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memory_graph

Read an entity's graph neighborhood to retrieve nodes, typed edges, canonical facts, and derived relationships, helping explore persistent long-term memory across sessions.

Instructions

Read an entity's graph neighborhood: nodes, typed edges, and each node's canonical facts. Transitive/inverse edges arrive pre-derived (marked derived: true with rule provenance).

Returns: {found, entity, nodes, edges, paths}.

Input Schema

TableJSON Schema

Name	Required	Description
`to`	No	Also return the shortest path from ``entity`` to this entity under ``paths``; its nodes are folded into the neighborhood.
`depth`	No	Hops from the root. Max 3.
`entity`	Yes	The root entity to read out from.
`include_facts`	No	False omits each node's canonical facts.
`relation_filter`	No	Keep only edges whose relation contains this substring.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed5 schema fields changedv0.15.0Sep 3, 2026

addedInput schema / properties / depth / description
```
Added value: +"Hops from the root. Max 3."
```
addedInput schema / properties / entity / description
```
Added value: +"The root entity to read out from."
```
addedInput schema / properties / include_facts / description
```
Added value: +"False omits each node's canonical facts."
```

addedInput schema / properties / relation_filter / description

Added value: +"Keep only edges whose relation contains this substring."

addedInput schema / properties / to / description

Added value: +"Also return the shortest path from ``entity`` to this entity under ``paths``; its nodes are folded into the neighborhood."

First observedv0.11.0Jul 31, 2026

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations, the description carries the behavioral burden. It discloses a non-obvious behavior: transitive/inverse edges arrive pre-derived and are marked derived: true with rule provenance. The word 'Read' plus the Returns line also signals a non-mutating query operation.

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?

Three short sentences with no filler: the action is in the first sentence, the notable derived-edge behavior in the second, and the return contract in the third. Every sentence earns its place and is front-loaded.

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?

Given a rich input schema, an output schema, and a return-shape overview in the description, the tool is adequately documented. A small gap is that the description does not explicitly mention depth/relation-filter behavior, though those are fully covered in the schema.

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 all five parameters. The description does not add parameter-level detail beyond what the schema provides, so the baseline of 3 is appropriate.

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?

The description opens with the specific verb 'Read' and a concrete resource, 'an entity's graph neighborhood,' then enumerates what is included: nodes, typed edges, canonical facts, and paths. This clearly distinguishes it from sibling write/relate/search tools such as memory_graph_relate or memory_search.

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

Usage Guidelines4/5

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

The description makes clear this is the read-neighborhood tool: use it when you need an entity's graph neighborhood with nodes, edges, and facts. It does not explicitly exclude alternatives or name a sibling, so it stops short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.