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memory_recall

Answer relational questions by following multi-hop links across a knowledge graph: trace how entities connect (e.g., what X runs on, how A reaches C) when a single search cannot chain the path.

Instructions

Multi-hop retrieval over the knowledge graph, for RELATIONAL questions whose answer is reached by following links — "what does X ultimately run on?", "how does A reach C?" — which single-shot memory_search can't chain. Read-only. low_confidence: true means no seed entity matched — fall back to memory_search.

Returns: {seeds, entities, edges, paths, texts, iterations}. entities/edges/texts are capped (currently 10/15/6) with a per-hop reservation, so a hub seed's own 1-hop ring can't crowd out the deeper hops the walk exists to reach; edges prefers links between surviving entities; each entity's facts is capped (currently 5). A fact carrying re_verify stands on a memory that has since been corrected — the value still stands, but check it before acting. A seed entity's constraint facts come first, marked pinned. Details: docs/guide/retrieval.md.

Input Schema

TableJSON Schema

Name	Required	Description
`hops`	No	Max graph hops. Clamped to 1..5.
`query`	Yes	Natural-language relational question; its top hits seed the graph walk.
`top_k`	No	Bounds only the SEED search (the initial hits that name the walk's start entities), not the result, which is capped separately; up to 3 ``texts`` slots go to seed hits.
`verbose`	No	Full fact/edge provenance and untruncated texts. Default facts are ``{attribute, value}``, edges ``{src, relation, dst}``, texts truncated to a preview.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedv0.15.0Sep 3, 2026

addedInput schema / properties / hops / description

Added value: +"Max graph hops. Clamped to 1..5."

addedInput schema / properties / query / description

Added value: +"Natural-language relational question; its top hits seed the graph walk."

addedInput schema / properties / top_k / description

Added value: +"Bounds only the SEED search (the initial hits that name the walk's start entities), not the result, which is capped separately; up to 3 ``texts`` slots go to seed hits."

addedInput schema / properties / verbose / description

Added value: +"Full fact/edge provenance and untruncated texts. Default facts are ``{attribute, value}``, edges ``{src, relation, dst}``, texts truncated to a preview."

First observedv0.11.0Jul 31, 2026

TDQS

A4.6/5.0

Behavior5/5

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

With no annotations, the description carries full responsibility. It discloses read-only status, the low_confidence failure signal, result caps with per-hop reservation rationale, edge preference behavior, the re_verify flag meaning, and pinned constraint facts. This goes far beyond what the schema conveys.

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 front-loaded with purpose and routing, then layers behavioral detail. It is dense but each sentence adds meaningful information; the only slight demerit is that the cap details could arguably live in the output schema or docs, though their inclusion does help an agent interpret results safely.

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?

For a complex retrieval tool with no annotations and a nontrivial output shape, the description covers when to use, when not to, failure semantics, result structure, caps, and data-quality caveats. It also points to detailed docs for deeper reading. Nothing essential is missing.

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 baseline is 3. The tool description adds context about result caps that indirectly clarify top_k's role, but the schema already explains that top_k bounds only the seed search. The description doesn't need to compensate further.

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 a precise verb+resource pairing: multi-hop retrieval over the knowledge graph for relational questions. It gives concrete example questions and explicitly contrasts itself with memory_search, making the tool's identity unmistakable even among 30+ siblings.

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

Usage Guidelines5/5

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

It states exactly when to use this tool (relational questions requiring link-following, which single-shot memory_search can't chain) and provides an explicit fallback rule: 'low_confidence: true' means no seed entity matched — fall back to memory_search. This is model guidance for tool selection.

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