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Search memory

search_memory
Read-onlyIdempotent

Retrieves stored facts and session history using hybrid semantic and keyword search, so you can recall user preferences, past decisions, and project context before answering.

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

NameRequiredDescriptionDefault
as_ofNoISO 8601 timestamp for point-in-time queries (returns facts valid at that moment)
queryYesSearch query
max_resultsNo
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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.5
  2. Removedv0.1.1
  3. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world behavior, so the bar is lower. The description adds useful behavioral context about response-size capping and the full_content tradeoff, plus the hybrid retrieval mode, though the capping detail largely restates the full_content schema description.

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?

Purpose and retrieval method are front-loaded, followed by usage guidance and exclusions. The long list of trigger phrases is effective but somewhat verbose, though each clause serves the routing purpose.

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?

For a five-parameter read tool with no output schema and covered annotations, the description gives enough routing and behavioral context to call it correctly. Minor gaps remain around as_of semantics and include_card, but those are fully documented 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 80%, so the schema already documents as_of, max_results, full_content, and include_card. The description only reinforces full_content usage ('pass full_content=true only when the full matching row is required') and does not explain as_of or max_results, so it adds marginal value beyond the schema baseline.

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?

States a specific verb and resource ('Searches the user's stored facts and session history') plus the retrieval method ('hybrid semantic + keyword retrieval'). It also differentiates from the sibling load_memory by instructing not to trigger when load_memory was just called.

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 trigger conditions (a long list of user phrasings and contexts), rules for implicit triggers ('Trigger even when the user doesn't explicitly say search'), and explicit non-triggers (one-shot math, translations, definitions, no plausible stored context, or immediately after load_memory). This is close to exhaustive routing guidance.

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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