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ms_search_nodes

Search MetaSounds nodes by natural-language purpose or effect, such as underwater or random variation, and get ranked matches with names, categories, and descriptions.

Instructions

Semantic search for MetaSounds nodes by description or purpose.

Uses TF-IDF embeddings for meaning-based search with tag fallback. Examples: "underwater effect", "random variation", "smooth transition"

Args: query: Natural language search query

Returns: JSON with ranked results (name, score, category, description).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.0.2

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden. It discloses the search mechanism ('TF-IDF embeddings for meaning-based search with tag fallback') and the return shape ('ranked results (name, score, category, description)'), which gives an agent useful context. It does not mention permissions or rate limits, but these are less critical for a read-only search 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 front-loaded with the core purpose, followed by method, examples, and structured Args/Returns sections. Every sentence adds value without redundancy or waste.

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?

For a simple search tool with one parameter and an output schema, the description covers purpose, method, parameter meaning, and return structure adequately. The only notable gap is the lack of explicit routing to sibling tools, but otherwise it provides what an agent needs to invoke the tool correctly.

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?

The input schema provides no description for the single query parameter (0% coverage), so the description must compensate. It does so by describing it as a 'Natural language search query' and providing example queries, which clarifies the expected format far beyond the schema's bare type information.

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 states a specific verb and resource: 'Semantic search for MetaSounds nodes by description or purpose.' It clearly communicates what the tool does, but does not explicitly differentiate itself from sibling tools like ms_list_nodes or ms_node_info, which likely cover browsing or fetching node details.

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?

Usage is implied through examples such as 'underwater effect' and 'random variation,' which show the kind of natural-language queries expected. However, there is no explicit guidance on when to use this semantic search versus alternatives like ms_list_nodes or browsing categories, and no exclusions are stated.

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