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enrich

Add structured metadata to existing memories using LLM extraction, enabling advanced filtering by topics, sentiment, entities, and categories.

Instructions

Enrich memories with LLM-extracted metadata (topics, sentiment, entities, categories). USE THIS WHEN: you want to add structured metadata to existing memories for better filtering. Requires LORE_ENRICHMENT_ENABLED=true and a configured LLM provider (LORE_LLM_PROVIDER + API key). Enrichment runs automatically on remember() when enabled; use this tool to enrich older memories.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "enrich"
  }
}

Input Schema

Name	Required	Description	Default
`all`	No
`force`	No
`project`	No
`memory_id`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jun 24, 2026

TDQS

A4.1/5.0

Behavior4/5

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

No annotations provided, so description carries full burden. It discloses that the tool requires configuration and that it is the manual counterpart to automatic enrichment. However, it does not specify whether enrichment overwrites existing metadata or is idempotent.

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?

Very concise: two sentences plus a usage directive. Front-loaded with the core purpose. No superfluous information.

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

Completeness2/5

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

Given 4 parameters, no annotations, and an output schema that is not described, the description is incomplete. It does not explain how parameters relate to each other, what the output contains, or edge cases like setting 'all' vs 'memory_id'.

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

Parameters2/5

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

Schema has 4 parameters with 0% description coverage. The description does not explain the meaning of individual parameters like 'memory_id', 'all', 'project', or 'force'. It only loosely implies targeting via the phrase 'enrich older memories'.

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?

The description clearly states the verb 'enrich', resource 'memories', and output 'LLM-extracted metadata (topics, sentiment, entities, categories)'. This distinguishes it from sibling tools that perform different operations like classification or extraction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use: 'USE THIS WHEN: you want to add structured metadata to existing memories for better filtering'. It also explains that automatic enrichment runs on remember() and this tool is for older memories, and lists prerequisites (LORE_ENRICHMENT_ENABLED=true, configured LLM provider).

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