MCP (Model Context Protocol)

The abstractcore.mcp module provides: - a minimal MCP JSON-RPC client (Streamable HTTP) → abstractcore.mcp.McpClient - a minimal MCP stdio client (spawn a subprocess) → abstractcore.mcp.McpStdioClient - tool discovery (tools/list) and conversion into AbstractCore tool specs → abstractcore.mcp.McpToolSource

What you can do today

1) Discover tools from an MCP server

from abstractcore.mcp import McpClient, McpToolSource

client = McpClient(url="http://localhost:3000")  # MCP streamable HTTP endpoint
source = McpToolSource(server_id="local", client=client)
tools = source.list_tool_specs()

Each returned tool spec is an AbstractCore-compatible dict you can pass to tools=[...] in generate()/agenerate(). Tool names are namespaced as:

mcp::<server_id>::<tool_name>

See abstractcore/abstractcore/mcp/naming.py.

2) Execute MCP tools in your host/runtime

AbstractCore’s default execution path is passthrough (execute_tools=False): the model can request tool calls and you execute them in your host/runtime.

The built-in abstractcore.tools.registry.execute_tools() executes Python callables registered in the (deprecated) global registry; it does not automatically route MCP tool calls. For MCP, your host/runtime should detect names starting with mcp:: and dispatch them to an MCP client.

from abstractcore.mcp import McpClient, parse_namespaced_tool_name

client = McpClient(url="http://localhost:3000")

def execute_mcp_tool_call(call: dict) -> dict:
    parsed = parse_namespaced_tool_name(call.get("name", ""))
    if not parsed:
        raise ValueError("Not an MCP tool call")
    server_id, tool_name = parsed
    return client.call_tool(name=tool_name, arguments=call.get("arguments") or {})

Transports supported

Streamable HTTP

McpClient posts JSON-RPC to the server URL. It automatically sets an Accept header compatible with streamable HTTP (application/json, text/event-stream) and will capture MCP-Session-Id responses when provided.

See abstractcore/abstractcore/mcp/client.py.

stdio

McpStdioClient spawns an MCP server subprocess and communicates over stdin/stdout with JSON-RPC, including a best-effort initialization handshake.

See abstractcore/abstractcore/mcp/stdio_client.py.

Configuration helpers

create_mcp_client(config=...) supports both HTTP and stdio config shapes:

from abstractcore.mcp import create_mcp_client

client = create_mcp_client(config={"url": "http://localhost:3000"})
client = create_mcp_client(config={"transport": "stdio", "command": ["my-mcp-server", "--stdio"]})

See abstractcore/abstractcore/mcp/factory.py.

Current limitations

• MCP is currently a library-level integration (tool discovery + clients). AbstractCore’s HTTP server does not expose MCP management endpoints.
• Tool execution routing for mcp::... names is host/runtime responsibility.