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plur_extract_meta

Transform stored engrams into structured meta-engrams using a 6-stage pipeline (structural analysis, clustering, alignment, formulation, hierarchy). Requires an LLM API endpoint.

Instructions

Extract meta-engrams from stored engrams using the 6-stage pipeline (structural analysis → clustering → alignment → formulation → hierarchy). Requires an LLM API endpoint.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scopeNoFilter source engrams by scope
domainNoFilter source engrams by domain prefix
dry_runNoIf true, extract but do not persist meta-engrams (default: false)
llm_modelNoModel name (default: gpt-4o-mini)
llm_api_keyYesAPI key for the LLM
llm_base_urlYesOpenAI-compatible API base URL (e.g. https://api.openai.com/v1)
run_validationNoWhether to run cross-domain validation (default: false)
Behavior4/5

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

Annotations (idempotentHint=false, destructiveHint=false) indicate non-idempotent and non-destructive behavior. Description adds pipeline stages and the need for an LLM endpoint, which are not in annotations. Does not conflict with 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, front-loaded sentences. First sentence covers action and pipeline; second states requirement. No waste or redundant information.

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

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers purpose and key requirement but lacks context on return values, error scenarios, or how the output is structured. With no output schema, more detail would be helpful for a tool with 7 parameters.

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?

All 7 parameters have descriptions in the input schema (100% coverage). Description does not add any additional parameter meaning beyond what is already in the schema.

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 the action (extract meta-engrams), specifies the resource (stored engrams), and outlines the 6-stage pipeline. Tool name and title reinforce this purpose, distinguishing it from siblings like plur_meta_engrams and plur_validate_meta.

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?

Description mentions a prerequisite (LLM API endpoint) but does not specify when to use this tool versus alternatives like plur_meta_engrams or plur_validate_meta. No when-not guidance or explicit context for selection.

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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