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recall

Search past decisions, patterns, and pitfalls using vector search and multi-factor scoring to surface relevant memories before responding.

Instructions

Search for relevant memories using vector search and six-factor scoring.

Primary read path — call before every non-trivial response to surface past decisions, patterns, and pitfalls. Combines vector similarity (LanceDB ANN), weight decay, recency, scope match, access count, and type boost into a single relevance score.

Read-only: yes. No side effects. May trigger background consolidation (non-blocking) if interval has elapsed.

Args: query: Natural language description of what you're looking for. 5-15 words works best. E.g. "how do we handle API auth?" scope: Scope to search. Auto-detected from project config or CWD. Use 'global' to search only cross-project memories. token_budget: Maximum tokens for returned content across all results (default 2000). Results accumulated until budget exhausted. min_score: Minimum final relevance score 0.0–1.0 (default 0.3). Score ≥ 0.6 = directly applicable, 0.4-0.6 = hint/context, < 0.4 = ignore. limit: Maximum candidates to score from ANN search (default 30). type_filter: Restrict to specific memory types, e.g. ['semantic', 'permanent'] to exclude ephemeral session captures.

Returns: list[dict]: Scored, deduplicated memory objects sorted by final_score descending. Embeddings and binary fields stripped. Empty list if no results above min_score.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language query. 5-15 words works best. E.g. 'how do we handle API auth?'
scopeNoScope to search. Auto-detected if omitted. Use 'global' for cross-project only.
token_budgetNoMaximum total tokens for returned content across all results.
min_scoreNoMinimum relevance threshold 0.0–1.0. >=0.6 directly applicable, 0.4-0.6 hint/context, <0.4 ignore.
limitNoMaximum candidates to score from ANN search.
type_filterNoRestrict to specific memory types, e.g. ['semantic', 'permanent'].

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

The description thoroughly discloses behavior: read-only, no side effects, potential non-blocking background consolidation. It also explains the scoring factors. Given no annotations, the description fully carries the burden of transparency.

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, front-loading the core purpose and usage. Every sentence is informative, though slightly verbose in parameter descriptions which are also in the schema.

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 6 parameters, an output schema described in text, and no annotations, the description is comprehensive. It covers purpose, behavior, parameter details, return format, and potential background effects, enabling an agent to use it 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?

Schema coverage is 100%, so baseline is 3. The description adds value beyond the schema by providing optimal query length (5-15 words), score thresholds for applicability, and details on auto-detection for scope.

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 the tool searches memories using vector search and six-factor scoring, and identifies itself as the primary read path. This distinguishes it from sibling tools like get (direct retrieval) and remember (storage).

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 explicitly recommends calling the tool before every non-trivial response, providing strong usage guidance. However, it does not explicitly state when not to use it or directly compare with alternatives.

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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