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search_memory

Search across every screen recording and imported video in one query to pinpoint the exact moment that matches a topic, phrase, or shown item, with timestamps and direct links.

Instructions

Search EVERYTHING in the user's Clipy memory at once — every screen recording they made AND every video they imported or watched (YouTube, local files) — and get back the exact moments that match, with timestamps. This is the tool to reach for first when the user refers to something they 'showed you', 'recorded', 'watched', or 'went over'; the per-library tools (search_recordings, list_context_documents) only see half the picture. Matching is semantic as well as literal: 'login flow' finds a moment where someone said 'the authentication screen'. Each result carries kind ('recording' or 'context'), the title, startMs, a plain-text snippet of what was said, and a URL that opens at that moment. Read next: get_transcript or get_recording for a 'recording' hit, read_context_document for a 'context' hit — pass the result's publicId. Read semantic.status before trusting an empty result: 'ok' means the search ran fully (so nothing found really does mean nothing recorded), while 'unavailable' or 'failed' means the semantic index did not run and these are KEYWORD-ONLY results — say so rather than concluding the user has nothing on the topic, and retry with more literal phrasings. Each hit's resolution says how precise its timestamp is: 'lexical' and 'refined' are exact moments, 'window' means startMs..endMs is a ~50-second SPAN to look in rather than a precise point, and 'document' means the match is about the whole recording and has no timestamp — do not quote a window or document hit as an exact time.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindsNoRestrict the search: 'recording' = the user's own screen recordings, 'context' = imported/watched videos. Omit to search both, which is usually right.
limitNoMax results (default 20).
queryYesWhat to look for, in natural language — a topic, phrase, or thing that was said or shown. 3-512 characters.
Behavior5/5

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

With no annotations, the description fully discloses important behaviors: semantic vs keyword-only modes, how to interpret semantic.status, and the meaning of resolution fields (lexical/refined/window/document). These details are critical for correct interpretation of results.

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?

Although long, every sentence adds essential information, from purpose to caveats. It is front-loaded with the primary function, then progressively details usage, result fields, and failure modes—dense but well-organized.

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

Completeness5/5

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

For a tool with no output schema and no annotations, this description covers all needed context: result structure, next-step tools, status interpretation, and resolution semantics. It is genuinely complete for an agent to invoke and interpret correctly.

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 baseline is 3. The description adds value by explaining that omitting kinds is 'usually right' and clarifying query semantics ('natural language — a topic, phrase, or thing'), going slightly beyond the schema.

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 memory types (recordings and context videos) and explicitly contrasts it with per-library siblings like search_recordings and list_context_documents, making its unique role unmistakable.

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?

Offers explicit when-to-use guidance ('reach for first when the user refers to something they showed you...') and names alternatives with their limitation ('only see half the picture'). Also gives read-next steps for result types.

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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