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Bifrost-MCP Gateway

by david-spies
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Bifrost-MCP-Gateway

⚡ Bifrost-MCP Gateway

Enterprise-grade Dynamic MCP Proxy — eliminates Token Bloat & Schema Stuffer Fatigue in Agentic AI pipelines by lazy-loading tool schemas on semantic intent.

The Problem

When coding agents like Claude Code, Cursor, or Gemini CLI connect to multiple MCP servers, every single tool schema (names, descriptions, JSON argument definitions) gets dumped into the model's context window on every single turn. Teams running 20+ servers routinely waste 50,000–100,000 tokens per turn just reading tool catalogs before processing the actual query.

Bifrost eliminates this by acting as a semantic proxy between your client and your backend tools.

Architecture

[Claude Code / Gemini CLI]
        │  Single endpoint — only 2 meta-tools registered
        ▼
 ┌─────────────────────┐
 │   Bifrost Gateway   │  ◄── Semantic intent router
 │    (FastAPI 0.115)  │       Lazy-loads schemas on demand
 └──────────┬──────────┘
            │
     /mcp/tools/resolve  ←── intent keyword
            │
   ┌────────┼─────────┬────────────┐
   ▼        ▼         ▼            ▼
[Filesystem] [DB] [GitHub API] [Slack/CI/CD…]

How It Works

Phase

What Happens

Token Cost

1. Bootstrap

Gateway registers 2 meta-tools with the agent

~200 tokens

2. On-Demand

Agent fires request_tool_schemas("github") → only GitHub schemas injected

~150 tokens

3. Orchestrate

Agent pushes a multi-step Python block via execute_orchestration_script

0 round-trips

Naive approach

All 20+ raw schemas dumped every turn

~8,000+ tokens

Typical savings: 70–90% context reduction per turn.

Quickstart

1. Install

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

2. Run

python gateway.py
# Gateway live at http://127.0.0.1:8000
# Dashboard at  http://127.0.0.1:8000/
# API docs at   http://127.0.0.1:8000/docs

3. Connect your MCP client

Point Claude Code or any MCP-compatible agent to:

http://127.0.0.1:8000/mcp/init

The agent receives only 2 meta-tools instead of your full registry.

API Reference

Method

Path

Description

GET

/mcp/init

Bootstrap handshake — returns compressed catalog + 2 meta-tools

POST

/mcp/tools/resolve

Lazy-load full schemas matching an intent keyword

POST

/mcp/tools/orchestrate

Execute a sandboxed Python script client-side

GET

/mcp/registry

List all registered tools

POST

/mcp/registry/add

Register a new tool at runtime

DELETE

/mcp/registry/{name}

Remove a tool

GET

/analytics

Live gateway metrics

GET

/health

Health probe

Example: On-Demand Schema Resolution

curl -X POST http://127.0.0.1:8000/mcp/tools/resolve \
  -H "Content-Type: application/json" \
  -d '{"user_intent": "github pull request"}'

Response:

{
  "intent": "github pull request",
  "matched_tools": [
    {
      "name": "create_github_pull_request",
      "description": "Creates a new pull request...",
      "input_schema": { ... }
    }
  ],
  "match_count": 1,
  "token_audit": {
    "full_registry_tokens": 2100,
    "hydrated_tokens": 180,
    "tokens_saved": 1920,
    "reduction_pct": 91.4
  }
}

Example: Orchestration Script

curl -X POST http://127.0.0.1:8000/mcp/tools/orchestrate \
  -H "Content-Type: application/json" \
  -d '{"script": "import json\nresult={\"status\":\"ok\"}\nprint(json.dumps(result))"}'

Adding Tools to the Registry

Edit config.json and restart, or POST at runtime:

curl -X POST http://127.0.0.1:8000/mcp/registry/add \
  -H "Content-Type: application/json" \
  -d '{
    "name": "search_vector_store",
    "category": "RAG",
    "description": "Semantic search over the enterprise knowledge base.",
    "schema": {
      "type": "object",
      "properties": {
        "query":   {"type": "string"},
        "top_k":   {"type": "integer"}
      },
      "required": ["query"]
    }
  }'

Security Model (OWASP Agentic Top 10 Mitigations)

The orchestration endpoint enforces a multi-layer security filter before executing any script:

Forbidden Pattern

Why

rm -rf

Filesystem destruction

os.system()

Raw shell execution

subprocess.Popen()

Arbitrary process spawning

shutil.rmtree()

Directory removal

eval() / exec()

Dynamic code injection

__import__()

Runtime import hijacking

socket.

Network exfiltration

ctypes

Native memory access

open(..., 'w')

Filesystem writes

importlib

Module system abuse

Scripts also run with a 15-second hard timeout to prevent agent hang-loops.

Running Tests

pip install pytest httpx
pytest tests/ -v

Project Structure

bifrost-mcp/
├── gateway.py          # Core FastAPI application
├── config.json         # Tool registry (20 enterprise tools)
├── requirements.txt    # Minimal dependencies
├── static/
│   └── index.html      # Live monitoring dashboard
└── tests/
    └── test_gateway.py # Full test suite

Tech Stack

Layer

Technology

Rationale

Backend

Python 3.10+ / FastAPI

Async, minimal, zero-overhead

Matching

Keyword tokenization

No vector DB — instant startup

Orchestration

Python subprocess (sandboxed)

Zero external runtime

Dashboard

Vanilla HTML/CSS/JS

Zero build step, instant load

Deployment

Single python gateway.py

Frictionless, no Docker needed

Roadmap

  • Semantic vector matching (optional sentence-transformers mode)

  • Per-client token budgets & rate limiting

  • Streaming SSE event bus for real-time dashboard

  • Docker + Kubernetes manifests

  • OAuth2 / API key authentication layer

  • Plugin interface for custom tool backends

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