Skip to main content

Datalayer

Reactor MCP Server

An extensible MCP server built on Reactor. Three ideas, and the rest follows from them.

A tool is a contribution

An extension does not reach into a server and register a function on it. It offers a tool at a contribution point, and the host builds a server from what has been offered:

from reactor import PluginManifest
from reactor_mcp_server import McpExtension, tool

class Sandboxes(McpExtension):
    def manifest(self) -> PluginManifest:
        return PluginManifest(name="sandboxes", version="1.0.0")

    @tool(title="Launch Sandbox")
    async def launch_sandbox(self, sandbox_name: str, variant: str = "eval") -> dict:
        """Launch a sandbox for this session to run code in."""
        return await launch(sandbox_name, variant)

Offering rather than registering is what makes the next part possible — and it means what a server is made of can be read, listed and drawn before one is built.

A contribution can be extended

An extension may narrow, wrap or re-describe a tool another extension offered:

class DatalayerSandboxes(McpExtension):
    def manifest(self) -> PluginManifest:
        return PluginManifest(name="sandboxes-datalayer", version="1.0.0")

    def tool_extensions(self):
        def on_datalayer(handler):
            async def narrowed(sandbox_name: str, environment: str = "ai-env") -> dict:
                return await handler(sandbox_name, variant="datalayer")
            return narrowed

        return (("launch_sandbox", ToolExtension(
            wrap=on_datalayer,
            description="Launch a sandbox on Datalayer.",
        )),)

Without this, an extension that wants to narrow somebody else's tool has one lever: register a tool of the same name and hope to load second, because the SDK keeps the first registration. That makes the outcome depend on the order two distributions happened to be installed in — it works where the entry point names sort the right way and silently does nothing where they do not. Extensions are applied by name, in a declared order, on every machine.

The URL decides what is served

Tools belong to named toolsets, and a client says which ones it wants in the URL it connects to:

URL What the client gets
https://mcp.example.com/mcp every toolset that is on by default
https://mcp.example.com/mcp?benchmarks the defaults, and benchmarks
https://mcp.example.com/mcp?toolsets=spaces,library the defaults, and those two
https://mcp.example.com/mcp?only=spaces spaces alone, plus the always-on toolsets
https://mcp.example.com/mcp?without=sandboxes the defaults, less that one

A toolset that is default=False costs a client that did not ask for it nothing: the extension offering it declares activation_events=[on_toolset("benchmarks")] and is not registered at all until a URL names it.

GET /toolsets answers what a deployment offers and what a given URL would activate, so a client can be told which name to put in its URL.

Running it

pip install reactor_mcp_server
reactor-mcp-server --port 4040

Every extension installed on the reactor.mcp.extensions entry-point group is discovered, started as a reactor plugin, and served. A deployment adds a toolset by installing a distribution:

[project.entry-points."reactor.mcp.extensions"]
sandboxes = "my_package:SandboxesExtension"

REACTOR_MCP_EXTENSIONS=a,b narrows discovery to the names it lists, which is what makes a tool list reproducible on a machine that happens to carry an extra extension.

In a server of your own

from reactor_mcp_server import build_host, create_mcp_app, load_extensions

host = build_host(load_extensions())
app = create_mcp_app(host, path="/mcp")

McpHost owns the reactor platform, so activation events, enablement, per-tenant scoping and disposal work as they do for any other reactor plugin, and a server is built once per distinct set of toolsets and shared.

License

Datalayer License.

Release files for reactor-mcp-server 1.0.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for reactor-mcp-server 1.0.2
File Size Uploaded
reactor_mcp_server-1.0.2.tar.gz 23.6 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for reactor-mcp-server 1.0.2
File Interpreter ABI Platform
reactor_mcp_server-1.0.2-py3-none-any.whl Python 3 none any Details

Total release size: 46.1 kB

Release files / reactor_mcp_server-1.0.2.tar.gz

Download URL reactor_mcp_server-1.0.2.tar.gz
Size 23.6 kB
Tags Source
SHA-256 checksum
How to use checksums
db7a6c1e73961fb423d709d66993cad7a9fa762f7749cf39895d36d2af926a12
BLAKE2b-256 checksum
How to use checksums
12288d7e5a90cd7bd71a13f5a6afc9dedb06a65fea924c12b428c69eab8d229c
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.11

Release files / reactor_mcp_server-1.0.2-py3-none-any.whl

Download URL reactor_mcp_server-1.0.2-py3-none-any.whl
Size 22.5 kB
Tags Python 3
SHA-256 checksum
How to use checksums
a8ee2eab29dac7a90f63f91582480f6bb6f70b1c5f0ae0dd9a5654993cc8b48e
BLAKE2b-256 checksum
How to use checksums
9ee9114c77e2ef2ea3584d5f8b1e40ea83964b4f039a9daf0519396eb96dc05d
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.11

Release history Release notifications | RSS feed

This release

1.0.2 This release

2 release files

1.0.1

2 release files

1.0.0

1 release file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page