mcp-memory-graph

memory_extract_entities

Extract entities and relationships from memory content to build a queryable knowledge graph.

Instructions

Store LLM-extracted entities and relationships for a memory. The calling agent should analyze memory content and provide structured entity/relationship data. This enables knowledge graph queries.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "memory_id": "<memory_id>",
      "entities": "<entities>"
    },
    "name": "memory_extract_entities"
  }
}

Input Schema

Name	Required	Description
`memory_id`	Yes	Memory ID to associate extracted entities with
`entities`	Yes	Entities extracted from the memory content
`relationships`	No	Relationships between entities

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.6.4Jun 17, 2026

TDQS

B3.2/5.0

Behavior2/5

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

With no annotations (only openWorldHint, not behavioral), the description carries full burden. It only says 'Store... for a memory,' which indicates a write operation but reveals no side effects (e.g., overwriting existing entities, permissions required, or any destructive behavior). For a mutation tool, this is insufficient transparency.

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?

The description is extremely concise: two sentences, front-loaded with the core action. Every word adds value, with no redundancy. The structure is clear and efficient.

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

Completeness2/5

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

Given the tool has a complex nested input schema, many sibling tools, and no output schema, the description is too minimal. It lacks context on prerequisites (e.g., memory existence), return values, how the tool fits into the larger system of memory tools, and what happens if relationships are omitted. The sentence about knowledge graph queries hints at purpose but doesn't compensate for the missing completeness.

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%, so each parameter is well-described in the schema itself. The description adds no additional semantics beyond what the schema provides (e.g., no explanation of how memory_id must be valid, or behavior on duplicate entities). Baseline score of 3 is appropriate as the schema does the heavy lifting.

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 clearly states the action 'Store LLM-extracted entities and relationships for a memory.' It specifies what the tool does (store structured entity/relationship data) and its benefit (enables knowledge graph queries). However, it does not explicitly distinguish this from siblings like 'memory_extract_learnings' or 'memory_store', which also deal with extraction or storage, missing an opportunity for differentiation.

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

Usage Guidelines3/5

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

The description provides some guidance: 'The calling agent should analyze memory content and provide structured entity/relationship data.' This implies the tool is for storing extracted data after analysis. However, it does not state when to use this tool versus alternatives (e.g., memory_store for raw text, or memory_extract_learnings for patterns), nor does it mention prerequisites like the memory needing to exist or not.

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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