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Search memory

search_memory

Search stored facts and session history using hybrid semantic and keyword retrieval. Retrieve relevant memories with text previews, configurable result limits, and time-based filtering.

Instructions

Searches the user's stored facts and session history using hybrid semantic + keyword retrieval. Result content is capped to keep strict MCP clients under response limits; pass full_content=true only when the full matching row is required. Use whenever the user asks about anything that might be stored: 'remember', 'recall', 'do you know', 'what did I say about', 'last time', 'context', 'profile', 'facts about me', 'who am I', 'my preferences', 'what have I told you', or when you need background on a topic before answering. Trigger even when the user doesn't explicitly say 'search' -- if the question involves past decisions, preferences, project details, or named people and tools, check memory first. Do NOT trigger for one-shot math, translations, definitions, or questions with no plausible stored context. Do NOT trigger if load_memory was just called and already returned the relevant context.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query
max_resultsNo
as_ofNoISO 8601 timestamp for point-in-time queries (returns facts valid at that moment)
full_contentNoWhen true, returns full result content instead of 800-character previews. Use sparingly for strict clients.
include_cardNoPhase 1 Wizard opt-in. When true, the response is { results, card } where card is a ConversationalCard summarising the matches for friendly chat surfaces. When false (default), returns the raw results array unchanged for backward compatibility.
Behavior5/5

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

With no annotations provided, the description fully discloses hybrid search method, content cap, full_content behavior, and include_card option. It sets clear expectations without contradictions.

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?

Front-loaded with purpose and key details. Slightly long due to extensive usage guidance, but well-organized. Could trim redundant phrases.

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?

Comprehensive for a search tool with 5 parameters and no output schema. Covers purpose, usage rules, parameter effects, and behavior. Missing some specifics about return format but adequate.

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 high (80%), and the description adds context for full_content (use sparingly), as_of (point-in-time), and include_card (Phase 1 Wizard opt-in). However, query parameter could have more usage examples.

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 it searches stored facts and session history using hybrid retrieval. It distinguishes itself from sibling tools like load_memory by specifying search behavior.

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?

Provides explicit triggering conditions (remember, recall, etc.) and non-triggering cases (one-shot math, translations). Also advises against triggering if load_memory was just called.

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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