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find_similar

Find AINSOF cues that sound like an AINSOF cue — the whole cue, or a passage of it marked with from and to (seconds, 5–60 s long), e.g. the hook or the bar before the drop. Each result names the matching passage inside it. Takes a track_id or the cue's title. For a song by another artist (a YouTube, Spotify, Apple Music or Deezer link) use search_by_reference instead.

Input Schema

NameRequiredDescriptionDefault
toNoEnd of the marked passage, in seconds (5–60 s after from).
fromNoStart of the marked passage, in seconds. Omit for the whole cue.
top_kNo
track_idYese.g. 'AIN-CAT 030_001' or 'Along The River'

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / top_k / default
      Previous value: -10New value: +8
  2. Added

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already declare the safety profile (readOnlyHint=false, destructiveHint=false, openWorldHint=false), and the description does not contradict them. It adds one useful piece of return context — "Each result names the matching passage inside it" — which matters since there is no output schema. However it says nothing about result count, top_k behavior, or why a lookup is flagged non-read-only, leaving the behavioral picture thin.

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?

Three sentences, front-loaded with the core purpose and ending with the routing exclusion where an agent needs it. The "cues that sound like an AINSOF cue" construction is slightly circular and the 5–60 s range duplicates the schema, but there is no filler.

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?

For a read-style similarity search with no output schema, the description covers input framing (whole cue vs passage), the identifier source (track_id or title), a concrete example, and the shape of results. The only gap is result-volume behavior (top_k default 8, max 25), which is a minor omission.

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 coverage is 75% and the schema already explains from/to seconds, the whole-cue default when from is omitted, and that track_id accepts a title. The description largely restates the 5–60 s passage constraint and the track_id-or-title option rather than adding new semantics, and top_k is never mentioned in either place.

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 a concrete verb and resource ("Find AINSOF cues that sound like an AINSOF cue") and immediately scopes it to whole-cue or sub-passage queries. It explicitly distinguishes itself from search_by_reference, so an agent can route without opening either schema.

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 (whole cue, or a passage marked with from/to, e.g. the hook or the bar before the drop) and a when-not-to-use with the named alternative ("For a song by another artist ... use search_by_reference instead"). Both the positive and negative routing conditions are stated.

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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