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Vestige

Vestige

by samvallad33
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Rust
Local

importance_score

Score content importance with a neuroscience model that analyzes novelty, arousal, reward, and attention. Returns composite score, channel breakdown, encoding boost, and explanations to prioritize.

Instructions

Score content importance using 4-channel neuroscience model (novelty/arousal/reward/attention). Returns composite score, channel breakdown, encoding boost, and explanations.

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": {
      "content": "<content>"
    },
    "name": "importance_score"
  }
}

Input Schema

NameRequiredDescriptionDefault
contentYesThe content to score for importance
projectNoOptional project/codebase name for context
context_topicsNoOptional topics for novelty detection context

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv2.6.0
  2. Removedv2.3.0
  3. First observedv2.1.27

TDQS

A4/5.0
Behavior4/5

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It does well by naming the scoring model and all return components, making the operation appear non-destructive and read-only. It could go further by specifying output format or failure behavior, but this is a fairly transparent scoring tool.

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?

The description is a single dense sentence with no filler. The core action, model, and output fields are all front-loaded and every word contributes useful information.

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?

There is no output schema, so the description's enumeration of return components provides necessary context. It is reasonably complete for a 3-parameter tool, though specifying the output structure or score scale would make it fully complete.

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 description coverage is 100%, so the baseline is 3. The description indirectly references content and novelty context through the model channels but does not add meaningful parameter details beyond what the schema already documents.

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?

The description names a specific operation ('Score content importance'), the distinctive 4-channel neuroscience model, and the concrete outputs (composite score, channel breakdown, encoding boost, explanations). This clearly distinguishes the tool from generic search or prediction siblings.

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 purpose implies when to use it—when content importance scoring is needed with optional project/topic context. However, it does not explicitly state when to prefer it over siblings such as predict or explore_connections, nor does it provide any 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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