Kindle Music

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

With no annotations, the description carries the full behavioral burden and does well: it discloses that expired or revoked lists return an empty sections array (pointing to expired/revoked fields) and that track_url is audition/temporary only (see audioNotice). Auth requirements and rate limits are not covered, keeping it from a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Content is well front-loaded and labelled (何时用 / 返回), but the full bilingual duplication states every fact twice, so each sentence does not fully earn its tokens. The structure itself is clean; the redundancy is what holds it back.

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

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

There is no output schema, and the description compensates by explaining the return shape (full list, empty sections on expired/revoked) plus the track_url caveat. Combined with a single required parameter, an agent has enough to call it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the single code parameter is documented in the schema as the code returned by km_create_result_set. The description only repeats 'by its code' without adding format, validation, or error semantics, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: reads back a published pick list by its code, and enumerates what the payload contains (sections, tracks, per-track status). The resource is clearly distinct from siblings like km_search or km_similar_by_track, though no sibling is named explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The '何时用 / When' line gives concrete triggers: continuing to add/remove/modify on an existing list, or confirming whether listed tracks are still in the catalog. Clear context but no explicit exclusions or named alternatives for retrieving lists another way.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full burden and does well: it discloses the failure mode (resolved=null when unrecognized or absent from library), the kind/source taxonomy, and gaps in the toolchain (playlists have no MCP reader; video-site links usually cannot yield a track name, so use km_similar_by_audio). It does not mention latency, network calls, or explicit read-only status, so it stops short of a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Bilingual and long, but structured with headers (When/Returns) and a per-kind bullet list, with purpose and the km_search caveat front-loaded. The kind-by-kind routing is dense but each branch earns its place; only minor redundancy comes from the paired Chinese/English text.

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

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

No output schema exists, so the description must explain returns and it does: it documents the { q, resolved } envelope plus the fields returned per kind/source and the exact next call for each. Nothing an agent needs to act on the result is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds meaning beyond the schema field by enumerating accepted input forms (album/label/pick-list/playlist/search-page URLs, external links, hyphenated codes) and stressing the 'exactly as the user gave it' constraint implicitly.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource pair (resolve a filename/link/album code into an actionable object) and explicitly differentiates from the sibling km_search ('不要把它们当关键词丢进 km_search'). An agent can identify the tool's job without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use triggers (pasted filename, UPM/kindlemusic link, hyphenated code) and the negative case (do not treat as keyword in km_search, which yields zero results). It also routes to the correct follow-up tool per kind (km_track_versions, km_get_result_set, km_similar_by_track), so alternatives are named and conditioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description carries the full burden and does so thoroughly: it discloses the hard English-only constraint for keywords/composer, how negation is mapped and relaxed, the half-time BPM trap that silently drops valid tracks, LLM daily quota degradation (llmQuotaExceeded), and translation_failed semantics. It also flags that returned track_url is audition-only.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with a one-line bilingual summary before diving into searchField, negation, fields, and BPM caveats, and sectioned with headers. It is dense and long, with bilingual restatement adding some redundancy, but for a 29-parameter tool most content earns its place.

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

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

Despite no annotations and no output schema, the description covers the return shape ({ total, appliedSearchField, searchId, fields, tracks[], search_url }), conditional keys (expanded, derivedNegations), and error flags. Combined with the schema, an agent has everything needed to invoke and interpret results correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is high (90%), but the description still adds substantial meaning: it explains that include facet values within a dimension are AND (except includeLabels/includeAlbums which are OR) while exclude* excludes on any match, that bpmMin/bpmMax and durationMin/durationMax must be paired, and that partition omission merges the two site partitions. It also documents pageSize clamping tied to the fields choice.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('检索曲目或专辑' / searches the Kindle Music production-music catalog) plus the dimensions it filters on (keyword, facet, BPM, duration). It also names sibling tools by role (km_resolve for filenames/links, km_vocabulary for facet values), so an agent can distinguish it without opening schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit when-to-use guidance across all seven searchField modes (keyword vs ai vs semantic vs track vs album vs lyrics vs composer), including the fallback chain (semantic degrades to ai). It states prerequisites ('include*/exclude* 的取值必须先用 km_vocabulary 取') and the routing rule '先用 km_resolve 定位' for downloaded filenames/links/IDs.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full behavioral burden and mostly delivers: it discloses the retrieval mechanism (vector, not keyword), that the anchor id must be a main version, that partition filtering is unsupported, and that track_url is audition/temporary-analysis only. It omits auth/permission requirements and any rate or cost characteristics, so not a full 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with purpose, then sectioned into When / Args / Returns, with every sentence carrying information (id provenance, limit bounds, partition caveat, return shape, audition caveat). The bilingual duplication roughly doubles the surface length for no added meaning, which is the main cost against a 5.

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

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, the description supplies the return shape ({ searchId, total, tracks[] } with km_search-consistent fields), the audition-only caveat on track_url, and the id-provenance rule, so an agent can call it correctly. The unexplained libraryType parameter is the one remaining completeness gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

At 67% schema coverage the description must add value, and it does for two of three params: id must be a km_search id (explicitly 'not track_identity') and must be a main version, and limit's default 12 / cap 50 is restated with the 'clamped' behavior. The third parameter, libraryType, is left entirely unexplained in both schema and description, which keeps this below 5.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a precise verb+resource ('find audio-similar tracks anchored on an existing catalog track') and explicitly disambiguates the mechanism ('vector search, not keywords'), which cleanly separates it from the sibling km_search. An agent can distinguish it from km_search and km_track_versions without opening a schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides an explicit 'When' trigger ('user asks for more like this one', or you've hit a good track and want candidates), a concrete iterative workflow ('take similars of a ~70% match, pick a closer one, repeat 2-3 rounds'), and negative guidance ('does not honor partition; narrow by BPM/duration/tags locally'). This is genuine routing guidance, not implication.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full behavioral burden and does so well: it discloses that non-existent album/track numbers return an empty versions[] rather than an error, that cutdown rows lack track_bpm and the four dimension tags (inherited from the main track), that libraryType is not returned by this endpoint itself, and that track_url is audition-only. These are non-obvious behaviors an agent could not infer from the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The text is well front-loaded and organized with clear section markers (When / Args / Returns), and the critical constraint about km_search being blind to cutdowns appears early with a warning glyph. The cost is that every statement is duplicated in Chinese and English, roughly doubling length without adding information for a bilingual reader.

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

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

With no output schema, the description fully documents the return shape ({ albumCode, trackNumber, total, versions[] }), the sort order (longest to shortest), the per-version fields to inspect (track_version, track_duration, orginal_time, track_mixout), and explicitly flags which fields are absent from cutdown rows. Nothing an agent needs to interpret the response is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all three parameters are already documented in the schema, including the instruction to use the MAIN track's track_number. The description largely restates this, adding only the sourcing note that both required values come from the km_search track object, which is already implied by the schema. Baseline 3 is correct when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb+resource ('Lists every cutdown version of one track') and enumerates the concrete version set (Full Length / 60 Sec / 30 Sec / 20 Sec / 15 Sec), so the agent knows exactly what comes back. It also explicitly positions itself against the sibling km_search, stating that km_search only indexes main tracks and cutdowns can ONLY be fetched here.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is an explicit 'When' section: for ads, TVCs and trailers with hard duration requirements, query this for every track before delivering candidates, because an existing 60s/30s/15s version saves the client an edit. It names the alternative (km_search) and states the precise condition under which that alternative fails, leaving nothing to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full burden and does substantial work: it warns that values must be replayed verbatim (no translation, case changes, or dropping the ';' in 'Parent;Leaf'), that labelZh is display-only, and that count should be used to avoid over-narrowing. It does not state read-only/side-effect status or any auth requirement, so it falls just short of complete.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose, usage, args, and returns are front-loaded and clearly sectioned, and every sentence carries content. It is, however, fully duplicated across Chinese and English, which doubles the length for any single reader.

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

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

There is no output schema, so the description must explain the return shape, and it does so precisely: per-facet { param, values } with { value, labelZh, count } items, plus a top-level labels[] describing 320 libraries. An agent has everything needed to call and interpret it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning: it clarifies that 'field' matches a facet name substring and that 'query' filters against values and their Chinese labels, with a concrete non-Latin example (query="悲伤") that the schema does not provide.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('列出曲库全部可用的筛选值 / Lists every filterable facet value in the catalog') and enumerates the facet domains covered. It is clearly distinguishable from km_search, which it explicitly references as the consumer of these values.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit precondition: 'Call this BEFORE using any include*/exclude* filter in km_search.' It names the sibling tool and the exact condition that should trigger this call, leaving nothing to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.