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mimir_extract

Read-only

Extract facts, preferences, and events from text or stored entities with a fully local rule-based extractor. No cloud or network required.

Instructions

Extract structured knowledge — facts, preferences, temporal events, episodes — from raw text or a stored entity, using a fully local, deterministic rule-based extractor (no cloud LLM, no embedding/API call, no network). Read-only: never writes to the store. Provide text, or category + key to extract from a stored entity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyNoKey of a stored entity to extract from (requires category).
textNoRaw text to extract from. If omitted, category + key of a stored entity are used.
categoryNoCategory of a stored entity to extract from (requires key).
strategyNoExtractor strategy: 'rule_based' (local heuristics) or 'none' (no-op).rule_based

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsNoExtracted items, each an object with `kind` and `text`.
totalNoNumber of items extracted
strategyNoExtractor strategy used
Behavior4/5

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

Beyond annotations (readOnlyHint=true), description adds 'never writes to the store' and details on deterministic, local, no-network operation. No contradiction 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 efficient sentences, front-loaded with main purpose. No redundant information.

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?

Given output schema exists, description covers inputs, usage, and behavioral traits sufficiently. No gaps.

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 100%, description adds clarification on usage of `text` vs `category`+`key` and explains `strategy` enum values (local heuristics vs no-op). Adds meaning beyond 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?

Clearly states verb 'extract' and resource 'structured knowledge' from raw text or stored entity. Distinguishes from siblings by specifying local, deterministic rule-based extractor. No tautology.

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?

Explains usage options: provide `text` or `category`+`key` directly. Does not explicitly list when not to use or alternatives, but clear context is given.

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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