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memo_graph

Read-onlyIdempotent

Query an entity knowledge graph to find shortest paths, explain connections via weighted edges, explore neighbors, or discover communities. Supports both memory-only and code-structure navigation.

Instructions

Navigate the entity knowledge graph (read-only).

One consolidated explorer over memo's corpus graph. Pick a verb:

  • "path": shortest entity path from a to b (fewest hops).

  • "why": weighted shortest path a->b as evidence — the same route with each hop's edge weight (how many memories bridge it), so a connection is explained, not just asserted.

  • "neighbors": direct neighbours of entity (or a).

  • "explore": a rich "what's around X" view of entity (or a) — degree, neighbours, and the memories that mention it.

  • "communities": clusters of related entities (limit caps count).

By default this navigates the MEMORY graph only (entities linked through shared memories). Set include_code=True to also fold in the codegraph code-structure layer (call/extends/etc. edges between code symbols).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
aNoFirst entity (path/why source; fallback for entity).
bNoSecond entity (path/why target).
verbYesOne of path | neighbors | explore | communities | why.
limitNoResult cap (neighbours, mentioning memories, communities).
entityNoEntity name for neighbors/explore.
include_codeNoFold in the codegraph code-structure layer (default off → memory-only, so results are durable-memory navigation).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

The description aligns with annotations (readOnlyHint, destructiveHint) and adds behavioral details beyond them: different verb behaviors, default scope (memory-only), and the effect of include_code. It does not contradict annotations, and the additional context is valuable for safe invocation.

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?

The description is well-structured with a clear introduction and bullet-like list of verbs. It is concise yet informative, though slightly verbose in the verb descriptions. Every sentence adds value, and the format aids quick scanning.

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

Completeness5/5

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

Given the six parameters, existing output schema (not shown but indicated), and the complexity of graph navigation, the description covers all necessary aspects: verbs, their arguments, default behavior, and optional codegraph integration. An AI agent has sufficient information to use the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Parameters are fully described in the schema (100% coverage), but the description adds context: it explains the purpose of each verb (e.g., 'why' reveals weighted paths), clarifies default behavior (memory-only), and describes the impact of include_code. This enhances understanding beyond the schema alone.

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 clearly states 'Navigate the entity knowledge graph (read-only)' and enumerates five distinct verbs with specific purposes. This differentiates it from sibling tools like memo_search or memo_provenance, as it focuses on graph traversal rather than keyword search or single entity retrieval.

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 does not explicitly state when to use this tool versus alternatives or when not to use it. While the verb list implies use cases (e.g., exploring connections), there are no exclusions or comparisons to other tools, leaving some ambiguity about when to choose memo_graph over other memo tools.

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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