nimbus-mcp

Official

by nimbusbci

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

Export Python

export_python

Export an EEG/BCI pipeline as a standalone runnable Python bundle, saved locally as a zip.

Instructions

Export the pipeline as a standalone runnable Python bundle (zip saved locally).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "train_graph": "<train_graph>"
    },
    "name": "export_python"
  }
}

Input Schema

Name	Required	Description	Default
`name`	No
`train_graph`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.5.1Oct 4, 2026

TDQS

C2.8/5.0

Behavior2/5

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

With no annotations, the description carries the full behavioral burden. It does disclose one side effect (a zip is saved locally), but omits where the file lands, whether it overwrites, what permissions or runtime requirements apply, and whether the exported bundle depends on the current environment.

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?

A single sentence with the action front-loaded and the output artifact in parentheses. Nothing is padded or repeated.

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?

The presence of an output schema means return values need not be described, but for a tool with an undocumented nested required parameter and no annotations, the description leaves too much unspecified: export location, naming via `name`, and what the underlying `train_graph` object must contain.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate and does not: neither the required `train_graph` object (nested, typed as a free-form object) nor the optional `name` parameter is explained. 'The pipeline' loosely hints at train_graph but leaves its expected shape and the purpose of `name` unknown.

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

Purpose4/5

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

States a specific verb (Export) and resource (the pipeline) plus the concrete output form: a standalone runnable Python bundle saved as a local zip. That distinguishes it from siblings like save_pipeline or download_artifact, though it never explicitly names those alternatives.

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

Usage Guidelines2/5

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

No statement of when to use this versus save_pipeline, load_pipeline, or download_artifact, and no prerequisites or exclusions. The only implied guidance is that it produces a Python bundle rather than a pipeline file.

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