generate-mcp-server

Generate a Model Context Protocol (MCP) server from an OpenAPI spec using Speakeasy. The MCP server exposes API operations as tools that AI assistants like Claude can call directly.

When to Use

• User wants to create an MCP server from their API
• User asks about Model Context Protocol integration
• User wants AI assistants to interact with their API
• User says: "generate MCP server", "create MCP server", "speakeasy MCP"
• User asks: "How do I make my API available to Claude?"
• User mentions: "mcp-typescript", "AI assistant tools", "Claude tools"

Inputs

Input	Required	Description
OpenAPI spec	Yes	Path or URL to the OpenAPI specification
Package name	Yes	npm package name for the MCP server (e.g., my-api-mcp)
Auth method	Yes	How the API authenticates (bearer token, API key, etc.)
Env var prefix	No	Prefix for environment variables (e.g., MYAPI)
Scope strategy	No	How to map operations to scopes (default: read/write by HTTP method)

Outputs

Output	Description
MCP server	TypeScript MCP server with one tool per API operation
CLI entry point	Command-line interface with stdio and SSE transports
Scope definitions	Scope-based access control for filtering tools
Docker support	Dockerfile and compose config for containerized deployment
Workflow config	.speakeasy/workflow.yaml configured for MCP generation

Prerequisites

1. Speakeasy CLI installed and authenticated:

speakeasy auth login
# Or for CI/AI agents:
export SPEAKEASY_API_KEY="<your-api-key>"

2. Node.js 20+ installed (for the generated MCP server).

3. A valid OpenAPI spec (3.0 or 3.1). Validate first:

speakeasy lint openapi --non-interactive -s ./openapi.yaml

Run speakeasy auth login to authenticate interactively, or set the SPEAKEASY_API_KEY environment variable.

Command

The generation uses speakeasy run after configuring the workflow, overlays, and gen.yaml. There is no single command -- follow the step-by-step workflow below.

# After all config files are in place:
speakeasy run

Step-by-Step Workflow

Step 1: Create the Scopes Overlay

Create mcp-scopes-overlay.yaml in the project root. This controls which API operations become MCP tools and what scopes they require:

# mcp-scopes-overlay.yaml
openapi: 3.1.0
overlay: 1.0.0
info:
  title: Add MCP scopes
  version: 0.0.0
actions:
  # Enable read operations
  - target: $.paths.*["get","head"]
    update:
      x-speakeasy-mcp:
        scopes: [read]
        disabled: false

  # Enable write operations
  - target: $.paths.*["post","put","delete","patch"]
    update:
      x-speakeasy-mcp:
        scopes: [write]
        disabled: false

  # Disable specific sensitive endpoints (customize as needed)
  # - target: $.paths["/admin/danger-zone"]["delete"]
  #   update:
  #     x-speakeasy-mcp:
  #       disabled: true

Step 2: Create the Workflow Configuration

Create .speakeasy/workflow.yaml:

# .speakeasy/workflow.yaml
workflowVersion: 1.0.0
speakeasyVersion: latest
sources:
  My-API:
    inputs:
      - location: ./openapi.yaml
    overlays:
      - location: mcp-scopes-overlay.yaml
    output: openapi.yaml
targets:
  mcp-server:
    target: mcp-typescript
    source: My-API

Replace ./openapi.yaml with the actual spec path or URL.

Important: Use the standalone mcp-typescript target, not typescript with enableMCPServer: true. The embedded approach (enableMCPServer flag) is deprecated.

Step 3: Configure gen.yaml

Create .speakeasy/gen.yaml:

# .speakeasy/gen.yaml
configVersion: 2.0.0
generation:
  sdkClassName: MyApiMcp
  maintainOpenAPIOrder: true
  devContainers:
    enabled: true
    schemaPath: ./openapi.yaml
typescript:
  version: 1.0.0
  packageName: my-api-mcp
  envVarPrefix: MYAPI

Key settings:

• target: mcp-typescript in workflow.yaml -- this is what triggers MCP server generation
• packageName -- the npm package name users will npx
• envVarPrefix -- prefix for auto-generated env var names

Step 4: Generate

speakeasy run

For AI-friendly output:

speakeasy run --output console 2>&1 | tail -50

Using the Generated MCP Server

CLI Usage

# Start with stdio transport (default, for local AI assistants)
npx my-api-mcp mcp start --bearer-auth "YOUR_TOKEN"

# Start with SSE transport (for networked deployment)
npx my-api-mcp mcp start --transport sse --port 3000 --bearer-auth "YOUR_TOKEN"

# Filter by scope (only expose read operations)
npx my-api-mcp mcp start --scope read --bearer-auth "YOUR_TOKEN"

# Mount specific tools only
npx my-api-mcp mcp start --tool users-get-users --tool users-create-user --bearer-auth "YOUR_TOKEN"

CLI Options

Flag	Description	Default
--transport	Transport type: stdio or sse	stdio
--port	Port for SSE transport	2718
--bearer-auth	API authentication token	Required
--server-url	Override API base URL	From spec
--scope	Filter by scope (repeatable)	All scopes
--tool	Mount specific tools (repeatable)	All tools
--log-level	Logging level	info

Claude Desktop Configuration

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "my-api": {
      "command": "npx",
      "args": [
        "-y", "--package", "my-api-mcp",
        "--",
        "mcp", "start",
        "--bearer-auth", "<API_TOKEN>"
      ]
    }
  }
}

Claude Code Configuration

Add to .claude/settings.json or use claude mcp add:

{
  "mcpServers": {
    "my-api": {
      "command": "npx",
      "args": [
        "-y", "--package", "my-api-mcp",
        "--",
        "mcp", "start",
        "--bearer-auth", "<API_TOKEN>"
      ]
    }
  }
}

Docker Deployment

For production, use SSE transport with Docker:

# Build and run
docker-compose up -d

# Configure MCP client to use SSE endpoint
# "url": "http://localhost:32000/sse"

The generated project includes a Dockerfile and docker-compose.yaml.

Example

Full example generating an MCP server for a pet store API:

# 1. Validate the spec
speakeasy lint openapi --non-interactive -s ./petstore.yaml

# 2. Create scopes overlay
cat > mcp-scopes-overlay.yaml << 'EOF'
openapi: 3.1.0
overlay: 1.0.0
info:
  title: Add MCP scopes
  version: 0.0.0
actions:
  - target: $.paths.*["get","head"]
    update:
      x-speakeasy-mcp:
        scopes: [read]
        disabled: false
  - target: $.paths.*["post","put","delete","patch"]
    update:
      x-speakeasy-mcp:
        scopes: [write]
        disabled: false
EOF

# 3. Create workflow (assumes .speakeasy/ dir exists)
mkdir -p .speakeasy
cat > .speakeasy/workflow.yaml << 'EOF'
workflowVersion: 1.0.0
speakeasyVersion: latest
sources:
  petstore:
    inputs:
      - location: ./petstore.yaml
    overlays:
      - location: mcp-scopes-overlay.yaml
    output: openapi.yaml
targets:
  mcp-server:
    target: mcp-typescript
    source: petstore
EOF

# 4. Create gen.yaml
cat > .speakeasy/gen.yaml << 'EOF'
configVersion: 2.0.0
generation:
  sdkClassName: PetStoreMcp
  maintainOpenAPIOrder: true
typescript:
  version: 1.0.0
  packageName: petstore-mcp
  envVarPrefix: PETSTORE
EOF

# 5. Generate
speakeasy run

# 6. Test locally
npx petstore-mcp mcp start --bearer-auth "test-token"

Expected Output

Workflow completed successfully.
Generated TypeScript MCP server in ./

The generated project contains:

• src/mcp-server/server.ts -- Main MCP server factory
• src/mcp-server/tools/ -- One tool per API operation
• src/mcp-server/mcp-server.ts -- CLI entry point
• src/mcp-server/scopes.ts -- Scope definitions

Best Practices

1. Use overlays for MCP config -- never edit the source OpenAPI spec directly
2. Enhance descriptions for AI -- add documentation overlays so AI assistants understand tool purpose
3. Filter tools at runtime -- use --scope and --tool flags to limit what is exposed
4. Use environment variables -- never hardcode tokens in config files
5. Start with read-only scopes -- add write scopes only when needed
6. Create a dedicated MCP package -- keep MCP separate from your main SDK

What NOT to Do

• Do NOT modify the source OpenAPI spec to add x-speakeasy-mcp -- use overlays instead
• Do NOT hardcode API tokens in Claude Desktop or Claude Code config files -- use environment variables or secrets managers
• Do NOT expose all operations without reviewing them -- disable sensitive admin endpoints
• Do NOT skip spec validation -- invalid specs produce broken MCP servers
• Do NOT use the deprecated enableMCPServer: true flag in gen.yaml -- use the standalone mcp-typescript target in workflow.yaml instead
• Do NOT generate without a scopes overlay -- tools will lack scope definitions
• Do NOT use the generated MCP server as a general SDK -- it is purpose-built for AI assistant integration

Troubleshooting

MCP server fails to start

Symptom: npx my-api-mcp mcp start errors immediately.

Cause: Missing or invalid authentication flags.

Fix:

# Ensure auth flag matches your API's auth scheme
npx my-api-mcp mcp start --bearer-auth "YOUR_TOKEN"

# Check --help for available auth flags
npx my-api-mcp mcp start --help

No tools appear in AI assistant

Symptom: MCP server starts but AI assistant shows no tools.

Cause: Missing x-speakeasy-mcp extensions or all operations disabled.

Fix: Verify the scopes overlay is listed in workflow.yaml under overlays: and that operations have disabled: false.

Generation fails with mcp-typescript target

Symptom: speakeasy run fails when using target: mcp-typescript.

Cause: Usually a spec validation issue, missing workflow config, or using the deprecated enableMCPServer flag instead of the mcp-typescript target.

Fix:

# Validate spec first
speakeasy lint openapi --non-interactive -s ./openapi.yaml

# Ensure workflow.yaml uses target: mcp-typescript (NOT target: typescript with enableMCPServer)
cat .speakeasy/workflow.yaml

# Remove enableMCPServer from gen.yaml if present -- it is deprecated

Tools missing expected operations

Symptom: Some API operations are not available as MCP tools.

Cause: Operations not targeted by the scopes overlay or explicitly disabled.

Fix: Review mcp-scopes-overlay.yaml target selectors. Ensure paths and methods match your spec.