What can you do with this server?

This server provides MCP tools to convert raw OEM energy telemetry into ODS-E records and validate them, using the installed odse Python package. * ConvertToODSE: Convert CSV or JSON telemetry data (provided directly as a text payload or via a file path relative to ODSE_WORKSPACE) into ODS-E energy time-series records using odse.transformer.transform. Supports explicit source (e.g., huawei, enphase) or auto-detection from payload content. Optional parameters: asset_id, timezone, and timeout (up to 600,000 ms). * ListSupportedOEMs: Retrieve the list of OEM source keys supported by the installed odse package, along with the odse version. * ValidateODSERecord: Validate a JSON array of ODS-E records (provided as a JSON string) using odse.validate_batch, with an optional validation level (default: schema). All tools return JSON responses. The server communicates over MCP via stdio, requiring Node.js ≥22 and Python 3 with the odse package installed.

Which integrations are available for this server?

Provides tools for converting Huawei telemetry payloads into ODS-E energy records and validating them according to the odse schema.

How do I use odse-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@odse-mcp Convert this telemetry payload to ODS-E" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

odse-mcp

by AsobaCloud

Overview Schema Related Servers Score Discussions

Python

Local

ODS-E Conversion

MCP server for ODS-E energy telemetry conversion. AI coding/data agents call its tools over the Model Context Protocol to convert OEM payloads into ODS-E records and validate them via the published odse Python package.

npm License: MIT

The installed odse package is the source of truth for OEM support, transform behavior, and validation semantics.

Quick start

Set ODSE_WORKSPACE to an absolute path used when resolving relative payload_file values. If omitted, the server uses its process working directory. The server speaks MCP over stdio (no HTTP port).

Prerequisite: python3 on PATH must be able to import odse (pip install odse).

Cursor

Add to ~/.cursor/mcp.json or the project .cursor/mcp.json:

{
  "mcpServers": {
    "odse": {
      "command": "npx",
      "args": ["-y", "@asobacloud/odse-mcp"],
      "env": {
        "ODSE_WORKSPACE": "/absolute/path/to/your/data"
      }
    }
  }
}

Restart Cursor (or reload MCP servers), then ask the agent to convert or validate ODS-E data.

Claude Code

CLI (user scope):

claude mcp add --transport stdio --scope user \
  --env ODSE_WORKSPACE=/absolute/path/to/your/data \
  odse -- npx -y @asobacloud/odse-mcp

Or put the same JSON under mcpServers in project .mcp.json (team-shared) or ~/.claude.json (user-wide):

{
  "mcpServers": {
    "odse": {
      "command": "npx",
      "args": ["-y", "@asobacloud/odse-mcp"],
      "env": {
        "ODSE_WORKSPACE": "/absolute/path/to/your/data"
      }
    }
  }
}

Verify with claude mcp list. Project .mcp.json servers need approval the first time you open the repo in Claude Code.

Codex

CLI:

codex mcp add odse --env ODSE_WORKSPACE=/absolute/path/to/your/data -- npx -y @asobacloud/odse-mcp

Or edit ~/.codex/config.toml (or project .codex/config.toml in a trusted project):

[mcp_servers.odse]
command = "npx"
args = ["-y", "@asobacloud/odse-mcp"]

[mcp_servers.odse.env]
ODSE_WORKSPACE = "/absolute/path/to/your/data"

Codex CLI, the IDE extension, and the ChatGPT desktop Codex host share this config.

Run directly

pip install odse
npx -y @asobacloud/odse-mcp

Related MCP server: data-bs-mcp

Tools

Tool	Runtime	What it does
`ListSupportedOEMs`	`python3`	Returns OEM source keys from the installed `odse` transformer registry, plus `odse.__version__`.
`ConvertToODSE`	`python3`	Calls `odse.transformer.transform` on raw CSV/JSON text or a file. Optional `source`, `asset_id`, `timezone`, `timeout`. If `source` is omitted, OEM is auto-detected from payload content only.
`ValidateODSERecord`	`python3`	Calls `odse.validate_batch` on a JSON array of records.

ConvertToODSE

Argument	Description
`payload`	Raw telemetry text (CSV or JSON)
`payload_file`	Path to a telemetry file (relative paths resolve against `ODSE_WORKSPACE`)
`source`	OEM key (e.g. `huawei`); omit to auto-detect
`asset_id`	Optional asset id forwarded to `odse.transform`
`timezone`	Optional timezone forwarded to `odse.transform` (e.g. `+02:00`)
`timeout`	Timeout in ms (default `120000`, max `600000`)

Example (explicit source):

{
  "name": "ConvertToODSE",
  "arguments": {
    "source": "huawei",
    "payload": "Time,Active Power(kW),Inverter State\n2024-01-01 12:00:00,12.0,0\n"
  }
}

Successful responses are JSON on the tool text channel, for example:

{
  "records": [
    {
      "timestamp": "2024-01-01T12:00:00Z",
      "kWh": 1.0,
      "error_type": "normal",
      "error_code": "0"
    }
  ],
  "count": 1,
  "source": "huawei",
  "odse_version": "0.8.2"
}

Live-fetch arguments (url, headers, body, …) are rejected: not part of this release.

ValidateODSERecord

Pass records as a JSON string of an array of objects. Optional level is forwarded to odse.validate_batch (default schema).

{
  "name": "ValidateODSERecord",
  "arguments": {
    "records": "[{\"timestamp\":\"2024-01-01T12:00:00Z\",\"kWh\":1.0,\"error_type\":\"normal\"}]"
  }
}

Validation follows odse.validate_batch package semantics (not a separate JSON Schema checker). Extra properties and numeric bounds behave as the installed odse version defines them.

Tool exit code 1 becomes isError: true on the MCP result. Unexpected crashes are reported as errors.

Requirements

Runtime	Required for
Node.js ≥ 22	MCP server
Python 3 + `odse`	All tools (`pip install odse`)

Architecture

rules/*.json          → tool schemas + execution specs
scripts/*.py          → odse wrappers (list / convert / validate)
src/loader.ts         → bundled rules
src/runner.ts         → spawn, timeouts, exit-code → isError
src/index.ts          → MCP stdio server
bin/cli.js            → npx / bin entrypoint

Development

git clone https://github.com/AsobaCloud/odse-mcp.git
cd odse-mcp
python3 -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install odse
npm install
npm run build
npm test

Script	Purpose
`npm run build`	Compile TypeScript → `dist/`
`npm test`	E2E via real MCP `Client` + fixture assertions
`npm start`	Run the server on stdio
`npm run dev`	`node --watch` on `dist/`

Local MCP config (instead of npx):

{
  "mcpServers": {
    "odse": {
      "command": "node",
      "args": ["/absolute/path/to/odse-mcp/dist/index.js"],
      "env": {
        "ODSE_WORKSPACE": "/absolute/path/to/your/data",
        "PATH": "/absolute/path/to/odse-mcp/.venv/bin:/usr/bin:/bin"
      }
    }
  }
}

Ensure the PATH (or environment) makes python3 resolve to an interpreter that can import odse.

Releasing

CI runs build + E2E on every push/PR (Node 22 + pip install odse). To publish a new version:

Bump version in package.json
Commit, push, and create a GitHub Release (gh release create vX.Y.Z --generate-notes)
.github/workflows/publish.yml publishes to npm (Trusted Publisher / OIDC, or NPM_TOKEN if configured)

License

MIT © Asoba

Available Tools

3 tools

ConvertToODSEA

Convert raw OEM energy telemetry (CSV/JSON text or file) into ODS-E energy-timeseries records using odse.transformer.transform. Optionally auto-detect the OEM when source is omitted.

ParametersJSON Schema

Name	Required	Description
`source`	No	OEM source key (e.g. huawei, enphase). If omitted, auto-detect from payload content.
`payload`	No	Raw telemetry payload text (CSV or JSON)
`timeout`	No	Timeout in ms (max 600000)
`asset_id`	No	Optional asset id forwarded to odse.transform
`timezone`	No	Optional timezone forwarded to odse.transform (e.g. +02:00)
`payload_file`	No	Path to a telemetry file (relative paths resolve against ODSE_WORKSPACE)

TDQS

A4.1/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must carry full behavioral transparency. It discloses that it uses odse.transformer.transform and can auto-detect the OEM, which gives some insight into internal logic. Yet it does not mention potential side effects, handling of conflicting payload/payload_file inputs, or return value shape, leaving the behavior only partially transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences long, front-loaded with the primary purpose and the key optional behavior. Every word contributes meaning, with no redundant information or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has 6 optional parameters and no output schema, so the description needs to clarify how inputs interact and what is returned. It explains the core transformation and auto-detection but does not cover cases like supplying both payload and payload_file, or describe the output format. The description is adequate but leaves notable gaps for an agent to safely invoke the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds value by clarifying that 'CSV/JSON text or file' encompasses both the payload and payload_file parameters, and by explaining the behavior when source is omitted (auto-detection). This goes beyond the individual parameter descriptions and aids selection.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool converts raw OEM energy telemetry (CSV/JSON text or file) into ODS-E energy-timeseries records, naming the specific transformation function. It distinguishes from siblings by using 'Convert' versus 'List' or 'Validate', leaving no ambiguity about the tool's role.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description makes it clear that the tool is for converting raw telemetry into ODS-E records, which is a distinct use case from listing OEMs or validating records. It also provides usage nuance by noting that OEM auto-detection occurs when source is omitted. However, it does not explicitly mention when not to use this tool or compare with alternatives, so a 4 is appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

ListSupportedOEMsA

List OEM source keys supported by the installed odse package, plus the odse package version.

ParametersJSON Schema

Name	Required	Description	Default
`timeout`	No	Timeout in ms (max 600000)

TDQS

A3.7/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the transparency burden. The verb 'List' implies a read-only operation, and mentioning the package version adds context, but it does not disclose potential errors, timeout behavior, or output format beyond the literal listing. This is adequate but not rich.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence that states both the primary action and the additional version output. No filler or redundant information; every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple list operation with no output schema, the description conveys the core output (OEM keys and version) but omits details like the structure of the returned keys or the behavior when the package has no supported OEMs. Given the tool's simplicity and zero annotations, this is minimally complete but not exhaustive.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The only parameter, timeout, is fully documented in the schema with a description and range, so schema coverage is 100%. The description does not require additional parameter detail, and the baseline of 3 applies because the schema already provides what the agent needs.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb 'List' and identifies the resource 'OEM source keys supported by the installed odse package', plus an additional output of the package version. This clearly distinguishes it from sibling tools that convert or validate records.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage: call this tool to discover available OEM source keys and the installed odse package version. However, it does not explicitly state when to prefer this over the sibling tools (ConvertToODSE, ValidateODSERecord) or provide exclusions, so guidance is implicit rather than explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

ValidateODSERecordB

Validate a JSON array of ODS-E records using odse.validate_batch (package semantics).

ParametersJSON Schema

Name	Required	Description
`level`	No	odse validation level (default: schema)
`records`	Yes	JSON array of ODS-E record objects
`timeout`	No	Timeout in ms (max 600000)

TDQS

B3.4/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, and the description only mentions 'package semantics' without detailing error behavior, side effects, or permissions. It does not disclose what happens on invalid records or the return format.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, focused sentence that uses the imperative verb and is easy to parse. No filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a validation tool with no output schema and no annotations, the description omits expected return values and error semantics. It is a superficial overview rather than a complete spec.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema fully describes all parameters, so the description adds little beyond the schema. The mention of batch validation is mildly informative but does not clarify parameter-specific behavior.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action (validate) and the resource (JSON array of ODS-E records), and references the underlying package for semantics. It is distinct from sibling tools that convert or list.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not explicitly state when to use this tool versus the siblings. It implies a validation use case but lacks guidance on prerequisites, alternatives, or conditions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

3 tool updatesv1.0.0Aug 8, 2026
- First observedConvertToODSE
- First observedListSupportedOEMs
- First observedValidateODSERecord

Install Server

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TDQS

A4/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: conversion, listing supported OEMs, and validation. There is no functional overlap between them, making misselection unlikely.

Naming Consistency5/5

All tool names follow the same Verb+Object pattern (ConvertTo, ListSupported, Validate) with consistent PascalCase. The naming convention is uniform and predictable.

Tool Count5/5

With 3 tools, the server is well-scoped for its stated purpose of converting and validating ODS-E records. Each tool is necessary and the count is within the ideal 3-15 range.

Completeness5/5

The tool surface covers the core workflow: convert data, list supported sources, and validate output. There are no obvious missing operations for the declared domain of ODS-E conversion and validation.

Maintenance

ActivityMaintained

ResponsivenessSyncing

Resources

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