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Graph-Memory

by stevepridemore
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TypeScript
Hybrid

Detect Knowledge Communities

graph_communities
Read-only

Find clusters of densely interconnected entities in a knowledge graph. Each entity belongs to one community, helping you explore related groups without specifying a starting point.

Instructions

Find clusters of densely-interconnected entities in the graph. Uses greedy seed-based BFS through edges above the weight threshold — works without GDS or APOC. Each entity is assigned to at most one community (the first that reaches it from a high-degree seed). Useful for understanding knowledge neighbourhoods (e.g. "everything related to infrastructure"). Returns at most max_communities clusters, each shaped {community_id, seed: {id, name, type}, size, members: [{id, name, type}]}, sorted by size desc; communities below min_size are filtered out. Use graph_query or graph_search instead when you have a specific entity to start from.

Input Schema

NameRequiredDescriptionDefault
max_hopsNoBFS depth from each seed (default 3, capped at 4).
min_sizeNoMinimum members for a community to be returned (default 2).
max_communitiesNoMaximum number of communities to return (default 10).
weight_thresholdNoOnly traverse edges with weight strictly greater than this (default 0.4).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.2.2
  2. Removedv0.1.4
  3. First observedv1.0.0

TDQS

A4.6/5.0
Behavior5/5

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

Annotations indicate read-only; description adds that no GDS/APOC is needed, explains seed selection and BFS behavior, and notes community assignment logic, providing rich context beyond 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?

Four sentences covering purpose, algorithm, use case, output format, and alternatives—no wasted words, front-loaded with key 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?

Covers algorithm details, return structure, filtering, and alternative tools. Minor gap: no mention of behavior when no communities are found, but otherwise thorough.

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?

Schema coverage is 100% with clear descriptions for each parameter. The description reaffirms parameter roles but does not add significant new meaning beyond 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?

Clearly states the tool finds clusters of densely-interconnected entities, describes the algorithm and output format, and distinguishes from siblings by advising when to use alternatives.

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 advises to use graph_query or graph_search when starting from a specific entity, and explains the algorithmic constraints (greedy seed-based BFS, weight threshold, unique community assignment).

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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