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Mnemograph

Mnemograph

by tm42
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_graph_health

Monitor and improve knowledge graph health by detecting orphans, duplicates, overloaded entities, and weak relations. Use periodic checks for graph hygiene.

Instructions

Assess knowledge graph quality. Returns: orphan count, potential duplicates, overloaded entities, weak relations. Run periodically to maintain graph hygiene. Use full=true for deep duplicate detection (slower).

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": "get_graph_health"
  }
}

Input Schema

NameRequiredDescriptionDefault
fullNoIf true, include expensive duplicate detection. Default false for fast checks.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.1

TDQS

A4.2/5.0
Behavior3/5

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

No annotations provided, so description carries the burden. It mentions that full=true is slower, but does not explicitly state read-only nature or side effects. For a health check tool, this is adequate but could be more explicit.

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?

Three sentences, front-loaded with purpose. No wasted words. Every sentence adds information.

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?

Explains return values (orphan count, etc.) and parameter behavior. For a simple tool with one optional parameter and no output schema, it is mostly complete. Could mention if it is a read operation, but not required.

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%, baseline 3. Description adds value by explaining that full=true triggers 'deep duplicate detection (slower)', which goes beyond the schema's description of 'expensive duplicate detection'.

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's purpose: 'Assess knowledge graph quality' and lists specific outputs (orphan count, potential duplicates, overloaded entities, weak relations). This distinguishes it from sibling tools like find_orphans, find_similar, and get_weak_relations.

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?

Provides guidance to 'Run periodically to maintain graph hygiene' and explains the full parameter tradeoff. Lacks explicit alternatives or exclusions, but context is clear.

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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