Skip to main content
Glama

Learn the artist's house style from the saved vault corpus

learn_from_my_corpus

Analyze your Obsidian vault corpus to extract palette, naming, recipe-shape, and param-default patterns, then write them to Memory/corpus_style.md for consistent style.

Instructions

Offline companion to learn_conventions: walks the Obsidian vault corpus (Recipes/, Components/, Looks/, Setlists/, Moodboards/) and distils palette, naming, recipe-shape, and param-default preferences into Memory/corpus_style.md (and optionally merges palettes/naming/favorite_generators into Memory/style.md). No TouchDesigner required — pure filesystem read. Requires TDMCP_VAULT_PATH (or pass vault_path).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
vault_pathNoOptional vault root override; defaults to TDMCP_VAULT_PATH.
observeNoWhich families to extract; subsets keep the run cheap.
min_supportNoMinimum frequency for a pattern to be recorded.
top_k_paletteNoHow many most-frequent palettes to keep.
dry_runNoIf true, return findings but do NOT write vault notes.
also_patch_style_memoryNoIf confident, merge palettes/naming/favorite_generators into Memory/style.md.
Behavior4/5

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

Description reveals write behavior (to Memory files) despite annotations saying readOnlyHint=false (non-read-only) and destructiveHint=false (non-destructive). Adds context that it is a pure filesystem read but also writes, providing clarity beyond annotations.

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?

Two concise sentences with front-loaded purpose and key details. No redundant information; every word earns its place.

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?

Covers core function, parameter context, behavioral notes, and dependencies. Lacks explanation of return values for dry_run, but does not have an output schema; still sufficient for the tool's complexity.

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?

Schema coverage is 100% with descriptions. Description adds value by noting environment variable TDMCP_VAULT_PATH and hinting that 'observe' subsets keep runs cheap, which aids effective parameter use.

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?

Clearly states it walks the Obsidian vault corpus and distills preferences into Memory/corpus_style.md. Distinguishes from sibling 'learn_conventions' by being an offline companion with no TouchDesigner required.

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?

Explicitly frames as offline companion to 'learn_conventions' and notes no TouchDesigner required, implying when to use. Does not explicitly list alternatives or when-not-to-use, but provides enough context for an AI agent.

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/Pantani/tdmcp'

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