apcore-mcp 0.5.0

Automatic MCP Server & OpenAI Tools Bridge for apcore

apcore-mcp

Automatic MCP Server & OpenAI Tools Bridge for apcore.

apcore-mcp turns any apcore-based project into an MCP Server and OpenAI tool provider — with zero code changes to your existing project.

┌──────────────────┐
│  django-apcore   │  ← your existing apcore project (unchanged)
│  flask-apcore    │
│  ...             │
└────────┬─────────┘
         │  extensions directory
         ▼
┌──────────────────┐
│    apcore-mcp    │  ← just install & point to extensions dir
└───┬──────────┬───┘
    │          │
    ▼          ▼
  MCP       OpenAI
 Server      Tools

Design Philosophy

• Zero intrusion — your apcore project needs no code changes, no imports, no dependencies on apcore-mcp
• Zero configuration — point to an extensions directory, everything is auto-discovered
• Pure adapter — apcore-mcp reads from the apcore Registry; it never modifies your modules
• Works with any xxx-apcore project — if it uses the apcore Module Registry, apcore-mcp can serve it

Installation

Install apcore-mcp alongside your existing apcore project:

pip install apcore-mcp

That's it. Your existing project requires no changes.

Requires Python 3.10+ and apcore >= 0.5.0.

Quick Start

Zero-code approach (CLI)

If you already have an apcore-based project with an extensions directory, just run:

apcore-mcp --extensions-dir /path/to/your/extensions

All modules are auto-discovered and exposed as MCP tools. No code needed.

Programmatic approach (Python API)

For tighter integration or when you need filtering/OpenAI output:

from apcore import Registry
from apcore_mcp import serve, to_openai_tools

registry = Registry(extensions_dir="./extensions")
registry.discover()

# Launch as MCP Server
serve(registry)

# Or export as OpenAI tools
tools = to_openai_tools(registry)

Integration with Existing Projects

Typical apcore project structure

your-project/
├── extensions/          ← modules live here
│   ├── image_resize/
│   ├── text_translate/
│   └── ...
├── your_app.py          ← your existing code (untouched)
└── ...

Adding MCP support

No changes to your project. Just run apcore-mcp alongside it:

# Install (one time)
pip install apcore-mcp

# Run
apcore-mcp --extensions-dir ./extensions

Your existing application continues to work exactly as before. apcore-mcp operates as a separate process that reads from the same extensions directory.

Adding OpenAI tools support

For OpenAI integration, a thin script is needed — but still no changes to your existing modules:

from apcore import Registry
from apcore_mcp import to_openai_tools

registry = Registry(extensions_dir="./extensions")
registry.discover()

tools = to_openai_tools(registry)
# Use with openai.chat.completions.create(tools=tools)

MCP Client Configuration

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "apcore": {
      "command": "apcore-mcp",
      "args": ["--extensions-dir", "/path/to/your/extensions"]
    }
  }
}

Claude Code

Add to .mcp.json in your project root:

{
  "mcpServers": {
    "apcore": {
      "command": "apcore-mcp",
      "args": ["--extensions-dir", "./extensions"]
    }
  }
}

Cursor

Add to .cursor/mcp.json in your project root:

{
  "mcpServers": {
    "apcore": {
      "command": "apcore-mcp",
      "args": ["--extensions-dir", "./extensions"]
    }
  }
}

Remote HTTP access

apcore-mcp --extensions-dir ./extensions \
    --transport streamable-http \
    --host 0.0.0.0 \
    --port 9000

Connect any MCP client to http://your-host:9000/mcp.

CLI Reference

apcore-mcp --extensions-dir PATH [OPTIONS]

Option	Default	Description
--extensions-dir	(required)	Path to apcore extensions directory
--transport	stdio	Transport: stdio, streamable-http, or sse
--host	127.0.0.1	Host for HTTP-based transports
--port	8000	Port for HTTP-based transports (1-65535)
--name	apcore-mcp	MCP server name (max 255 chars)
--version	package version	MCP server version string
--log-level	INFO	Logging: DEBUG, INFO, WARNING, ERROR

Exit codes: 0 normal, 1 invalid arguments, 2 startup failure.

Python API Reference

serve()

from apcore_mcp import serve

serve(
    registry_or_executor,        # Registry or Executor
    transport="stdio",           # "stdio" | "streamable-http" | "sse"
    host="127.0.0.1",           # host for HTTP transports
    port=8000,                   # port for HTTP transports
    name="apcore-mcp",          # server name
    version=None,                # defaults to package version
    on_startup=None,             # callback before transport starts
    on_shutdown=None,            # callback after transport completes
    tags=None,                   # filter modules by tags
    prefix=None,                 # filter modules by ID prefix
    log_level=None,              # logging level ("DEBUG", "INFO", etc.)
    validate_inputs=False,       # validate inputs against schemas
    metrics_collector=None,      # MetricsCollector for /metrics endpoint
)

Accepts either a Registry or Executor. When a Registry is passed, an Executor is created automatically.

/metrics Prometheus Endpoint

When metrics_collector is provided to serve(), a /metrics HTTP endpoint is exposed that returns metrics in Prometheus text exposition format.

• Available on HTTP-based transports only (streamable-http, sse). Not available with stdio transport.
• Returns Prometheus text format with Content-Type text/plain; version=0.0.4; charset=utf-8.
• Returns 404 when no metrics_collector is configured.

from apcore.observability import MetricsCollector
from apcore_mcp import serve

collector = MetricsCollector()
serve(registry, transport="streamable-http", metrics_collector=collector)
# GET http://127.0.0.1:8000/metrics -> Prometheus text format

to_openai_tools()

from apcore_mcp import to_openai_tools

tools = to_openai_tools(
    registry_or_executor,       # Registry or Executor
    embed_annotations=False,    # append annotation hints to descriptions
    strict=False,               # OpenAI Structured Outputs strict mode
    tags=None,                  # filter by tags, e.g. ["image"]
    prefix=None,                # filter by module ID prefix, e.g. "image"
)

Returns a list of dicts directly usable with the OpenAI API:

import openai

client = openai.OpenAI()
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Resize the image to 512x512"}],
    tools=tools,
)

Strict mode (strict=True): sets additionalProperties: false, makes all properties required (optional ones become nullable), removes defaults.

Annotation embedding (embed_annotations=True): appends [Annotations: read_only, idempotent] to descriptions.

Filtering: tags=["image"] or prefix="text" to expose a subset of modules.

Using with an Executor

If you need custom middleware, ACL, or execution configuration:

from apcore import Registry, Executor

registry = Registry(extensions_dir="./extensions")
registry.discover()
executor = Executor(registry)

serve(executor)
tools = to_openai_tools(executor)

Features

• Auto-discovery — all modules in the extensions directory are found and exposed automatically
• Three transports — stdio (default, for desktop clients), Streamable HTTP, and SSE
• Annotation mapping — apcore annotations (readonly, destructive, idempotent) map to MCP ToolAnnotations
• Schema conversion — JSON Schema $ref/$defs inlining, strict mode for OpenAI Structured Outputs
• Error sanitization — ACL errors and internal errors are sanitized; stack traces are never leaked
• Dynamic registration — modules registered/unregistered at runtime are reflected immediately
• Dual output — same registry powers both MCP Server and OpenAI tool definitions

How It Works

Mapping: apcore to MCP

apcore	MCP
module_id	Tool name
description	Tool description
input_schema	inputSchema
annotations.readonly	ToolAnnotations.readOnlyHint
annotations.destructive	ToolAnnotations.destructiveHint
annotations.idempotent	ToolAnnotations.idempotentHint
annotations.open_world	ToolAnnotations.openWorldHint

Mapping: apcore to OpenAI Tools

apcore	OpenAI
module_id (image.resize)	name (image-resize)
description	description
input_schema	parameters

Module IDs with dots are normalized to dashes for OpenAI compatibility (bijective mapping).

Architecture

Your apcore project (unchanged)
    │
    │  extensions directory
    ▼
apcore-mcp (separate process / library call)
    │
    ├── MCP Server path
    │     SchemaConverter + AnnotationMapper
    │       → MCPServerFactory → ExecutionRouter → TransportManager
    │
    └── OpenAI Tools path
          SchemaConverter + AnnotationMapper + IDNormalizer
            → OpenAIConverter → list[dict]

Development

git clone https://github.com/aipartnerup/apcore-mcp-python.git
cd apcore-mcp
pip install -e ".[dev]"
pytest                           # 260 tests
pytest --cov                     # with coverage report

Project Structure

src/apcore_mcp/
├── __init__.py              # Public API: serve(), to_openai_tools()
├── __main__.py              # CLI entry point
├── adapters/
│   ├── schema.py            # JSON Schema conversion ($ref inlining)
│   ├── annotations.py       # Annotation mapping (apcore → MCP/OpenAI)
│   ├── errors.py            # Error sanitization
│   └── id_normalizer.py     # Module ID normalization (dot ↔ dash)
├── converters/
│   └── openai.py            # OpenAI tool definition converter
└── server/
    ├── factory.py           # MCP Server creation and tool building
    ├── router.py            # Tool call → Executor routing
    ├── transport.py         # Transport management (stdio/HTTP/SSE)
    └── listener.py          # Dynamic module registration listener

License

Apache-2.0