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Neuronpedia MCP Server

Neuronpedia MCP Server

by manncodes
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript

search_top_features

Retrieve the top features with highest activation for any input text across a model layer, enabling neural network interpretability analysis.

Instructions

Find the top activating features for given text

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": {
      "model": "<model>",
      "layer": "<layer>",
      "text": "<text>"
    },
    "name": "search_top_features"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYesInput text to analyze
topKNoNumber of top features to return (default: 10)
layerYesLayer number
modelYesModel name

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, and the description is a single sentence lacking details on side effects, permissions, or data scope. It does not disclose whether the tool is read-only or what 'activating features' entails operationally.

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?

The description is very concise at one sentence, with no wasted words. However, it could benefit from slight restructuring to front-load key information like the tool's purpose and scope.

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?

Given the absence of an output schema and annotations, and the complexity of 'activating features', the description is incomplete. It does not explain what the return value is, how to interpret results, or any limitations.

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 covers 100% of parameters with descriptions, so the description adds no additional meaning beyond what the schema already provides. Baseline score of 3 is appropriate as the schema does the heavy lifting.

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 'Find the top activating features for given text' clearly indicates what the tool does: it retrieves activating features for a text input. It uses a specific verb and resource, and although it doesn't explicitly distinguish from siblings like 'get_activations', the term 'activating features' is distinct enough to convey the tool's purpose.

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 usage guidelines are provided. The description does not specify when to use this tool compared to alternatives like 'get_activations' or 'search_explanations', nor does it mention any prerequisites or exclusions.

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

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