@agiflowai/one-mcp

MCP proxy for aggregating multiple MCP servers behind one endpoint.

one-mcp supports three proxy modes:

• meta: expose describe_tools and use_tool
• flat: expose proxied tools and resources directly
• search: expose describe_tools, list_tools, and use_tool

Use this when:

• you want one MCP entry instead of many
• you want cached startup metadata across multiple downstream servers
• you want to choose between meta-tool routing and direct flat exposure

Quick Start

1. Create Configuration

npx @agiflowai/one-mcp init

This creates mcp-config.yaml:

mcpServers:
  filesystem:
    command: npx
    args:
      - -y
      - "@modelcontextprotocol/server-filesystem"
      - "${HOME}/Documents"
    config:
      instruction: "Access files in the Documents folder"

  scaffold-mcp:
    command: npx
    args:
      - -y
      - "@agiflowai/scaffold-mcp"
      - "mcp-serve"
    config:
      instruction: "Scaffold projects and features"

2. Configure Your Agent

Add to your MCP config (.mcp.json, .cursor/mcp.json, etc.):

{
  "mcpServers": {
    "one-mcp": {
      "command": "npx",
      "args": ["-y", "@agiflowai/one-mcp", "mcp-serve", "--config", "./mcp-config.yaml"]
    }
  }
}

To change proxy behavior, set --proxy-mode:

{
  "mcpServers": {
    "one-mcp": {
      "command": "npx",
      "args": ["-y", "@agiflowai/one-mcp", "mcp-serve", "--proxy-mode", "search", "--config", "./mcp-config.yaml"]
    }
  }
}

3. Start Using

one-mcp now fronts the configured downstream servers through one MCP server.

Configuration

Proxy Modes

Use mcp-serve --proxy-mode <mode> to control how one-mcp exposes downstream tools.

meta mode:

• Default mode
• Exposes describe_tools and use_tool
• describe_tools includes the proxied capability catalog in its description

flat mode:

• Exposes proxied tools directly in tools/list
• Exposes proxied resources directly in resources/list
• Name clashes are prefixed as serverName__toolName or serverName__resourceUri
• describe_tools is still exposed when file-based skills or prompt-based skills exist

search mode:

• Exposes describe_tools, list_tools, and use_tool
• describe_tools stays compact and is used for schemas and skill instructions
• list_tools shows server capability summaries and can filter results by capability or server

Server Types

mcpServers:
  # Stdio server (local command)
  local-server:
    command: npx
    args:
      - -y
      - "@modelcontextprotocol/server-example"
    env:
      API_KEY: "${MY_API_KEY}"
    config:
      instruction: "Description for the AI agent"

  # HTTP/SSE server (remote)
  remote-server:
    url: https://api.example.com/mcp
    type: sse  # or http
    headers:
      Authorization: "Bearer ${TOKEN}"
    config:
      instruction: "Remote server description"

  # Disabled server (skipped)
  disabled-server:
    command: node
    args: ["server.js"]
    disabled: true

  # Custom timeout for slow servers
  slow-server:
    command: npx
    args: ["-y", "@heavy/mcp-package"]
    timeout: 60000  # 60 seconds (default: 30000)

Environment Variables

Use ${VAR_NAME} syntax:

mcpServers:
  api-server:
    command: npx
    args:
      - "@mycompany/mcp-server"
      - "${HOME}/data"           # Expands to /Users/username/data
    env:
      API_KEY: "${MY_API_KEY}"   # Reads from environment

Tool Blacklisting

Prevent specific downstream tools from being listed or executed:

mcpServers:
  filesystem:
    command: npx
    args: ["-y", "@modelcontextprotocol/server-filesystem", "/workspace"]
    config:
      instruction: "File system access (read-only)"
      toolBlacklist:
        - write_file
        - create_directory
        - delete_file

Blacklisted tools:

• Won't appear in tool listings
• Return an error if called

Compact Tool Descriptions

Omit downstream tool descriptions from capability listings:

mcpServers:
  filesystem:
    command: npx
    args: ["-y", "@modelcontextprotocol/server-filesystem", "/workspace"]
    config:
      omitToolDescription: true  # Show only tool names

Default output:

filesystem:
  - read_file: Read contents of a file at the specified path...
  - list_directory: List all files and directories...

With omitToolDescription:

filesystem:
  read_file, list_directory, search_files

Skills

File-based skills are loaded from SKILL.md files and exposed through describe_tools.

Configuration

Enable file-based skills by adding a skills section:

mcpServers:
  # ... your MCP servers

skills:
  paths:
    - ".claude/skills"           # Relative to config file
    - "/absolute/path/to/skills" # Absolute paths also supported

Skill File Structure

Example:

.claude/skills/
├── pdf/
│   └── SKILL.md
└── data-analysis/
    └── SKILL.md

SKILL.md format:

---
name: pdf
description: Create and manipulate PDF documents
---

# PDF Skill

This skill helps you work with PDF files...

## Usage
...

Required Frontmatter

Each SKILL.md must define:

• name: Unique identifier for the skill
• description: Brief description shown to clients

Precedence

When multiple paths are configured, skills from earlier paths take precedence over skills with the same name from later paths.

Prompt-Based Skills

You can also expose MCP prompts as skills.

Auto-Detection From Frontmatter

If prompt content contains YAML frontmatter with name and description, one-mcp can expose it as a skill.

Prompt content example:

---
name: code-reviewer
description: Review code for best practices and potential issues
---

# Code Review Instructions

When reviewing code, follow these guidelines...

Some servers, such as @agiflowai/scaffold-mcp, support --prompt-as-skill and can emit that frontmatter automatically:

mcpServers:
  scaffold-mcp:
    command: npx
    args:
      - -y
      - "@agiflowai/scaffold-mcp"
      - "mcp-serve"
      - "--prompt-as-skill"  # Enables front-matter in prompts

Multi-line descriptions are supported:

---
name: complex-skill
description: |
  A skill that does multiple things:
  - First capability
  - Second capability
  - Third capability
---

Explicit Configuration

You can also configure prompt-to-skill mappings explicitly:

mcpServers:
  my-server:
    command: npx
    args:
      - -y
      - "@mycompany/mcp-server"
    config:
      instruction: "My MCP server"
      prompts:
        code-review:
          skill:
            name: code-reviewer
            description: "Review code for best practices and potential issues"
            folder: "./prompts/code-review"  # Optional: resource folder
        documentation:
          skill:
            name: doc-generator
            description: "Generate documentation from code"

Skill Configuration Fields (Explicit Config)

| Field | Required | Description | |-------|----------|-------------| | name | Yes | Unique skill identifier shown to clients | | description | Yes | Brief description of what the skill does | | folder | No | Optional folder path for skill resources |

Skill Naming and Precedence

• File-based skills take precedence over prompt-based skills with the same name
• Skills are only prefixed with skill__ when they clash with MCP tool names
• Skills that only clash with other skills are deduplicated (first one wins, no prefix)

MCP Tools

The MCP tools exposed by one-mcp depend on --proxy-mode.

describe_tools

Returns detailed tool schemas and skill instructions.

{
  "toolNames": ["read_file", "write_file"]
}

use_tool

Executes a proxied tool in meta and search modes.

{
  "toolName": "read_file",
  "toolArgs": {
    "path": "/path/to/file"
  }
}

list_tools

Only available in search mode. Returns tool names grouped by proxied server, with server capability summaries and optional filtering.

{
  "capability": "review",
  "serverName": "architect-mcp"
}

CLI Commands

# Start MCP server (stdio, for Claude Code/Cursor)
npx @agiflowai/one-mcp mcp-serve --config ./mcp-config.yaml

# Start and clear the cached definitions first
npx @agiflowai/one-mcp mcp-serve --config ./mcp-config.yaml --clear-definitions-cache

# Start with HTTP transport
npx @agiflowai/one-mcp mcp-serve --config ./mcp-config.yaml --type http --port 3000

# Start stdio-http bridge (reuses existing HTTP server on host/port when available)
npx @agiflowai/one-mcp mcp-serve --config ./mcp-config.yaml --type stdio-http --host 127.0.0.1 --port 3000

# Start stdio-http bridge with explicit search proxy mode
npx @agiflowai/one-mcp mcp-serve --config ./mcp-config.yaml --type stdio-http --proxy-mode search --host 127.0.0.1 --port 3000

# Initialize config file
npx @agiflowai/one-mcp init --output mcp-config.yaml

# Pre-download packages for faster startup
npx @agiflowai/one-mcp prefetch --config ./mcp-config.yaml

# Pre-download packages and build a definitions cache for faster discovery
npx @agiflowai/one-mcp prefetch --config ./mcp-config.yaml --definitions-out ./.cache/one-mcp-definitions.json

# Search tools across configured servers
npx @agiflowai/one-mcp search-tools --config ./mcp-config.yaml

# Search tools as JSON
npx @agiflowai/one-mcp search-tools --config ./mcp-config.yaml --json

# Filter tools by capability or server
npx @agiflowai/one-mcp search-tools --config ./mcp-config.yaml --capability review
npx @agiflowai/one-mcp search-tools --config ./mcp-config.yaml --server filesystem

# Get tool details
npx @agiflowai/one-mcp describe-tools --config ./mcp-config.yaml --tools read_file,write_file

# Execute a tool directly
npx @agiflowai/one-mcp use-tool --config ./mcp-config.yaml --tool-name read_file --args '{"path": "/tmp/test.txt"}'

# List all resources from configured servers
npx @agiflowai/one-mcp list-resources --config ./mcp-config.yaml

# Filter resources by server
npx @agiflowai/one-mcp list-resources --config ./mcp-config.yaml --server my-server

# Read a resource by URI
npx @agiflowai/one-mcp read-resource --config ./mcp-config.yaml file:///readme.md

# Read from a specific server
npx @agiflowai/one-mcp read-resource --config ./mcp-config.yaml --server my-server file:///readme.md

# List prompts from configured servers
npx @agiflowai/one-mcp list-prompts --config ./mcp-config.yaml

# Get a prompt by name
npx @agiflowai/one-mcp get-prompt --config ./mcp-config.yaml scaffold-feature

# Get a prompt from a specific server with arguments
npx @agiflowai/one-mcp get-prompt --config ./mcp-config.yaml --server scaffold-mcp --args '{"projectPath":"apps/web"}' scaffold-feature

Prefetch Command

Pre-download packages used by MCP servers (npx, pnpx, uvx, uv) and optionally build a cached definitions file for faster tool/prompt discovery during mcp-serve startup:

# Prefetch all packages
npx @agiflowai/one-mcp prefetch --config ./mcp-config.yaml

# Prefetch packages and write a definitions cache
npx @agiflowai/one-mcp prefetch --config ./mcp-config.yaml --definitions-out ./.cache/one-mcp-definitions.json

# Build only the definitions cache
npx @agiflowai/one-mcp prefetch --config ./mcp-config.yaml --definitions-out ./.cache/one-mcp-definitions.yaml --skip-packages

# Clear the default definitions cache
npx @agiflowai/one-mcp prefetch --config ./mcp-config.yaml --clear-definitions-cache --skip-packages

# Dry run - see what would be prefetched
npx @agiflowai/one-mcp prefetch --config ./mcp-config.yaml --dry-run

# Run prefetch in parallel (faster)
npx @agiflowai/one-mcp prefetch --config ./mcp-config.yaml --parallel

# Filter by package manager
npx @agiflowai/one-mcp prefetch --config ./mcp-config.yaml --filter npx

| Option | Description | |--------|-------------| | -c, --config | Path to config file | | -p, --parallel | Run prefetch commands in parallel | | -d, --dry-run | Show what would be prefetched without executing | | -f, --filter | Filter by package manager: npx, pnpx, uvx, or uv | | --definitions-out | Write a JSON or YAML definitions cache file for mcp-serve | | --skip-packages | Skip package prefetch and only write the definitions cache | | --clear-definitions-cache | Delete the effective definitions cache file before continuing |

Definitions Cache Workflow

For installations with many MCP servers, especially stdio-backed servers, mcp-serve now tries to use a definitions cache automatically. The default cache path is under ~/.aicode-toolkit/, and the filename is derived from the sanitized absolute config path. For example, /tmp/project/mcp-config.yaml becomes ~/.aicode-toolkit/tmp_project_mcp-config.yaml.definitions-cache.json.

If that cache file exists and matches the current config, one-mcp starts from cached tool/prompt metadata and connects to downstream MCP servers on demand. If the cache is missing or stale, one-mcp keeps the current eager startup behavior and writes a fresh cache in the background for the next run.

You can still prebuild the cache explicitly:

# Step 1: Warm packages and cache definitions
npx @agiflowai/one-mcp prefetch --config ./mcp-config.yaml --definitions-out ./.cache/one-mcp-definitions.json

# Step 2: Start one-mcp using the prefetched definitions
npx @agiflowai/one-mcp mcp-serve --config ./mcp-config.yaml --definitions-cache ./.cache/one-mcp-definitions.json

The definitions cache stores tool schemas, prompt metadata, and prompt-based skill metadata. Use --clear-definitions-cache on mcp-serve or prefetch to delete the cache and force a cold start.

Server Options

| Option | Description | Default | |--------|-------------|---------| | -c, --config | Path to config file (YAML or JSON) | Required | | -t, --type | Transport: stdio, http, sse, stdio-http | stdio | | -p, --port | Port for HTTP/SSE/stdio-http internal HTTP | 3000 | | --host | Host for HTTP/SSE/stdio-http internal HTTP | localhost | | --no-cache | Force reload config, bypass cache | false | | --definitions-cache | Read tool/prompt/skill definitions from a specific JSON or YAML cache file | Auto-derived from config path | | --clear-definitions-cache | Delete the effective definitions cache file before startup | false |

Notes

• one-mcp reads YAML or JSON config files
• environment variables use ${VAR_NAME} interpolation
• downstream stdio/http/sse servers can be mixed in one config
• definitions cache is keyed by config content and one-mcp version

Related Packages

• @agiflowai/scaffold-mcp - Code scaffolding and templates
• @agiflowai/architect-mcp - Design patterns and code review
• @agiflowai/aicode-toolkit - Unified CLI toolkit

License

AGPL-3.0 © AgiflowIO