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Nexus Memory

Nexus Memory

by Neboy72
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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find_entities

Query entity-typed memories in Nexus Memory by type and visibility tier, returning matching entities with IDs, names, content, and attributes.

Instructions

Knowledge Graph: Find all entity-typed memories in Qdrant. Returns a list of {id, name, entity_type, content, attributes} dicts.

Input Schema

NameRequiredDescriptionDefault
limitNoMax results (default 50)
entity_typeNoFilter by entity type: device, service, person, location, organization, concept, software, protocol
access_levelNoCaller's visibility tier: only entities at this level or below are returned (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 entities at this level or below are returned (public < trusted < private)",
+  "enum": [
+    "public",
+    "trusted",
+    "private"
+  ],
+  "type": "string"
+}
  2. First observedv0.13.1

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It does disclose the backing store (Qdrant) and the exact return record keys, which compensates for the missing output schema, but says nothing about ordering, pagination beyond the limit param, or whether results are truncated.

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 short sentences, zero filler, with the resource scope front-loaded and the return shape second. Nothing is redundant.

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?

A simple read tool with fully documented params and the return shape spelled out, so an agent can call it correctly. Still missing is any routing context versus the many sibling retrieval tools, which is the main practical risk here.

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% and all three parameters (limit, entity_type, access_level) are fully documented in the schema, including the enum and the visibility-tier semantics. The description adds nothing beyond that, so the baseline of 3 applies.

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?

States a specific verb and resource: 'Find all entity-typed memories in Qdrant', plus the return shape. It is clearly distinguishable from recall/get_related by the 'entity-typed' qualifier, though it never names a sibling to contrast with.

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?

There is no when-to-use guidance at all — nothing says when to call find_entities versus recall, get_related, get_subgraph, or graph_traverse, all of which live in the same namespace and could plausibly return related data.

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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