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Nexus Memory

Nexus Memory

by Neboy72
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_subgraph

Retrieves a knowledge graph subgraph centered on a specific fact for visualization, returning nodes and edges up to a chosen depth while filtering by access level.

Instructions

Knowledge Graph: Get a subgraph centered on a fact for visualization. Returns {nodes, edges} where nodes have {id, depth} and edges have {source, target, relation}.

Input Schema

NameRequiredDescriptionDefault
fact_idYesThe Qdrant point ID to center the subgraph on
max_depthNoMaximum hops (default 2)
access_levelNoCaller's visibility tier: only nodes at this level or below are included (public < trusted < private)private

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.22.40
    • addedInput schema / properties / access_level
      Added value: +{
+  "default": "private",
+  "description": "Caller's visibility tier: only nodes at this level or below are included (public < trusted < private)",
+  "enum": [
+    "public",
+    "trusted",
+    "private"
+  ],
+  "type": "string"
+}
  2. First observedv0.13.1

TDQS

B3.1/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It discloses the return shape ({nodes, edges}), but does not explain read-only safety, access filtering, traversal direction, depth effects, or whether restricted nodes can be exposed.

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 sentences, front-loaded with the core action and followed by the return shape. There is no wasted wording.

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

Completeness3/5

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

The description is sufficient to understand the input and return shape, and the schema fully documents the parameters. However, without annotations or an output schema, it leaves gaps around usage versus sibling tools and behavioral details such as access filtering and traversal semantics.

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 description coverage is 100%, so the schema already documents fact_id, max_depth, and access_level. The description adds no parameter-specific semantics beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: 'Get a subgraph centered on a fact for visualization.' It also clarifies the returned structure. It does not distinguish this tool from sibling graph tools such as graph_traverse or get_related.

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

Usage Guidelines2/5

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

The phrase 'for visualization' implies a use case, but the description gives no explicit guidance on when to use this tool versus graph_traverse, get_related, or recall. It also provides no when-not-to-use conditions or prerequisites.

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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