Allotrope MCP Server

A Model Context Protocol (MCP) server that provides tools for working with Allotrope Simple Model (ASM) data formats. This server enables AI assistants to validate instrument data files against ASM schemas and discover available ASMs.

What is Allotrope?

Allotrope is a data standards framework for laboratory and analytical instrument data. The Allotrope Simple Model (ASM) provides a standardized JSON format for representing instrument data, making it easier to integrate, analyze, and share scientific data across different systems and organizations.

Related MCP server: Trace MCP

Features

This MCP server provides the following tools:

• describe_asm: Retrieve full metadata for a specific ASM model by name, including its description, manifest URL, JSON schema URL, and data instance example URLs

• fetch_asm_document: Download a raw ASM JSON document from purl.allotrope.org to the local filesystem at a path mirroring the URI structure

• list_asms: List all available Allotrope Simple Models (ASMs) with their descriptions from a bundled reference file

• validate_asm_schema: Validate ASM JSON documents against their corresponding JSON schemas to verify data compliance

• validate_field_map: Validate a field mapping file produced by a custom converter script, comparing source values against ASM values to confirm data integrity

Installation

Prerequisites

• Python 3.10 or higher

• uv package manager

Install MCP server

Add the following configuration to your MCP client to download and install the server.

{
  "mcpServers": {
    "allotrope-mcp-server": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/aws-samples/sample-laboratory-data-transformation-mcp.git",
        "allotrope-mcp-server"
      ],
      "disabled": false,
      "autoApprove": [],
      "disabledTools": []
    }
  }
}

Setup for local development

# Clone the repository
git clone <your-repo-url>
cd allotrope-mcp-server

# Install dependencies
uv sync

# Run the server
uv run allotrope-mcp-server

Workflow Diagram

sequenceDiagram
    participant Client as MCP Client<br/>(AI Assistant / IDE)
    participant Server as allotrope-mcp-server<br/>(FastMCP / stdio)
    participant FS as Local Filesystem
    participant Ref as model_reference.json<br/>(bundled)
    participant PURL as purl.allotrope.org<br/>(HTTPS)

    Note over Client,Server: Trust Boundary: stdio (no auth)

    Client->>Server: list_asms()
    Server->>Ref: read model_reference.json
    Ref-->>Server: ASM name → description map
    Server-->>Client: JSON result

    Client->>Server: describe_asm(model_name)
    Server->>Ref: lookup model_name
    Ref-->>Server: metadata (URIs, description)
    Server-->>Client: JSON result

    Client->>Server: validate_asm_schema(asm_document_path, asm_schema_path)
    Note over Server,FS: Path traversal risk (T1) — sanitised by M1
    Server->>FS: read asm_document_path
    FS-->>Server: ASM JSON document
    Server->>FS: read asm_schema_path
    FS-->>Server: JSON Schema
    Server->>Server: validate document against schema
    Server-->>Client: validation result

    Client->>Server: fetch_asm_document(asm_document_uri, output_dir)
    Note over Server: URI prefix check (PURL_ORIGIN allowlist — A002)
    Note over Server,PURL: TLS encrypted (CN001) — MitM risk (T5)
    Server->>PURL: GET asm_document_uri (HTTPS)
    PURL-->>Server: ASM JSON document
    Note over Server,FS: Arbitrary write risk (T2) — sanitised by M1
    Server->>FS: write to output_dir/<path>
    Server-->>Client: saved file path

Integration with AI coding tools

Kiro

This repo includes a Kiro Power in the power-instrument-data-to-allotrope/ folder. The power bundles the MCP server configuration and a guided workflow for converting laboratory instrument data into valid ASM JSON.

Install the Power

1. Open Kiro and go to the Powers panel (click the Powers icon in the sidebar, or run View: Show Powers from the command palette).

2. Click Add Custom Power and then Import power from a folder. Select the power-instrument-data-to-allotrope/ directory from this repo.

3. Kiro will register the allotrope-mcp-server MCP server automatically using the bundled mcp.json.

Use the Power

Once installed, open a new chat and type / to browse available powers. Select Instrument Data to Allotrope and provide:

• input_path — path to your instrument data file

• asm_model — the target ASM model name (e.g. plate-reader)

• output_path (optional) — destination for the generated ASM JSON (defaults to <input_path>.asm.json)

Kiro will guide you through schema discovery, data parsing, code generation, and validation against the ASM schema.

Agent Skill

The repo also includes an Agent Skill at .agents/skills/instrument-data-to-allotrope/SKILL.md. Skills follow an open standard and can be imported into Kiro (or any compatible AI tool) independently of the Power.

Note: The skill requires the allotrope-mcp-server MCP server to be connected. Use the Power (above) to configure it automatically, or add the server manually via the MCP settings.

Usage Examples

Once configured in Kiro, you can use natural language to interact with the tools:

• "List all available ASMs"

• "Describe the plate-reader ASM model"

• "Validate this ASM document against the plate reader schema"

• "Check if my instrument data file is valid ASM format"

• "Fetch the plate reader embed schema document to my project"

Example: Validating an ASM Document

You: Validate tests/testdata/plate_reader_weyland_yutani_valid.json 
     against tests/testdata/plate_reader.embed.schema.json

Kiro will use the validate_asm_schema tool to check the document and report any validation errors.

Example: Fetching a Raw ASM Document

You: Download the plate reader schema document to my project

Kiro will use the fetch_asm_document tool to download the raw JSON document from purl.allotrope.org and save it locally at a path that mirrors the URI structure.

Tool Reference

describe_asm

Returns the full metadata for a specific ASM model by name. Looks up the model in the bundled model_reference.json and returns its description, manifest URL, JSON schema URL, and data instance example URLs as a JSON string.

Parameters:

Returns: A JSON object with the model metadata on success, or an object with an error key and a valid_model_names list if the model name is not recognised.

Example response (success):

{
  "description": "...",
  "asm_manifest": "http://purl.allotrope.org/manifests/...",
  "asm_json_schema": "http://purl.allotrope.org/json-schemas/...",
  "asm_data_instance_examples": ["http://purl.allotrope.org/test/..."]
}

fetch_asm_document

Downloads a raw ASM JSON document from the Allotrope PURL repository (purl.allotrope.org) and saves it to the local filesystem at a path that mirrors the URI structure. $ref references are not resolved — the document is saved exactly as received.

Parameters:

Behavior:

• Rejects URIs that do not start with http://purl.allotrope.org (case-sensitive) — no network call is made on rejection

• If the file already exists at the derived local path, returns the path immediately without re-downloading

• Downloads the document and validates it is well-formed JSON before writing

• Creates parent directories as needed and saves the document as UTF-8 JSON with 2-space indentation

• Returns a JSON object with a path key on success, or an error key on failure

Example response (success):

{"path": "/absolute/path/to/json-schemas/adm/plate-reader/REC/2025/12/plate-reader.embed.schema"}

list_asms

Lists all available Allotrope Simple Models (ASMs) with their descriptions. Reads from the bundled model_reference.json file and returns a mapping of ASM IDs to descriptions.

Parameters: None

Returns: A JSON object mapping ASM identifiers to their descriptions, or an error key on failure.

validate_asm_schema

Validates an ASM JSON document against its corresponding JSON schema.

Parameters:

validate_field_map

Validates a field mapping file produced by a custom converter script. Reads the JSON file and compares each entry's source_value against its asm_value using string equality (primary) and numeric float equality (fallback). Returns a structured result with match counts, mismatches, and a summary message.

Parameters:

Returns: A JSON object with matched, total, mismatches, and message keys on success, or an error key on failure.

Example response (all match):

{
  "matched": 19,
  "total": 19,
  "mismatches": [],
  "message": "The conversion script accurately reproduced all 19 field(s) from the raw data file."
}

Example response (with mismatches):

{
  "matched": 17,
  "total": 19,
  "mismatches": [
    {
      "source_field": "Recorded",
      "source_value": "2023-10-26:11:15:40",
      "asm_field": "measurement time",
      "asm_value": "2023-10-26T11:15:40+00:00",
      "unit": ""
    }
  ],
  "message": "The conversion script needs to be updated to address 2 mismatched field(s)."
}

Note: Entries where asm_value is an ISO 8601 normalised form of source_value (e.g. timestamp conversion) will appear as mismatches. This is intentional — the tool surfaces all value divergences so the developer or AI agent can review whether they are acceptable.

Security Considerations

What the server provides

• Path traversal protection — validate_asm_schema and fetch_asm_document resolve and sanitise all caller-supplied file paths. Paths that escape the intended working directory are rejected before any file I/O occurs.

• HTTPS-only external requests — fetch_asm_document enforces a hard-coded http://purl.allotrope.org URI prefix check. Any URI that does not match this origin is rejected without making a network call.

• File size limits — validate_asm_schema enforces a maximum file size before reading documents or schemas into memory, preventing resource exhaustion from oversized inputs.

• Recursive schema depth limit — JSON Schema validation caps recursion depth to guard against stack overflow or CPU exhaustion from schemas with circular $ref cycles.

• Error message sanitisation — internal filesystem paths and stack traces are stripped from error responses returned to the MCP client.

What you are responsible for

• Securing your local environment — the server runs as a local process with the same filesystem permissions as the invoking user. Ensure your machine, user account, and any Docker container running the server are appropriately hardened.

• Validating AI assistant behavior — the server trusts all tool arguments passed by the MCP client without authenticating the caller. A compromised or misbehaving AI assistant could supply malicious file paths or URIs. Review tool invocations in your IDE and treat unexpected calls as suspicious.

• Prompt injection awareness — content fetched from purl.allotrope.org or read from local files is returned to the AI assistant. Malicious content in those files could attempt to influence subsequent assistant actions (indirect prompt injection). Only point the server at files and URIs you trust. See Prompt Injection below for details.

• Supply chain hygiene — install the package from the official PyPI release and pin dependency versions using uv.lock. Verify that your Python environment has not been tampered with before running the server.

• No authentication layer — the MCP stdio interface has no built-in authentication. If you expose the server beyond a local process (e.g. via a network socket), you are responsible for adding appropriate access controls.

Prompt Injection

The MCP server passes tool arguments supplied by an AI assistant directly to filesystem and network operations. Because the server cannot distinguish a legitimate assistant request from one that has been manipulated by malicious content, all MCP tool arguments must be treated as untrusted input.

Indirect prompt injection can occur when:

• A document or schema file read by validate_asm_schema contains embedded instructions that the AI assistant interprets as commands.

• A JSON document fetched from purl.allotrope.org by fetch_asm_document contains text that causes the assistant to invoke further tool calls with attacker-controlled arguments.

• An AI agent loop passes the output of one tool call as the input path or URI of the next without human review.

Recommended mitigations for MCP client operators:

• Review tool invocations before approving them, especially calls that supply file paths or URIs you did not explicitly request.

• Avoid chaining tool outputs directly into subsequent tool inputs without inspecting the intermediate content.

• Restrict the working directory available to the server so that even a successful path traversal attempt cannot reach sensitive files outside the project.

• Treat any unexpected or unsolicited tool call as a potential injection attempt and abort the session.

Development

Running Tests

uv run pytest --cov --cov-branch --cov-report=term-missing

Linting and Formatting

uv run ruff check .
uv run ruff format .

Type Checking

uv run pyright

Resources

• Allotrope Foundation

• Model Context Protocol Documentation

License

This project is licensed under the MIT-0 license. See the LICENSE file for details.

The Allotrope Foundation® Simple Models (“ASM”) and other data is collectively licensed under three licenses, depending on intended usage and membership status. Please visit https://gitlab.com/allotrope-public/asm/-/blob/main/LICENSE.md for more information.