60fps MCP: iOS interaction library

Search shots

60fps_search_shots

Read-onlyIdempotent

Semantic search over the 60fps library of mobile interaction reference clips. Start here when you have the interaction in words. When the user names one shot and wants more like it ("I like the Flighty connection assistant sheet, find similar shots"), use this only to find that shot's slug, then call 60fps_get_related_shots with it: search results include the named shot itself and are ranked against the words, not against the shot. Returns the best-matching shots (title, app, one-line summary, motion behavior, filters, intensity, mood, and video/poster/page URLs). Use natural language, e.g. 'springy bottom sheet for checkout' or 'calm premium onboarding'. Optionally narrow by filter slugs, app (slug or name), or platform. Filters are exact and results must have ALL of them, so an unknown slug returns nothing: leave filters empty unless you took the slugs from a result's filters. An app that is not in the library is ignored and flagged in notice. Read-only; results capped at 20. total is how many shots were searched, not a match count: results are the closest by meaning, so always judge them on their own merits rather than assuming a high count means a good match. DO NOT CALL for: Android, Material Design, Flutter or web, since this library is iOS only and will still return iOS shots; generating or critiquing a design (icons, colour palettes, logos, brand), since this searches recordings of shipped apps and cannot design anything; or performance, profiling and frame-rate debugging, which share the name 60fps and nothing else. For any of those, answer directly and do not call this tool.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`app`	No	Restrict to one app (slug or display name)
`limit`	No	Max results to return (default 10, max 20)
`query`	Yes	Natural-language description of the interaction to find
`filters`	No	Exact filter slugs that results must ALL have. Use only slugs seen in a result's `filters`; an unknown slug returns no results
`platform`	No	Restrict to a platform (60fps is iOS-only; watch is derived)
`response_format`	No	Output text format: 'markdown' (human-readable, default) or 'json' (raw). structuredContent (machine-readable JSON) is always returned regardless.	markdown

Output Schema

TableJSON Schema

Name	Required	Description
`count`	Yes	Number of results in this response
`query`	Yes
`total`	Yes	How many shots were searched after filters, not a match count. Results are the closest, ranked by relevance
`results`	Yes
`has_more`	Yes	Whether more candidates exist beyond `limit`
`truncated`	No	True if results were dropped to fit the size limit
`truncation_message`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 13, 2026

changedInput schema / properties / filters / description

Previous value: -"Filter slugs that results must ALL have (from the 89-slug taxonomy)"New value: +"Exact filter slugs that results must ALL have. Use only slugs seen in a result's `filters`; an unknown slug returns no results"

First observedSep 13, 2026

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive, so safety is covered. The description adds valuable semantics beyond that: unknown filter slugs return nothing, unknown apps are ignored and flagged in notice, results are capped at 20, and total is the searched corpus size not a match count. This substantially enriches what the structured fields alone convey.

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?

Front-loaded with the core purpose and workflow, then examples, parameter guidance, output summary, and a DO NOT CALL section. It is fairly long but every block earns its place; slightly more telescoping could tighten it further.

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?

Covers purpose, sibling routing, parameter gotchas, output fields, result caps, the total-vs-match-count warning, and edge cases for out-of-scope calls. With an output schema present, the description need not explain return values in depth and is complete for correct invocation.

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 schema already documents all six parameters, setting a baseline of 3. The description goes beyond by explaining filter AND-semantics, the slug-provenance rule, and the app-ignored-and-flagged behavior, which are not fully captured in the schema and materially affect correct invocation.

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 the specific verb and resource ('semantic search over the 60fps library of mobile interaction reference clips') and precisely defines its scope. It explicitly distinguishes itself from sibling 60fps_get_related_shots by describing the named-shot workflow, and even pre-empts a naming collision with performance tooling.

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?

Gives explicit when-to-use ('start here when you have the interaction in words'), a conditional routing rule to a sibling tool for named-shot similarity, and a thorough DO NOT CALL list with reasons for Android/Material/Flutter/web, design generation, and performance debugging.

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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