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Knowledge Graph Memory Server

Knowledge Graph Memory Server

by modelcontextprotocol
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

Search Nodes

search_nodes
Read-onlyIdempotent

Search a local knowledge graph by query to find entities, types, or observations, so Claude can recall user details across chats.

Instructions

Search for nodes in the knowledge graph based on a query

Input Schema

NameRequiredDescriptionDefault
queryYesThe search query to match against entity names, types, and observation content

Output Schema

NameRequiredDescriptionDefault
entitiesYes
relationsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv2.0.0
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • changedOutput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  2. Changed3 schema fields changed
    • addedInput schema / $schema
      Added value: +"http://json-schema.org/draft-07/schema#"
    • addedInput schema / properties / query / maxLength
      Added value: +2048
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": false,
+  "properties": {
+    "entities": {
+      "items": {
+        "additionalProperties": false,
+        "properties": {
+          "entityType": {
+            "description": "The type of the entity",
+            "type": "string"
+          },
+          "name": {
+            "description": "The name of the entity",
+            "type": "string"
+          },
+          "observations": {
+            "description": "An array of observation contents associated with the entity",
+            "items": {
+              "type": "string"
+            },
+            "type": "array"
+          }
+        },
+        "required": [
+          "name",
+          "entityType",
+          "observations"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "relations": {
+      "items": {
+        "additionalProperties": false,
+        "properties": {
+          "from": {
+            "description": "The name of the entity where the relation starts",
+            "type": "string"
+          },
+          "relationType": {
+            "description": "The type of the relation",
+            "type": "string"
+          },
+          "to": {
+            "description": "The name of the entity where the relation ends",
+            "type": "string"
+          }
+        },
+        "required": [
+          "from",
+          "to",
+          "relationType"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    }
+  },
+  "required": [
+    "entities",
+    "relations"
+  ],
+  "type": "object"
+}
  3. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint=false, so safety and repeatability are covered. The description adds nothing beyond that — no matching behavior (fuzzy vs exact), ranking, or result limit context — so it neither contradicts nor enriches the structured data.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

One short, front-loaded sentence with no filler. It is efficient, though its brevity is also the source of the missing guidance rather than a virtue of tight editing.

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?

An output schema exists, so return structure need not be explained, and with one fully documented parameter the surface is small. Still, the description omits how search results are matched or ordered, which matters for a search tool with several retrieval siblings.

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 the single parameter is fully documented in the schema (matching names, types, and observation content). The description only restates that a query drives the search, adding no syntax or format detail. Baseline 3 is appropriate.

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+resource ('Search for nodes in the knowledge graph') with the query basis made explicit. However, it offers no differentiation from siblings like open_nodes or read_graph, so an agent cannot tell from the description alone which retrieval tool fits which situation.

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 guidance on when to use search over alternatives such as open_nodes (direct lookup) or read_graph (full dump). No exclusions or preconditions are stated; the agent must infer the use case.

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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