Connect any API. To any AI agent.
Auto-generate Model Context Protocol tool definitions from any REST, GraphQL, SOAP, or gRPC API. Self-hosted in minutes. No glue code. No rewrites.
AI that delivers
Legacy and most existing APIs weren't designed for a world where the consumer is an LLM. Schemas lack semantic context, tool boundaries are ambiguous, and payloads burn through context windows.
Expose, govern, and optimize LLM, MCP, and API resources through a single point of control — eliminating integration complexity without creating and maintaining hundreds of individual MCP servers.
for developers
Ship an MCP server in 2 minutes. Not 2 weeks.
Stop writing tool definitions by hand. Point MCP Bridge at any schema URL and every operation becomes a fully typed, annotated MCP tool — ready for Claude, GPT, Gemini, or any MCP-compatible client.
# Pull and run MCP Bridge
$ docker run -d \
--name mcp-bridge \
-p 8080:8080 \
-v ./bridge.yaml:/app/config.yaml \
appfactor/mcp-bridge:latest
# Point any MCP client at the endpoint
$ curl https://localhost:8080/mcp/tools
→ 42 tools generated · ready
apis:
- name: "payments"
protocol: "rest"
schema: "https://api.acme.io/openapi.yaml"
auth:
type: "oauth2"
flow: "client_credentials"
code_mode: true # 98% less context
observability:
otel: true
log_level: "info"
# Auto-generated tools, ready for any LLM
tools_generated: 42
protocols: [rest, graphql]
annotations:
read_only: 28
idempotent: 31
destructive: 4
avg_tokens_per_tool: 487
code_mode_tokens: 960 # vs ~48,000 raw
Schema-driven
OpenAPI 3, GraphQL introspection, WSDL, and .proto files — all parsed automatically.
Self-hosted
Docker container on AWS ECS, Azure Container Apps, or any orchestrator. Your data never leaves your network.
Built in Rust
Memory-safe, high-throughput, production-ready. Zero external SaaS dependencies at runtime.
How it works
From API to AI-ready tool in four steps.
Import API schemas
Provide a schema via URL, paste content, or upload files. Supports OpenAPI (JSON/YAML), GraphQL introspection, WSDL, and gRPC (server reflection or .proto files).
Auto-generate MCP tools
Each operation becomes a fully described MCP tool with typed input/output schemas, parameter mappings, behavioural annotations, and documentation.
Execute at runtime
MCP Bridge validates inputs, maps parameters, handles authentication, and forwards requests to the backend. Responses are post-processed to reduce token waste.
Scale with Code Mode
For large APIs, 3 meta-tools replace the full catalog — cutting context window usage by ~98%. The LLM orchestrates calls via a secure Boa sandbox.
Not a gateway
Built for AI agents, not just HTTP traffic.
API gateways route HTTP requests. MCP Bridge does something fundamentally different — it translates APIs into semantically rich tool definitions LLMs can reason about, select correctly, and call efficiently.
It handles what gateways were never designed for: tool curation, response post-processing to reduce token waste, context window management, and AI-specific observability across latency, throughput, token usage, and error rates.
Code Mode
~98% less
Three meta-tools replace hundreds of individual tool definitions. The LLM discovers tools on demand and orchestrates calls via JavaScript in a secure Boa sandbox with 30-second timeout — same capabilities, a fraction of the token cost.
Standard — 100+ tool definitions
Code Mode — 3 meta-tools
Capabilities
Enterprise-ready, on day one.
Tool curation
Enable, rename, edit descriptions, customize parameter mappings, and configure per-tool response processing.
Response post-processing
Per-tool declarative rules — unwrap, select, exclude, limit, sort, flatten, aggregate — or custom JavaScript in a sandbox.
Enterprise auth
Bearer, Basic, API Key, OAuth2, AWS Cognito SRP. OIDC for the web UI with Entra ID, Keycloak, Auth0, Okta.
Analytics dashboard
Latency, throughput, per-tool and per-API metrics, token usage breakdowns, error rates. O-Tel in Enterprise.
Tool annotations
Read-only, destructive, idempotent, and open-world hints auto-inferred from API semantics. Editable per tool.
Semantic tool search
Hybrid search with full-text, trigram fuzzy matching, and optional vector similarity via pgvector and HNSW.
Self-hosted
Docker container on AWS ECS, Azure Container Apps, or any orchestrator. Zero external SaaS dependencies.
Reliability
Per-API token bucket rate limiting, exponential backoff with jitter, configurable retry policies, health checks.
Who it's for
Built for the teams making AI work in production.
Platform Engineering
Expose internal APIs to AI agents
Without writing or maintaining MCP adapters for each service. Import schemas, configure auth, and expose governed tools through a single control plane.
AI Engineers
A managed tool layer for agents
Build agents that call enterprise APIs with authentication, rate limiting, response post-processing, and observability built in — not bolted on.
Enterprise Organizations
Bridge your API portfolio to MCP
Adopt MCP as a standard. Connect your existing API landscape to LLM clients quickly and securely, without refactoring services.
Give your AI agents the data access they need.
Available on AWS and Azure Marketplace. Tiers from evaluation to enterprise-wide deployment.