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Metis

by SVerITG
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Metis — KG Index Memory

kg_index_memory

Indexes memory entries into a topic-linked knowledge graph by connecting items with shared topics, making cross-layer connections discoverable.

Instructions

Build a knowledge graph from memory entries, linking items by shared topics.

Scans memory_entries, episodic_memory, semantic_memory, and ideas,
extracts their topic tags, and creates bidirectional edges between
items that share topics. This makes cross-pollination between memory
layers discoverable via kg_memory_connections().

Takes no arguments. Safe to re-run (uses REPLACE semantics).

Returns:
    Summary of nodes and edges indexed.

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.4

TDQS

A4.7/5.0
Behavior5/5

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

No annotations are provided, but the description fully compensates by disclosing key behaviors: it takes no arguments, creates bidirectional edges, uses REPLACE semantics (safe to re-run), and returns a summary. It also explains the outcome (cross-pollination discoverable). No behavioral traits are hidden.

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?

Five short sentences front-loaded with the main action, followed by specifics, safety note, and return value. Every sentence adds value with no redundancy.

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 zero parameters, an existing output schema (mentioned as summary), and no annotations, the description provides a complete picture of what the tool does, what it scans, its idempotency, and its purpose within the knowledge graph workflow.

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?

The tool has zero parameters and schema coverage is 100%. The description confirms 'Takes no arguments,' which is sufficient. Baseline 4 is appropriate since no additional parameter meaning is needed.

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 tool builds a knowledge graph from memory entries by linking shared topics. It lists the specific memory resources scanned and the output (bidirectional edges). This distinguishes it from sibling tools like kg_memory_connections and kg_paths.

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?

Description notes that it is safe to re-run with REPLACE semantics, implying idempotent usage. However, it does not explicitly state when to use this tool over alternatives or when not to use it, though the purpose is clear enough in context.

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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