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search_memory

Search previous conversation logs to find specific information from past sessions using full-text queries.

Instructions

Full-text search across UnClick Memory conversation logs. Use when you need to recall something specific from a previous session.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query
max_resultsNo
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is a search operation but doesn't describe what 'full-text search' entails (e.g., fuzzy matching, exact phrases), what format results return, whether there are rate limits, authentication requirements, or performance characteristics. For a search tool with zero annotation coverage, this leaves significant behavioral gaps.

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 is perfectly concise with two sentences that each serve a distinct purpose: the first states what the tool does, the second provides usage guidance. There's zero wasted language, and the most important information (the search functionality) is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations, no output schema, and incomplete parameter documentation (50% schema coverage), the description is insufficiently complete. While it states the purpose and basic usage context, it doesn't explain what the search returns, how results are structured, any limitations or constraints, or how it differs from similar sibling tools. For a search tool that presumably returns complex results, more context is needed.

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 50% (only 'query' has a description), and the description doesn't add any parameter-specific information beyond what's in the schema. It mentions 'search query' which aligns with the schema's 'query' parameter description, but provides no additional context about query syntax, supported operators, or the meaning of 'max_results' beyond its numeric constraints. With moderate schema coverage, the baseline 3 is appropriate.

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 performs 'full-text search across UnClick Memory conversation logs' with the specific purpose of 'recall[ing] something specific from a previous session.' It uses a specific verb ('search') and resource ('conversation logs'), but doesn't explicitly differentiate from sibling tools like 'unclick_search' or 'get_startup_context' that might also access memory data.

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 provides clear context for when to use it: 'when you need to recall something specific from a previous session.' This gives practical guidance about the tool's intended use case. However, it doesn't explicitly state when NOT to use it or mention alternatives among the sibling tools (e.g., when to use 'get_startup_context' instead for general context retrieval).

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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