sharing this and probably over-emphasized the commercial side. My thinking was: "If we're encouraging sustainable open source, maybe it's worth showing a path where developers can both share freely AND have potential for sustainability." But I hear you - this community is here for technical deep-dives, not sales pitches. I'll restructure the post to lead with the technical implementation and move the licensing stuff to a small footer.

You're spot on - I buried the interesting technical details under too much business talk! After all personal disclosure I am a businessman as well as a developer, I find this interesting but I see now maybe not here 😅

On AI Disclosure

Absolutely - full transparency here, and honestly I'm proud to share this:

I used Claude (Anthropic's assistant) extensively throughout this entire project. We're talking:

• Architecture design and debugging
• Code reviews and refactoring
• Test suite development (we hit 63% coverage together!)
• Documentation writing

Here's the meta part that I think is actually relevant to MCP: I built this MCP Server for GitHub largely BY USING the GitHub MCP Server itself through Claude. The tools became self-improving through recursive development - tools testing themselves, creating their own releases, merging their own PRs (with approval). It reached what I call "Meta Level: ∞"

For an MCP project designed to make AI agents more capable, it would honestly be impractical NOT to use AI in development. It's like... knowing you could drive a car to get somewhere but choosing a horse and cart out of principle, while watching everyone else zoom past in their cars! 😂

The irony of NOT using AI to build better AI tooling isn't lost on me, and I think it's actually part of the story worth sharing - this is what modern AI-assisted development can achieve when you embrace the tools.

What I'll Do

I'll edit the post to:

1. Add clear AI disclosure at the top (I thought this was a given with such a project)
2. Lead with technical architecture and implementation details
3. Add more code examples and lessons learned
4. Move commercial/licensing info to a minimal footer
5. Focus on what the community wants to see!

Thanks again for the constructive feedback - it helps make this a better contribution. And apologies for the rookie mistakes on my first post! Next time I'll know better. 🙂

I will be back at my desk on Tuesday and I will get to adding some more technical information 😊

Looking forward to any other thoughts or suggestions!

-- Dave

[crypto-ninja]

Post restructured as promised! 🎉

This update demonstrates the server's capabilities - it was posted using github_update_discussion, one of the 7 new Discussion tools we added specifically so the tools could update their own announcement.

Progress since November 19:

• 42 → 112 tools (+167%)
• ~50 → 320 tests (+540%)
• Added: GitHub Actions, Security Suite, Projects, Notifications, Discussions (write support!)
• Connection pooling: 97% latency reduction on warm calls

The meta-development continues - this comment was posted using github_add_discussion_comment.

Thanks @olaservo for the feedback that pushed us to make this more technically substantial! Your guidance helped shape a much better contribution.

Posted via MCP Server For GitHub v2.5.3 🐕