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search_memory

Search all stored transcriptions using keyword or semantic modes to find matching segments, with options for context, deduplication, and export.

Instructions

Search across ALL stored transcriptions. No audio_path needed, queries everything in memory. Default mode is 'keyword' (literal match). Use 'semantic' mode for meaning-based search.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoSearch mode. 'keyword' (default) finds segments containing the exact word/phrase. 'semantic' finds segments similar in meaning.
queryYesSearch query. For keyword mode: a word or phrase to find literally. For semantic mode: a natural language description (e.g. 'discussion about funding challenges').
top_kNoNumber of results to return. Default: 10
outputNoOptional file path to save results. Use .csv for plain data or .xlsx for styled spreadsheets with bold headers and formatting.
context_wordsNoWords of context per result. Default: 25. Use 150 for full evidence blocks when Claude needs to answer a question. Semantic mode only.
dedup_secondsNoMerge matches within this many seconds of each other. Default: 0 (off). Semantic mode only.
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the search scope and modes, which is helpful, but it does not explain return format, result ordering, pagination, or any permissions/rate limits. For a search tool, this is acceptable but not rich; a description of the output structure would have been more transparent.

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 only three sentences, each earning its place: the first states scope, the second clarifies no audio_path, and the third explains modes. It is front-loaded and contains no redundancy or filler.

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

Completeness3/5

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

The tool has 6 parameters and no output schema, so the description should compensate by explaining what the agent can expect in return. While the parameter descriptions cover input semantics, the description does not provide any return value details, result format, or behavioral constraints (e.g., how results are scored or sorted). This is a notable gap for an agent.

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 the baseline is 3. The description adds value by explaining the default mode ('keyword' = literal match) and how to use semantic mode, which is not fully captured in the schema. It also clarifies that no audio_path is needed, reinforcing the query scope. This extra context raises the score above 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?

The description clearly states the tool searches across ALL stored transcriptions, with a specific verb ('Search'), a defined resource ('ALL stored transcriptions'), and scope that distinguishes it from siblings like search_audio ('No audio_path needed'). It also introduces the two modes, leaving no ambiguity about what the tool does.

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 tells when to use this tool ('No audio_path needed, queries everything in memory') and provides guidance on mode selection ('Default mode is keyword... Use semantic mode for meaning-based search'). It doesn't mention alternatives like deep_search or batch_search, so it lacks full exclusions, but the guidance is clear enough for most use cases.

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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