nimbus-mcp

Official

by nimbusbci

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Python

Hybrid

Load Pipeline

load_pipeline

Retrieve a saved EEG/BCI pipeline by project ID for editing or re-running, including its training graph and metadata.

Instructions

Load a project's saved pipeline (train graph + meta) for editing/re-running.

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": {
      "project_id": "<project_id>"
    },
    "name": "load_pipeline"
  }
}

Input Schema

Name	Required	Description	Default
`project_id`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.5.1Oct 4, 2026

TDQS

B3.2/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses what is loaded (graph + meta) and its purpose, but says nothing about failure modes (e.g., no saved pipeline), permissions, or whether loading mutates session state versus returning the artifact.

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

Conciseness4/5

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

A single dense sentence with the verb and scope front-loaded and useful clarifications in parentheses. Nothing is wasted, though the parenthetical is terse.

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?

An output schema exists, so return values need not be explained. However, given zero annotations and a mutation-adjacent workflow role, the description omits behavioral details such as what happens when no pipeline is saved or how the loaded state is exposed.

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% for the single project_id parameter. The phrase 'a project's saved pipeline' loosely ties project_id to the project, but gives no format, ID scheme, or example, so it barely compensates for the coverage gap.

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?

The description uses a specific verb (load) and resource (a project's saved pipeline), and clarifies the payload as 'train graph + meta' with the intent 'for editing/re-running.' It implicitly contrasts with siblings like save_pipeline and run_pipeline, though it never names them.

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 phrase 'for editing/re-running' hints at the intended workflow and implies this is a prerequisite to editing, but there is no explicit when-to-use vs. alternative guidance and no mention of what to do if no saved pipeline exists.

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