agentskills-mcp-server 0.2.1

MCP server integration for the Agent Skills format — expose skills as MCP tools and resources (https://agentskills.io)

agentskills-mcp-server

MCP server integration for the Agent Skills SDK — expose a skill registry as an MCP server.

Creates a Model Context Protocol server from a SkillRegistry, exposing skills as MCP tools and resources. Works with any MCP-compatible client (Claude Desktop, VS Code, custom clients, etc.).

Installation

pip install agentskills-mcp-server

With provider extras:

pip install agentskills-mcp-server[fs]    # filesystem provider
pip install agentskills-mcp-server[http]  # HTTP provider

Requires Python 3.12+. Installs agentskills-core, mcp, and pydantic as dependencies.

Quick Start (CLI)

Create a server.json config file:

{
    "name": "My Skills Server",
    "skills": [
        {
            "id": "incident-response",
            "provider": "fs",
            "options": {"root": "./skills"}
        }
    ]
}

Start the server:

python -m agentskills_mcp_server --config server.json

With Streamable HTTP transport:

python -m agentskills_mcp_server --config server.json --transport streamable-http

The server listens on http://127.0.0.1:8000/mcp.

MCP Client Integration

Any MCP-compatible client (Claude Desktop, VS Code, etc.) can connect to the server.

Stdio (local):

{
    "command": "python",
    "args": ["-m", "agentskills_mcp_server", "--config", "server.json"]
}

Streamable HTTP (remote):

{
    "url": "http://127.0.0.1:8000/mcp"
}

Config Reference

The server.json file supports the following structure:

Field	Type	Required	Description
name	str	Yes	Display name shown to MCP clients
instructions	str	No	Server-level instructions sent during handshake
skills	list	Yes	One or more skill definitions (see below)

Each skill entry:

Field	Type	Required	Description
id	str	Yes	Skill identifier
provider	str	Yes	Provider type: "fs" or "http"
options	dict	No	Provider-specific options

Provider options:

• fs: root (path to skills directory, default ".")
• http: base_url (required), headers (optional), params (optional query string parameters)

Only "fs" and "http" are supported as provider types.

Programmatic Usage

For custom providers or advanced setups, use the Python API directly:

from agentskills_core import SkillRegistry
from agentskills_mcp_server import create_mcp_server

registry = SkillRegistry()
await registry.register("incident-response", my_custom_provider)

server = create_mcp_server(registry, name="My Skills Server")
server.run()  # stdio by default

Tools

The server exposes tools that let the LLM agent access skill content:

Tool	Parameters	Description
get_skill_metadata	skill_id	Read frontmatter (name, description, etc.)
get_skill_body	skill_id	Load full skill instructions
get_skill_reference	skill_id, name	Read a reference document
get_skill_script	skill_id, name	Read a script
get_skill_asset	skill_id, name	Read an asset

Resources

The server provides resources for system-prompt context:

URI	Description
skills://catalog/xml	XML catalog of all registered skills
skills://catalog/markdown	Markdown catalog of all registered skills
skills://tools-usage-instructions	Workflow instructions for using the tools

The MCP client reads these resources and injects them into the system prompt, giving the agent both what skills exist and how to interact with them.

API

create_mcp_server(registry, *, name, instructions=None) -> FastMCP

Parameter	Type	Description
registry	SkillRegistry	The registry whose skills are exposed
name	str	Display name for the MCP server (required)
instructions	str | None	Optional server-level instructions sent to clients

Returns a configured FastMCP instance ready for server.run().

Supported transport modes: stdio (default), streamable-http.

License

MIT