nimbus-mcp

Official

by nimbusbci

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

Validate Pipeline

validate_pipeline

Validate an EEG/BCI pipeline graph before running to catch invalid nodes or connections and prevent failed execution.

Instructions

Validate a pipeline graph before running. ExecGraphSnapshot: {nodes: [{id, type, config}], connections: [{from, to}]}. Build it from get_template(id).train or from scratch using list_nodes().

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": "validate_pipeline"
  }
}

Input Schema

Name	Required	Description	Default
`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

A4.2/5.0

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 implies a non-mutating pre-flight check ('before running'), which is useful context, but it says nothing about what happens on failure, whether it blocks execution, or any auth/permission needs. The presence of an output schema lowers the bar since return values need not be described here.

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?

Purpose is front-loaded in the first clause, followed by the input structure and how to build it. Three compact sentences, each earning its place with no filler.

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

Completeness4/5

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

With an output schema present, the description only needs to cover purpose, input construction, and execution context, which it does. It is nearly complete, though it leaves unstated what kinds of problems validation catches and whether it is purely a dry-run.

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 description coverage is 0% and the single parameter is a loosely-typed object, so the description must compensate. It does so by spelling out the ExecGraphSnapshot shape (nodes with id/type/config, connections with from/to), which meaningfully exceeds the bare 'type: object' in the schema, though it doesn't detail the per-node config fields.

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?

States a specific verb (validate) and resource (a pipeline graph), and adds the timing qualifier 'before running'. This naturally distinguishes it from the sibling validate_node_config, which operates on a single node, without the agent needing to open either schema.

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?

Gives clear context for when to reach for it ('before running') and even routes the agent to sibling tools for building the input ('get_template(id).train', 'list_nodes()'). It stops short of explicit when-not guidance or a direct comparison to alternatives like validate_node_config.

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