Skip to main content
Glama
nobulart

octane-mcp

by nobulart

octane_find_grammar

Search a reference corpus for the closest prior grammar to warm-start a new subject. Returns ranked matches with pixel-derived acceptance spec for conditioning.

Instructions

WP9 RAGS retrieval: find the nearest existing corpus grammar to warm-start a new subject.

Searches the harvested reference corpus (corpus/) for entries whose labels / domain / subject / title / dominant colors match query. Returns ranked matches, each carrying its pixel-derived derived_acceptance spec so a new render can be conditioned against the closest prior reference. Pure offline ranking: keyword + hue-overlap + era, no embeddings, no network.

Args: query: free-text subject, e.g. "red sphere" or "blue ceramic vase". top_k: max matches to return (default 3). domain: optional domain filter (e.g. "photoreal", "stylized"). only_converged: if true, only return entries that have a rendered preview.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
top_kNo
domainNo
only_convergedNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Despite no annotations, the description reveals that the tool does pure offline ranking (keyword, hue-overlap, era), no embeddings, no network. It returns ranked matches with derived_acceptance spec. This is sufficiently transparent for a retrieval 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 concise and well-structured: a one-line summary, a paragraph of behavioral details, and a bulleted parameter list. Every sentence adds value without unnecessary repetition.

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

Completeness5/5

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 needs only to hint at return values, which it does ('ranked matches...derived_acceptance spec'). It covers purpose, parameters, and behavior adequately for the tool's complexity (4 params, no nesting).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage was 0%, but the description includes an Args section that explains each parameter in detail: query (with examples), top_k (default 3), domain (with examples of filters), only_converged (boolean condition). This fully compensates for the lack of schema descriptions.

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 opens with a clear, specific verb+resource: 'find the nearest existing corpus grammar to warm-start a new subject.' It distinguishes itself from sibling tools (which are about building, rendering, etc.) by focusing on retrieval and ranking.

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?

The description implies usage for initializing a new subject by finding a close prior reference. It notes it's offline and uses specific ranking methods, giving context. However, it does not explicitly state when not to use this tool or mention alternative tools for similar tasks.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/nobulart/octane-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server