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Neuronpedia MCP Server

Neuronpedia MCP Server

by manncodes
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript

get_activations

Retrieve activation values for a specific neuron feature by analyzing input text across model layers. Understand feature responses to interpret model behavior.

Instructions

Get activation values for a specific feature on 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>",
      "feature": "<feature>",
      "text": "<text>"
    },
    "name": "get_activations"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYesInput text to analyze
layerYesLayer number
modelYesModel name
featureYesFeature number

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.1/5.0
Behavior2/5

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

No behavioral traits disclosed beyond the basic action. No annotations provided, so description carries full burden but fails to mention error handling, performance, or side effects.

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?

Single sentence is concise and front-loaded with key action. However, it could be slightly more informative without losing conciseness.

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?

No output schema and 4 required parameters; description does not explain return values or behavior, making it incomplete for effective use.

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?

Schema coverage is 100% with basic descriptions. Tool description adds no additional meaning beyond schema, so baseline score of 3 applies.

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?

Description clearly states verb 'Get' and resource 'activation values', and specifies the target (specific feature on given text). It differentiates from siblings like generate_explanation by focusing on activation retrieval.

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 guidance on when to use this tool vs alternatives like steer_generation or search_top_features. No context on prerequisites or when not to use.

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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