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search_memory

Retrieve relevant memories using semantic search with temporal and categorical filters. Access past work, debugging history, and project context to inform coding tasks, writing, and problem-solving.

Instructions

Search indexed memories by semantic similarity and return ranked results with optional temporal filtering. Read-only, but may fire stored reminders as a side effect. Use proactively at the start of tasks, when debugging, writing, or when the user references past work.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query — natural language or keywords
limitNoMax results to return
scopeNoOptional explicit scope
sessionIdNoOptional session identifier to infer session:<id> scope
allScopesNoWhen true, explicitly allow cross-scope search
categoryNoFilter by memory category: profile (identity/background), preferences (habits/style), entities (projects/tools/people), events (past happenings), cases (problem-solution pairs), patterns (reusable workflows)
profileNoRetrieval profile
renderNoResult rendering mode: verbatim (default, original order) or highlight (reorder by contextual relevance to query)verbatim
afterNoFilter memories stored after this date (ISO format YYYY-MM-DD, or relative like '最近30天', 'last 7 days')
beforeNoFilter memories stored before this date (ISO format YYYY-MM-DD, or relative)
graphNoEnable KG graph traversal (PPR) for relationship-aware search. Use when query involves entity relationships (e.g. 'what tools does Alice use', 'Bob的朋友').
includeArchivedNoWhen true, also return archived/superseded/consolidated memories (default: only active)
detail_levelNoResult detail level: brief (ID+score+one-liner), normal (default, current behavior), full (include metadata)normal
Behavior4/5

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

Since no annotations are provided, the description carries the full burden of disclosing behavioral traits. It successfully declares the operation as 'Read-only' (safety profile) and crucially warns that it 'may fire stored reminders as a side effect,' which is essential context for agent planning not found in the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description consists of three tightly constructed sentences: one defining the core function, one disclosing safety/side effects, and one providing usage guidance. No words are wasted and critical information is front-loaded.

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?

Given the high schema coverage (100%) and lack of output schema, the description is appropriately complete. It covers the search mechanism, filtering capabilities, side effects, and usage patterns. It could be improved by hinting at the result structure or contrasting with retrieval siblings, but it suffices for the complexity.

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?

With 100% schema description coverage, the baseline is 3. The description mentions 'temporal filtering' (mapping to after/before parameters) and 'semantic similarity' (contextualizing query), but does not clarify ambiguous schema entries like 'scope' or 'profile' (both lacking descriptive enums in the description text).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool searches 'indexed memories by semantic similarity' and returns 'ranked results with optional temporal filtering.' It uses specific verbs and resources. However, it does not explicitly distinguish this from sibling tools like `memory_drill_down` or `retrieve_skill`.

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?

It provides explicit guidance on when to use the tool: 'proactively at the start of tasks, when debugging, writing, or when the user references past work.' This gives clear context for invocation, though it lacks explicit exclusions or named 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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