MCP Gateway

Give your AI access to every tool it needs -- without burning your context window or building MCP servers.

Independent Reviews

• Five MCP hot-reload tools compared -- Ruach Tov Collective's BPD-based comparison of mcp-gateway against four restart-focused alternatives. Includes a feature matrix and architectural analysis.
• mcp-gateway deep dive -- Detailed walkthrough of the capability system, SHA-256 integrity pinning, and the v2.5-to-v2.9 development arc.

MCP Gateway sits between your AI client and your tools. Instead of loading hundreds of tool definitions into every request, the AI gets a compact Meta-MCP surface -- 14 tools minimum, 16 in the README benchmark scenario, 17 when webhook status is surfaced -- and discovers the right backend tool on demand.

Public quantitative claims in this README are sourced from docs/BENCHMARKS.md and the machine-readable benchmarks/public_claims.json, with CI checks to catch drift.

What MCP Gateway is / is not

MCP Gateway is a tool and capability gateway. It routes MCP tool/resource/prompt traffic to backend MCP servers and capability-backed REST APIs, and it can proxy MCP server-to-client requests like sampling/createMessage, elicitation/create, and roots/list back to the connected client over the existing gateway session.

MCP Gateway is not a general OpenAI/Anthropic chat completions or embeddings gateway. When a backend asks for sampling/createMessage, the connected client still performs the model call. The OpenAI-compatible prompt-cache helpers in the gateway exist only so gateway_invoke can preserve prompt_cache_key behavior for backends or capabilities that happen to call LLM APIs internally.

Why

The context window is the bottleneck. Every MCP tool you connect costs ~150 tokens of context overhead. Connect 20 servers with 100+ tools and you've burned 15,000 tokens before the conversation starts -- on tool definitions the AI probably won't use this turn.

Worse: context limits force you to choose which tools to connect. You leave tools out because they don't fit -- and your AI makes worse decisions because it can't reach the right data.

MCP Gateway removes that tradeoff entirely.

The base discovery quartet (gateway_list_servers, gateway_list_tools, gateway_search_tools, gateway_invoke) stays constant. The README benchmark scenario also surfaces stats, cost report, playbooks, profile controls, disabled-capability visibility, and reload for a 15-tool surface. Surfacing webhook status adds the 16th tool.

Why not...

Security

Connecting N MCP servers to an agent means accepting N attack surfaces. Tool poisoning, rug pulls, and exfiltration via hidden instructions in tool descriptions are demonstrated attacks, not hypotheticals. Invariant Labs' writeup (MCP Security Notification: Tool Poisoning Attacks) and Simon Willison's summary (MCP has prompt injection security problems) lay out the threat model.

mcp-gateway puts every backend tool description behind one audit surface and defends it structurally:

• Tool-poisoning validator (AX-010). Every backend tool description is scanned before it reaches the agent's context window. HIGH patterns fail-closed: <IMPORTANT> blocks, ~/.ssh/~/.aws/id_rsa/.env//etc/passwd, sidenote exfiltration language, curl .* https?://, base64 in exfil context. MEDIUM patterns warn: 40+ consecutive spaces, zero-width / bidi-override Unicode, oversized descriptions. Implementation: src/validator/rules/tool_poisoning.rs (19 tests).
• SHA-256 capability hash-pinning. mcp-gateway cap pin <file> writes a sha256: line over the file's canonical hash (grep -v '^sha256:' capability.yaml | sha256sum is reproducible from any shell). The loader refuses any mismatched file on load and on every watcher event.
• Rug-pull detection. When a pinned capability's on-disk content changes after approval, the watcher unloads it and logs RUG-PULL DETECTED. The capability stays quarantined until an operator re-pins. Implementation: src/capability/hash.rs and detect_rug_pulls in src/capability/backend.rs.
• Centralized audit surface. Capability YAMLs are plain text, diffable, grep-able, PR-reviewable. The agent only ever sees the compact Meta-MCP surface (13-16 tools). No N-server tool-list pollution means no N-server attack surface.

Full walkthrough, PoC snippets, and roadmap: docs/blog/security-aware-mcp-gateway.md.

• OWASP Agentic AI Top 10. Full coverage across all 10 risks. See docs/OWASP_AGENTIC_AI_COMPLIANCE.md.

Recent additions

• OpenAPI importer. mcp-gateway cap import <spec-url-or-file> turns an OpenAPI 3 spec into one validated capability YAML per operation. The full Swagger Petstore spec becomes 19 validated capability YAMLs end-to-end:

  mcp-gateway cap import https://petstore3.swagger.io/api/v3/openapi.json --output capabilities/ --prefix petstore
  

  22 tests across src/capability/openapi.rs and tests/openapi_import_tests.rs.

Quick Start

Tell your AI assistant (recommended):

Read https://github.com/MikkoParkkola/mcp-gateway and install mcp-gateway to consolidate all my MCP servers behind one gateway

Your agent will install the binary, run the setup wizard, import your existing MCP servers, and wire itself up. Works in Claude Code, Cursor, Windsurf, Codex, and any AI with terminal access.

Or four commands:

brew install MikkoParkkola/tap/mcp-gateway   # 1. install
mcp-gateway setup wizard --configure-client  # 2. import existing servers + wire up clients
mcp-gateway serve                            # 3. run
mcp-gateway doctor                           # 4. verify everything is healthy

That's it. Your AI clients now talk to the gateway and the gateway routes to every backend you already had configured — at a flat ~15 tools instead of ~150. Start with gateway_search_tools from your AI client to find any backend tool, then invoke it with gateway_invoke.

Nothing to import yet? mcp-gateway init --with-examples writes a working gateway.yaml with public capabilities so you can confirm the gateway is alive before adding your own servers.

Install

# macOS Apple Silicon
curl -L https://github.com/MikkoParkkola/mcp-gateway/releases/latest/download/mcp-gateway-darwin-arm64 -o mcp-gateway && chmod +x mcp-gateway

# macOS Intel
curl -L https://github.com/MikkoParkkola/mcp-gateway/releases/latest/download/mcp-gateway-darwin-x86_64 -o mcp-gateway && chmod +x mcp-gateway

# Linux x86_64
curl -L https://github.com/MikkoParkkola/mcp-gateway/releases/latest/download/mcp-gateway-linux-x86_64 -o mcp-gateway && chmod +x mcp-gateway

Set up — three ways

Option A — Auto-import everything (recommended)

mcp-gateway setup wizard --configure-client

Scans Claude Desktop, Claude Code, Cursor, Zed, Continue.dev, Codex, and running MCP processes; lets you pick which servers to import into gateway.yaml; and writes the gateway entry back into each detected client config so they route through the gateway instead. Add --yes to skip the prompts and import everything.

Option B — Add servers from the built-in registry

48 popular MCP servers are pre-registered with the right command, args, and env-var template. mcp-gateway add is claude mcp add / codex mcp add compatible:

mcp-gateway add tavily                                       # known server, fills env vars
mcp-gateway add my-server -- npx -y @some/mcp-server --flag  # arbitrary stdio command
mcp-gateway add --url https://mcp.sentry.dev/mcp sentry      # HTTP server
mcp-gateway add -e API_KEY=xxx my-server -- npx my-mcp-server

mcp-gateway list shows what's configured. mcp-gateway remove <name> removes one.

Option C — Hand-write gateway.yaml

For the full schema reference, see docs/QUICKSTART.md#configuration. Minimal example:

server:
  port: 39400

meta_mcp:
  enabled: true

backends:
  tavily:
    command: "npx -y @anthropic/mcp-server-tavily"
    description: "Web search"
    env:
      TAVILY_API_KEY: "${TAVILY_API_KEY}"

  sentry:
    http_url: "https://mcp.sentry.dev/mcp"
    description: "Sentry issues"

Run and verify

mcp-gateway serve                  # start the gateway
mcp-gateway doctor                 # diagnose config, port, env vars, backend health
mcp-gateway doctor --fix           # auto-fix issues where possible

The web dashboard is at http://localhost:39400/ui once serve is running.

Connect AI clients (if you skipped Option A)

setup export writes the gateway entry into client config files for you. It auto-detects the right path per client:

mcp-gateway setup export --target all                 # all detected clients
mcp-gateway setup export --target claude-code         # one client
mcp-gateway setup export --target all --dry-run       # preview without writing
mcp-gateway setup export --target all --watch         # regenerate on gateway.yaml changes

Modes: --mode proxy (HTTP), --mode stdio (subprocess), --mode auto (probe health endpoint, fall back).

{
  "mcpServers": {
    "gateway": {
      "type": "http",
      "url": "http://localhost:39400/mcp"
    }
  }
}

Key Benefits

1. Unlimited Tools, Minimal Tokens

The gateway exposes 14 Meta-MCP tools minimum, 16 in the README benchmark scenario, and 17 when webhook status is surfaced. The base discovery quartet stays fixed; the rest are operator helpers for stats, cost, playbooks, profile control, disabled-capability visibility, reload, and webhook status.

Token math (Claude Opus @ $15/M input tokens, reproducible via python benchmarks/token_savings.py --scenario readme):

• Without: 100 tools x 150 tokens x 1,000 requests = 15M tokens = $225
• With (README benchmark): 16 Meta-MCP tools x 100 tokens x 1,000 requests = 1.6M tokens = $24.00

2. Any REST API to MCP Tool -- No Code

Turn any REST API into a tool by dropping a YAML file (~30 seconds) or importing an OpenAPI spec:

mcp-gateway cap import stripe-openapi.yaml --output capabilities/ --prefix stripe

The gateway ships with 100+ built-in capabilities -- weather, Wikipedia, GitHub, stock quotes, package tracking, and more. Capability YAMLs hot-reload automatically after file changes, no restart needed.

3. Change Your MCP Stack Without Losing Your AI Session

Your AI connects once to localhost:39400. Behind it, capability YAMLs plus reloadable gateway config sections (including backend add/remove/update and routing/profile changes) can reload live via file watching, gateway_reload_config, or POST /ui/api/reload. Listener address changes report restart_required; env_files list changes stay startup-only and take effect after restart. Your AI session stays connected.

4. Production Resilience

Circuit breakers, retry with backoff, rate limiting, health checks, graceful shutdown, and concurrency limits. One flaky server won't take down your toolchain.

Architecture

┌───────────────────────────────────────────────────────────────┐
│                    MCP Gateway (:39400)                        │
│  ┌─────────────────────────────────────────────────────────┐  │
│  │  Meta-MCP: 13-16 Tools + Surfaced Tools                 │  │
│  │  • gateway_list_servers    • gateway_search_tools       │  │
│  │  • gateway_list_tools      • gateway_invoke             │  │
│  └─────────────────────────────────────────────────────────┘  │
│                                                               │
│  ┌─────────────────────────────────────────────────────────┐  │
│  │  Failsafes: Circuit Breaker │ Retry │ Rate Limit        │  │
│  └─────────────────────────────────────────────────────────┘  │
│                            │                                  │
│         ┌──────────────────┼──────────────────┐               │
│         ▼                  ▼                  ▼               │
│  ┌─────────────┐    ┌─────────────┐    ┌─────────────┐       │
│  │   Tavily    │    │  Context7   │    │   Pieces    │       │
│  │   (stdio)   │    │   (http)    │    │   (sse)     │       │
│  └─────────────┘    └─────────────┘    └─────────────┘       │
└───────────────────────────────────────────────────────────────┘

Features

Web Dashboard

Embedded web UI at /ui -- live status, searchable tools, server health, config viewer. Operator dashboard at /dashboard. Cost tracking at /ui#costs. All served from the same binary and port, no frontend build step.

Security & Governance

Integration & Discovery

Protocol & Transport

• MCP Version: 2025-11-25 (latest spec)
• Transports: stdio, Streamable HTTP, SSE, WebSocket
• Hot Reload: Capability YAMLs plus reloadable gateway config sections are watched and reloaded live
• Reload Outcomes: gateway_reload_config and /ui/api/reload return restart_required for listener changes (for example server.host / server.port); env_files list edits remain startup-only
• Config Discovery: Auto-finds gateway.yaml in cwd, ~/.config/mcp-gateway/, /etc/mcp-gateway/
• "Did You Mean?": Levenshtein-based typo correction on tool names
• Tool Annotations: MCP 2025-11-25 readOnlyHint, destructiveHint, openWorldHint
• Dynamic Descriptions: Live tool/server counts in meta-tool descriptions
• Tunnel Mode: Expose via Tailscale or pipenet without opening ports
• Shell Completions: mcp-gateway completions bash|zsh|fish
• Spec Preview (opt-in): Filtered tools/list (SEP-1821), tools/resolve (SEP-1862), dynamic promotion

Supported Backends

Any MCP-compliant server works. All three transport types supported:

Remote MCP servers plug in by URL — no extra code. See examples/gateway-full.yaml for a commented GitMCP backend entry and docs/REMOTE_BACKENDS.md for a step-by-step walkthrough.

API

Performance

SKILL.md / agentskills.io compatibility

MCP Gateway can ingest Agent Skills / Claude Code SKILL.md files and expose them as discoverable skills alongside capability YAML. This lets the gateway consume any SKILL.md — whether authored locally, shipped from agentskills.io, or pulled from a GitHub release — and surface it through the same meta-tool surface used for capabilities.

# Import a local skill directory (auto-discovers SKILL.md + resources/)
mcp-gateway skills import ~/.claude/skills/gws-gmail-send

# Import a single SKILL.md file
mcp-gateway skills import ./path/to/SKILL.md

# Import from an agentskills.io URL
mcp-gateway skills import https://agentskills.io/skills/my-skill/SKILL.md

# List imported skills
mcp-gateway skills list

# Search by name, description, trigger, or keyword
mcp-gateway skills search "gmail"

# Show the full body (including any embedded code blocks)
mcp-gateway skills show gws-gmail-send

# Remove a skill
mcp-gateway skills remove gws-gmail-send

What gets parsed

• YAML frontmatter (name, description, version, effort, allowed-tools, triggers, keywords)
• Markdown body, with fenced bash/python/json code blocks extracted as structured SkillCodeBlock entries
• Progressive-disclosure resources: SKILL.advanced.md, reference.md, README.md, and any resources/*.md files in the skill directory

Security model (read-only)

Imported skills are stored as data, not executed. Embedded bash or python blocks are parsed and surfaced to users/agents via skills show, but MCP Gateway will never run them automatically. A future release may add opt-in execution gated on per-skill user consent. If you need to run a skill's commands today, copy them from skills show and run them in your own shell.

Registry location: ~/.mcp-gateway/skills.json (override with MCP_GATEWAY_SKILLS_REGISTRY or --registry).

Reference: Anthropic SKILL.md spec · agentskills.io

Documentation

Troubleshooting

Backend won't connect? Test the command directly (npx -y @anthropic/mcp-server-tavily), then check gateway logs with --log-level debug.

Circuit breaker open? Check curl localhost:39400/health | jq '.backends'. Adjust thresholds in failsafe.circuit_breaker.

Tools not appearing? Verify the backend is running (gateway_list_servers). Tool lists are cached for 5 minutes.

Contributing

1. Fork and branch (git checkout -b feature/your-feature)
2. Test (cargo test) and lint (cargo fmt && cargo clippy -- -D warnings)
3. PR against main with a clear description and CHANGELOG entry

See CONTRIBUTING.md for full details. Look for good first issue or help wanted to get started.

Ecosystem

mcp-gateway is part of a suite of MCP tools:

License

MIT License -- see LICENSE for details.

Credits

Created by Mikko Parkkola. Implements Model Context Protocol version 2025-11-25.

Changelog | Releases