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

With no annotations provided, the description carries the full behavioral burden. It explicitly says 'Read-only,' which is a useful non-mutating flag, but it does not disclose pagination/scan behavior, rate limits, or output characteristics beyond the schema's parameter notes.

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?

A single sentence that is front-loaded with the core behavior and ends with the useful read-only flag. No filler or repetition; every word 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?

For a read-only query tool with fully schema-documented parameters, this description is adequate for selection and invocation. It lacks an output schema and does not describe result shape beyond 'show tracks,' but the core behavior and safety profile are sufficiently conveyed.

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%, with all three parameters (scan_cap, max_results, response_format) documented with types, defaults, and enum values. The tool description adds no parameter-specific meaning beyond the schema, so the baseline of 3 is appropriate.

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 ('Show'), resource ('tracks you saved'), and a distinctive temporal filter (calendar day month+day in previous years). The 'on this day' framing makes the intent unmistakable and separates it from generic saved-track listing tools like get_saved_tracks.

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 intended use case is evident from the description, but there is no explicit when-to-use or when-not-to-use guidance. It does not name alternatives or exclusions, leaving the agent to infer when this tool is preferred over related saved-track or search tools.

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 provided, the description carries the full disclosure burden but only reveals the 100-URI cap, which is already encoded in the schema as uris.maxItems. It does not state that duplicates are appended by default, that the operation mutates the playlist irreversibly, or what dry_run and position imply behaviorally, nor does it mention any returned snapshot or receipt.

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?

Two short sentences with no filler, and the hard constraint is front-loaded. It is efficient and readable, though it borders on under-specification given the 5-parameter surface.

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?

The tool has 5 parameters, no annotations, and no output schema, yet sits among closely related siblings (batch_add_to_playlist, add_to_queue, playlist_add_by_search). The description does not cover what the call returns, how it differs from batch_add_to_playlist, or the default duplicate-appending behavior, leaving the agent to infer too much for confident invocation.

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 the baseline of 3 applies. The description's 'Max 100 URIs per call' reinforces the uris.maxItems constraint but adds no meaning beyond what the schema already documents for each parameter.

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 states a specific action ('Add') with a clear resource ('tracks or episodes to a playlist') and a cardinality constraint ('Max 100 URIs per call'). The action and resource are unambiguous, but it does not differentiate from the very similar sibling batch_add_to_playlist, so it stops short of a 5.

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 description provides no guidance on when to use this tool versus nearby alternatives such as batch_add_to_playlist, add_to_queue, or playlist_add_by_search. The 'Max 100 URIs per call' line is an operational constraint, not a selection criterion, and nothing excludes or routes to alternatives.

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 behavioral burden. It does disclose the core mutation ('add to queue') and the end-of-queue positioning, and the dry_run parameter is a helpful safety signal. However, it does not mention prerequisites such as requiring an active playback device, or what happens to the existing queue.

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 main action is front-loaded and concise, and the related-tool pointers are useful. The structure is slightly repetitive ('Also covers' followed by 'See also' naming the same tools), but it remains compact and readable.

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 simple mutation with fully documented parameters, the description is mostly sufficient. However, with no output schema and no annotations, it omits behavioral context like active-device requirements and error-prone preconditions, leaving some ambiguity for the agent.

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 the schema fully documents uri, dry_run, device_id, and response_format. The description adds no parameter-level meaning beyond the schema, which matches the baseline expectation.

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 states a specific action ('Add'), resource ('track or episode'), and precise placement ('to the end of the playback queue'). It also points to distinct siblings for bulk and playlist-wide queuing, making the tool's scope clear.

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 description explicitly routes bulk operations to batch_add_to_queue and playlist queuing to queue_playlist, which is strong usage guidance. It does not state any exclusions or conditions, but the alternatives are named clearly enough for an agent to decide.

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 behavioral burden and does useful work: it discloses the quota and backend cost ('one paginated /artists/{id}/albums walk') and clarifies how anniversaries are selected. It does not detail output sorting or error behavior, but the core side-effect-free, quota-relevant behavior is surfaced.

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 description is compact, front-loaded with the core function, and every sentence carries information: the result, the use case, and the quota. No wasted words.

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 read-style lookup with four well-documented parameters, the description covers the main behavior and cost. There is no output schema, but response_format in the input schema partially compensates by describing the available return styles.

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 the baseline is 3. The description adds a little context by explaining window_days and the milestone-flag concept, but it mostly restates what the schema already specifies and does not enrich response_format.

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 clearly states what the tool reports: upcoming album anniversaries for an artist within a configurable window, with milestone years flagged. It avoids tautology and conveys a distinct capability, but it does not explicitly contrast itself with siblings like check_artist_releases or artist_album_timeline.

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?

It gives a use case ('plan re-listens or anniversary posts'), which implies when an agent would call it. It does not state when to prefer alternatives or when the tool is not appropriate, leaving much of the routing decision 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 zero annotations, the description carries the full behavioral burden. The 'Quota: 🔴 paginated walk + batched /albums lookups' line is a genuinely useful disclosure of API call pattern and cost, which is rare and valuable. But it doesn't state that the operation is read-only, what happens for an artist with no studio albums, or how results are paginated/capped.

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?

Two sentences with the core output contract front-loaded; no wasted words. The 'epics and the EPs' phrase adds mnemonic value rather than fluff, and the quota line packs disproportionate operational information into one compact fragment.

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?

The description covers purpose and cost well for a report-style tool, but with no annotations and no output schema it leaves gaps: the concrete return shape beyond the three summary fields, ranking direction (descending is only implied by 'epics'), edge cases, and max_results semantics. Adequate for basic tool selection, thin for confident invocation.

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 75%, and the schema already documents artist_id, max_albums, and response_format including defaults and enum meaning. The description's quota line implies cost scales with the walk but never ties it to max_albums or explains how max_results caps output; max_results is left undocumented in both schema and description.

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 scope — an artist's studio albums — and three concrete outputs (total length, track count, longest track) with a defined ordering (ranked by runtime). The verb is nominalized ('Runtime profile') rather than an explicit action verb, and it doesn't name its closest siblings like albums_runtime_batch or album_track_stats, so differentiation is implicit in the 'studio albums' scope rather than explicit.

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 'studio albums' scope implies exclusions (no singles, EPs, live albums, remixes), and the quota warning signals this is a heavy operation, hinting the agent should prefer lighter tools when appropriate. However, no sibling is named and no explicit when-to-use / when-not-to-use guidance is given, so routing decisions rely on 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 provided, the description carries the full burden, and it explicitly states 'Read-only', disclosing the safety profile up front. It also discloses the normalization behavior (edition-noise stripping) that drives detection. It does not describe the return format or pagination behavior, but for a lint tool this is a secondary concern given the safety and matching semantics are both clearly disclosed.

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?

Two compact sentences with zero filler: the first states the action and exact detection rule, and the second delivers the safety flag. Every word earns its place, and the most decision-relevant information is front-loaded.

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 read-only analysis tool with three fully-documented optional parameters, the description covers the core invocation needs: what it scans, how it matches, and that it is non-mutating. The one gap is return-value semantics — no output schema exists and the description does not hint at what the lint report contains — but this is partially mitigated by the response_format parameter enumerating output styles.

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 the schema already documents all three parameters thoroughly: scan_cap explains its default and upper bound, max_results explains its default and env fallback, and response_format explains each enum value. The description adds nothing about parameters, so the baseline 3 applies — 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 uses a specific verb ('Lint') targeting a specific resource ('your saved albums') and precisely defines what it detects: the same record across multiple editions, with an explicit matching rule (same name after edition-noise stripping + primary artist, distinct album IDs). This level of specificity distinguishes it from generic duplicate-finding siblings like duplicate_saved_versions even without naming them.

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 purpose statement implies when to use it (when you suspect the same record is saved in multiple editions), but the description never names alternatives or states when NOT to use it. This is a real gap given the sibling list contains duplicate_saved_versions and find_duplicate_saved_tracks, which an agent could easily confuse with this tool. The usage context is clear for the tool itself, but no exclusions or alternative routing is provided.

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 supplied, the description carries the behavioral burden. It explicitly discloses the quota (1 GET /albums/{id} + 1 paginated discography walk), which tells the agent about resource usage and read-only GET semantics. It also reveals that the tool will walk the artist discography, which is important hidden behavior not otherwise visible 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 description is compact and front-loaded: the first sentence establishes the tool's purpose and key output features, and the second sentence states the quota. Every clause adds substantive information without redundancy or filler.

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 convey what the tool returns; it does so by listing the report's contents (track listing, runtime, duration stats, edition detection, label/copyright). The quota and discography-walk behavior add important operational context. It could be slightly more explicit about the output format or edge cases, but it is largely complete for a read-only report tool.

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 the parameters are already well documented. The description adds no additional parameter-level detail beyond the general 'one album' framing, which is already implied by album_id. This matches the baseline for a fully self-documenting schema.

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 is specific: it names the tool as a 'deep focus report on one album' and enumerates distinct deliverables (track-by-track listing, cumulative runtime, duration stats, edition detection, label/copyright capture). This uniquely positions it against sibling tools like get_album or album_track_stats by describing a composite analysis rather than a raw lookup.

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 phrase 'deep focus report on one album' implies when to use it, but there is no explicit guidance about when to prefer it over alternatives such as get_album, get_album_tracks, album_track_stats, or album_duration_report. No when-not-to-use or alternative-routing information is provided.

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 must carry the behavioral burden, and it does add a useful quota warning: '🔴 paginated walk + batched /albums lookups.' This signals cost and operation style. However, it does not disclose edge cases such as artists with more studio albums than the max_albums default, how 'studio album' is determined, or what happens when a track 1 is missing.

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?

Two sentences with no filler. The core behavior is front-loaded, the 'how each record begins' framing adds context efficiently, and the quota note is a compact, useful operational warning that 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?

For a tool with no output schema, no annotations, and a parameter-rich sibling environment, the description conveys the main purpose and cost but leaves gaps. It does not clarify whether max_albums can exceed 30 to truly cover 'every' studio album, nor does it describe what the response formats contain beyond the schema enum values.

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?

The input schema has 100% description coverage: artist_id, max_albums, and response_format each have meaningful descriptions. The tool description does not add parameter-level detail beyond the schema, so it earns the baseline score of 3 rather than higher.

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 states a specific verb and resource: 'List track 1 (the side-A opener) of every studio album by an artist, chronologically.' This clearly communicates the tool's core function and the unique 'how each record begins' angle. However, it does not explicitly distinguish itself from sibling discography tools like get_artist_albums or artist_album_timeline, so it stops short of a 5.

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 use case is implied through the descriptive 'how each record begins' view: an agent can infer this is for when the user wants album openers. But there is no explicit when-to-use, when-not-to-use, or alternative recommendation among the many discography-related siblings, so guidance is only implied rather than stated.

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 provided, the description carries the full burden and does substantial work: it discloses the deterministic nature, the exact selection algorithm, the ordering rule, and the API quota (1 GET /albums/{id}, +1 paged walk above 50 tracks). It does not explicitly state read-only status or error behavior, but 'builds a plan' strongly implies a pure computation with no side effects.

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?

Two dense sentences with zero filler. The first sentence front-loads the core action and output composition; the second adds the quota. Every clause earns its place, and the algorithm is packed efficiently without losing readability.

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 plan-building tool with no output schema and no annotations, the description covers the essential behavioral surface: determinism, selection criteria, ordering, and API cost. Missing are edge-case behavior (e.g., albums with fewer than four tracks) and explicit confirmation of no side effects, but the core information an agent needs to call it correctly is present.

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 the schema fully documents album_id, market, and response_format. The description adds marginal value by noting the operation consumes the 'full album payload', which clarifies how album_id is used internally, but this is lightweight context rather than substantive parameter semantics.

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 ('Builds'), resource ('album'), and a precisely defined output: opener, mid-point, closer, longest track, ordered by original position with cumulative offsets. This clearly distinguishes it from siblings like album_openers_report, album_track_stats, or front_to_back_plan without needing to inspect their 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?

The phrase "sample this album" plan implies the use case (wanting a representative cross-section of an album), and the quota line conveys cost expectations. However, there is no explicit when-to-use guidance, no exclusions, and no named alternatives among the hundreds of siblings, leaving selection partially 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 of behavior disclosure. It reveals local-compute behavior, the exactness limit at 50 tracks, the partial-flagging above that threshold, and the quota cost of one GET /albums?ids= call. This is rich, non-obvious behavioral context that goes well beyond a simple function summary.

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?

Two tight sentences: the purpose is front-loaded, the correctness caveat is contextual, and the quota is stated last. No filler or redundancy; every phrase 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?

For a 3-parameter tool with no output schema, the description is complete enough. It covers the batching limit, the exactness boundary, the partial-flagging behavior, and the quota implication. An agent has what it needs to invoke the tool correctly and interpret the result.

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 the schema already documents all three parameters well. The description reinforces the album_ids batching limit and implies what the computed output means, but it does not add significant new parameter-level semantics beyond the schema.

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 states a specific computation (total and mean track length per album), the resource (albums), and a batching scope (up to 20 in one pass). It clearly distinguishes itself from raw album retrieval tools like get_several_albums by emphasizing local-compute and runtime statistics.

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 description makes the intended use clear: computing runtime stats for multiple albums efficiently in one API call. It communicates batching limits and quota impact, which helps an agent decide when to use this tool over per-album alternatives. However, it does not explicitly name sibling tools or state when not to use it.

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 present, the description carries the behavioral disclosure burden. It usefully discloses the operation's cost: 'Quota: one album fetch + one paginated discography walk.' It also conveys read-only intent through 'track listing' and 'spot,' though it never explicitly says no changes are made to the user's library.

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 description is two focused sentences, front-loading the tool's primary action and outcome, then adding the quota warning. There is no filler, repetition of the tool name, or unnecessary detail.

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 read-only explorer with three parameters and no annotations, the description supplies the essential context: what it returns, its differentiating use case, and its quota cost. The main gap is the unclarified max_results parameter, but a correct invocation is still achievable from the schema and description together.

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?

The description adds no parameter-level meaning beyond the schema. Album_id and response_format are documented in the schema, but max_results is not described anywhere, and the description's 'full track listing' wording does not clarify how max_results interacts with the album tracks versus the paginated discography walk.

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 clearly states a specific resource and function: 'Full track listing for one album with per-track cross-album duplicate counts.' The added phrase 'spot which songs are unique to this release vs recycled across the discography' differentiates it from simpler album-listing tools like get_album_tracks and from album stats tools like album_track_stats.

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 description gives a concrete use case: identifying which tracks on an album are unique versus reused elsewhere in the discography. It does not explicitly name sibling alternatives or state when not to use this tool, so it stops short of a 5, but the scenario is clear enough to guide selection.

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 behavioral disclosure burden. It discloses that this is a '[local-compute]' operation built from a paged /albums/{id}/tracks walk and states the quota, which helps an agent assess cost. However, it does not explicitly state that the operation is read-only or side-effect-free, and the 'local-compute' label is somewhat ambiguous about whether any API request is actually performed.

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 description is two sentences with no wasted words. It front-loads the computed statistics and immediately gives quota and pagination context, making it easy for an agent to parse.

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?

The description covers the main purpose, the specific statistics returned, and the quota/cost. Since there is no output schema, naming min/max/mean/median and longest-track callout partially compensates. It does not mention how market affects track availability or what happens on invalid album IDs, but these are minor for a stats tool.

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%, with each parameter (album_id, market, response_format) already documented in the schema. The description adds only the album-scope context and the internal paginated walk, which does not materially enhance parameter understanding beyond the schema.

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 clearly states the tool computes 'Track-length statistics for one album' with min/max/mean/median plus a longest-track callout, giving a specific verb, resource, and output. It does not explicitly name sibling alternatives, but the scope ('one album') and stats focus are unambiguous enough to separate it from related tools.

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?

Usage is implied by the description: an agent should use this when it needs track-length statistics for a single album. It also signals cost with 'Quota: 🟡 one paginated walk,' but there is no explicit guidance on when to prefer this over alternatives like album_duration_report or album_track_explorer.

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 of behavioral disclosure. It identifies the operation as a PUT mutation, but it does not state whether volumes are changed on multiple devices, whether presets apply only to active devices, whether the operation is reversible, or what happens when no presets exist.

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 description is a single front-loaded sentence with no filler. It names the action, the scope, and the endpoint concisely while remaining immediately scannable.

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 mutating bulk operation with no annotations and no output schema, the description is thin. It omits the concrete effect on devices, the return/confirmation shape, and failure behavior, so an agent would have to infer or safely probe these details.

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?

Input schema coverage is 100%, with clear descriptions for dry_run and response_format, so the baseline is 3. The description itself adds no parameter-level meaning beyond what the schema already provides.

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 states a specific action ('Apply all stored per-device volume presets') and identifies the exact resource/endpoint (PUT /me/player/volume). The 'all stored per-device' scope distinguishes it from siblings like list_device_presets, set_device_volume_preset, and apply_volume_plan, so an agent can select 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?

No guidance is given about when to choose this tool over related siblings such as set_device_volume_preset, list_device_presets, apply_volume_plan, or plan_volume_level_across_devices. The action is implied by the name and description, but there are no explicit exclusions, prerequisites, or alternative selection criteria.

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 by disclosing the operation order and that missing targets are skipped. It also exposes the dry_run behavior in the schema, which is a significant transparency feature. It could go further by mentioning failure modes or prerequisites like an existing scene, but the core behavior is well disclosed.

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 description is a single, front-loaded sentence that packs the action, the order of operations, and the skip behavior without any wasted words. Every clause adds useful information.

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 multi-step, state-changing tool with no output schema and no annotations, the description covers the essential behavior and order. It explains the side-effect sequence and skip behavior, though it does not mention error conditions or prerequisites such as the scene needing to exist.

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 parameters are already well documented in the schema. The description adds operation ordering context but does not need to elaborate on individual parameters; the baseline of 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?

The description states a precise action ('Apply a saved scene') and enumerates the exact operations performed: resolve device hint, transfer playback, set volume/shuffle/repeat, and start the saved context. This clearly distinguishes it from sibling tools like save_scene, list_scenes, and delete_scene.

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 description makes it clear this tool is for applying an already-saved scene, which is a distinct context from saving, listing, or deleting scenes. It does not explicitly name alternatives or state when not to use it, but the purpose is clear enough for an agent to select it appropriately.

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?

The description discloses that this tool executes changes on the live playlist and that dry_run=true prevents actual writes, which is important safety context given no annotations are present. However, it does not describe what happens on commit in detail, such as whether tracks are added/removed or whether the operation is reversible. The description carries the behavioral burden but only partially fulfills it.

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 core purpose and dry-run behavior are front-loaded in a clear first sentence. The second sentence's Snapshot guide is a long comma-separated list of related tools that adds useful navigation context but feels somewhat cluttered and redundant with sibling tool names already discoverable. The description is acceptable but not tightly minimal.

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 annotations and no output schema, the description must supply operational context, and it does highlight the critical dry_run safety default. Still, it leaves some ambiguity about what a 'merge plan' entails, what the returned plan looks like, and how commit behavior modifies the live playlist. Together with the strong schema, it is workable but not fully complete.

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 the input schema already explains all parameters including dry_run, mode, playlist, and response_format. The description adds only minor context by restating the dry_run default. This meets the baseline for schema-heavy parameter documentation without adding substantial new meaning.

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 clearly states the tool applies a merge plan to the live playlist by replaying changes between two snapshots, using a specific verb and resource. It also emphasizes the dry_run default, which helps distinguish execution from plan-only behavior. However, it does not explicitly differentiate itself from closely related siblings like merge_snapshot_changes_plan or restore_playlist_plan.

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 description conveys that dry_run=true returns a plan by default, which gives useful context for previewing. It also includes a Snapshot guide listing related snapshot tools, but it does not explicitly state when to prefer this tool over restore_playlist_from_snapshot or merge_snapshot_changes_plan. Usage guidance is implied rather than directly stated.

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 provided, the description carries the full disclosure burden and does meaningful work: it reveals the tool is mutating (via 'apply'), that it is non-mutating by default ('Preview by default'), and that it acts per-device over the API. This is substantial behavioral context, though it does not cover failure handling, requirements for active/volume-capable devices, or what the apply mode actually returns.

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?

Two sentences with zero filler: the first front-loads the action, resource, scope, and API mechanism; the second states the critical default behavior. Every clause 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?

The description is adequate for a moderate-complexity tool with fully documented parameters, and it covers the key preview-by-default behavior. But with no output schema and no annotations, it leaves gaps: there is no mention of what apply mode returns, no relationship to the sibling plan_volume_level_across_devices, and no guidance on prerequisites like active devices.

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 four parameters are already well documented in the schema. The description adds minimal parameter-level value: 'all (or selected)' restates the device_ids default and 'pass dry_run=false to apply' restates the dry_run semantics, so it stays at the baseline 3.

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 states a specific verb ('Set'), a precise resource ('one target volume level across all (or selected) Spotify devices'), and the mechanism ('per-device PUT /me/player/volume'). This inherently distinguishes it from single-device siblings like set_volume and planning-only tools like plan_volume_level_across_devices.

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 phrasing 'across all (or selected) Spotify devices' implies the tool is for multi-device volume setting, and 'Preview by default' hints at a plan-then-apply workflow. However, no alternative tools are named and there are no explicit when-to-use vs when-not-to-use conditions, leaving the agent to infer the decision boundary between this tool, set_volume, and plan_volume_level_across_devices.

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 provided, the description carries full disclosure burden and does well: it flags the destructive nature ('Batch DELETE /me/episodes'), the confirmation threshold ('elicitation >50'), and the safe-preview path ('dry_run supported'). The term 'elicitation' is cryptic shorthand for confirmation prompting, and auth/rate-limit details are absent, but the core safety-relevant behaviors are disclosed.

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 description is a single dense sentence that front-loads the action and packs selection criteria, endpoint, threshold, and dry-run support into minimal words with no filler. The 'elicitation >50' phrasing is compact but jargon-y, slightly harming readability; otherwise every element 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?

For a destructive mutation tool with no annotations and no output schema, the description covers the essentials: target definition, endpoint, confirmation threshold, and dry-run escape hatch. It does not describe return values or response structure, though the response_format parameter (concise/detailed/json) partially compensates.

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 the schema already documents all four parameters; per the baseline, the description needn't repeat them. It does add useful context linking 'resume_point.fully_played' to the selection logic and 'elicitation >50' to the confirm parameter, but it adds no format or syntax detail beyond the schema.

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 states a specific verb and resource: 'Remove fully-played episodes from your episode library in bulk,' which clearly distinguishes it from sibling tools like remove_saved_episodes (targeted removal) and mark_episode_played_plan (planning, not removal). It also pins down the selection criterion ('checks resume_point.fully_played'), leaving no ambiguity about what counts as a target.

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?

Usage context is implied rather than explicit: the bulk-removal semantics and 'dry_run supported' hint at a cleanup workflow, but the description never names an alternative or states when NOT to use this tool (e.g., when the user wants to remove a specific episode, remove_saved_episodes would be the choice). No explicit exclusions or routing guidance is given.

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 provided, the description carries the full behavioral disclosure burden. It clearly states the tool is read-only and requires no authentication, and it reveals the underlying methodology: lifetime intensity plus a recency half-life fitted to stream ages. This goes beyond a simple action statement and gives the agent meaningful insight into how the metric is computed.

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 description is compact and front-loaded: a hook question, a precise explanation of the algorithm, a safety declaration, and a legacy-alias note all in two sentences. Every sentence contributes useful information without unnecessary prose.

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 simple read-only tool with three parameters and no output schema, the description covers the key operational facts: what the metric means, that it is read-only/no-auth, and that a canonical alternative exists. The response_format parameter in the schema clarifies output modes, so the lack of an explicit return shape in the description is a minor gap rather than a blocking one.

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 the input schema already documents all three parameters, including the substring-match behavior for artist and the public-profile note for statsfm_user. The description adds no additional parameter-specific meaning beyond what the schema provides. Baseline 3 is appropriate.

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 clearly identifies the tool's function: computing artist affinity from lifetime stream intensity plus a recency half-life. Though it lacks a conventional imperative verb like 'calculate,' the resource and the calculation approach are explicit. Mentioning the canonical alias also helps distinguish it from the related statsfm_artist_affinity sibling.

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 description explicitly notes 'Read-only, no auth,' which tells the agent this is a safe, publicly accessible operation. It also provides clear routing guidance by labeling the tool as a legacy alias and directing the agent to prefer the canonical statsfm_artist_affinity. It does not compare against broader analytics siblings, but the alias guidance is a strong usage signal.

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 provided, the description carries the full behavioral burden. It discloses quota behavior ('2 paginated walks'), the endpoints touched ('artist albums + saved albums'), matching semantics ('edition-insensitively'), and the presence of a 'missing list'. It does not explicitly state that the operation is read-only, but the 'Checks' framing makes that reasonably clear.

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?

Two compact sentences with no filler. The core purpose and algorithm are front-loaded, followed by an operational quota warning. Every phrase earns its place, and the terminology is dense but precise.

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 read-only analysis tool with one required parameter and clear schema coverage, the description is nearly complete: it explains what is compared, how matching works, the quota cost, and the kind of output. It does not describe the exact return structure, but no output schema exists and the description's 'missing list' is a reasonable summary for call correctness.

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 the schema already documents all three parameters. The description adds contextual detail relevant to saved_cap through '2 paginated walks' and to the overall matching semantics, but it does not enrich individual parameter meanings beyond the schema. Baseline 3 is appropriate when the schema carries the parameter documentation burden.

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 names a specific verb ('Checks'), a concrete resource ('saved-album library for one artist'), and the exact matching strategy (canonical studio-album titles matched edition-insensitively against saved albums). It also states the output ('missing list'), so an agent can tell what this tool computes without looking at 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?

The description implies usage context: you call this when you need per-artist completeness of saved albums. However, it does not name any sibling alternatives such as artist_complete_check, artist_completeness_score, or artist_collection_gaps, nor does it state when NOT to use it. The one-artist scope is clear, but routing relative to near-neighbor tools is left 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?

No annotations are provided, so the description carries the full burden. It does disclose that this is '[local-compute]' and entails 'one paginated /artists/{id}/albums walk,' which informs the agent about the underlying operation and quota usage. However, it does not specify the output shape beyond a 'table,' nor clarify side effects (though read-only is implied). This is partial but not comprehensive transparency.

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 description is a single, dense sentence that front-loads the core purpose (chronological table with gap stats) and includes a quota qualifier. There is zero waste, and the key details are immediately visible.

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 moderately complex tool with 4 parameters and no output schema, the description offers some context (local-compute, quota) but lacks details on calculation semantics (e.g., how 'drought' is defined, whether all album groups are included by default) and edge cases. The response_format param covers output variance, but overall completeness is adequate but incomplete.

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 75%, but the max_results parameter has no description in the schema, and the tool description does not clarify it. The description adds no parameter-specific meaning; it only describes the overall output. With moderate coverage and no compensation from description, parameter semantics are weak.

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 specifies a clear deliverable: a chronological release table with inter-release gap days, median gap, and longest drought highlighted. It names the underlying resource (/artists/{id}/albums), so an agent knows what data is involved. However, it does not explicitly differentiate itself from similar sibling tools like artist_discography_timeline or artist_singles_timeline, which limits clarity about its unique role.

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 description provides no guidance on when to use this tool vs alternatives. With many timeline/discography siblings (artist_discography_timeline, artist_singles_timeline, get_artist_albums, etc.), the agent must guess when this tool is appropriate. The quota note is operational, not usage guidance.

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?

The description explicitly discloses that the tool performs exactly one paginated /artists/{id}/albums walk and consumes one quota, which is valuable behavioral and cost context. It does not explicitly say 'read-only' or describe output formatting, but 'Numbers-only catalog profile' and the absence of mutation language make side effects unlikely.

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 description is two sentences with no fluff; the metric list is dense and front-loaded. There is minor redundancy between 'from one paginated discography walk' and the quota sentence's 'one paginated /artists/{id}/albums walk'.

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?

The enumerated metrics make the return value reasonably clear even without an output schema, and the quota disclosure aids cost planning. However, the description does not differentiate from the near-duplicate artist_discography_stats sibling, nor does it clarify pagination depth or edge cases, leaving tool selection somewhat ambiguous.

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 schema already documents artist_id, include_groups, and response_format. The description's mention of 'release totals by group' loosely aligns with include_groups but adds no parameter-level detail beyond what the schema provides.

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 specifies the tool's output as a numbers-only catalog profile and enumerates concrete metrics (release totals by group, summed track counts, average tracks, active span, busiest year, median release gap), making the function clear. However, it does not distinguish itself from the near-identically named sibling artist_discography_stats, so the description alone does not let an agent tell them apart.

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?

No when-to-use or alternative guidance is given. The quota and 'one paginated walk' hint at cost-aware usage, but the description never states when to prefer this tool over artist_discography_stats or artist_discography_timeline.

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 provided, the description carries the full disclosure burden and does well: it flags local-compute, real-payload provenance, the 403 failure mode with album-only fallback and 'explicit disclosure,' plus quota cost. It doesn't state read-only status, invalid-id behavior, or the return shape, but the disclosed failure/fallback behavior is far beyond typical.

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?

Every clause earns its place: source, computation origin, dead-endpoint avoidance, gating surface, fallback and disclosure, and quota. Purpose is front-loaded and there is zero filler given the complexity it conveys.

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 multi-source tool with no annotations and no output schema, the description covers provenance, failure mode, fallback, disclosure behavior, and cost. The main omission is a concrete description of the result shape, though 'co-appearance counts' and the response_format enum give partial hints. This is strong but not exhaustive.

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 80%, so the baseline is 3; the tool description adds almost no parameter-level meaning beyond alluding to album walking and counts. The one undocumented parameter (max_results) is not compensated for in the description. This meets but doesn't exceed the baseline.

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 opening clause states a specific verb ('extracted') plus resource ('an artist's top tracks and recent albums') and the computed metric ('co-appearance counts'), so an agent knows exactly what this tool produces. It also contrasts itself with the 'dead related-artists endpoint,' making its scope unambiguous. Though it doesn't name near-twin siblings like find_collaborations, the mechanism described is specific enough to tell it apart.

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 description gives actionable usage context: the top-tracks source is registration-gated (#329), the 403 fallback path is described, and the quota ('1 + paginated API calls') is stated. It also tells the agent to use real payloads over the dead related-artists endpoint. It lacks explicit routing to/from MCP sibling alternatives, so it stops short of a 5.

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. It does disclose meaningful traits beyond the schema: the depth-2 walk algorithm, the 'from discography walks alone' constraint, and the quota formula '1 + up to first_degree_cap additional small walks.' However, it does not explicitly state whether this is read-only, what happens on quota exhaustion, or how the result is returned.

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?

Two sentences with no filler: the first states the core algorithm and outcome, and the second gives the quota. The most important behavioral trait, depth-2 and quota, is front-loaded, and every clause 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?

The description covers the algorithm and quota but leaves gaps an agent may need: max_results is undocumented in both schema and description, and with no output schema the description does not clarify what the 'concise', 'detailed', or 'json' response formats contain. It is adequate for selecting the tool but not fully complete for invoking it with confidence.

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 83%, so the baseline is 3, but the description adds useful semantic context by explaining how first_degree_cap and releases_per_collab are used in the walk ('each top collaborator's recent releases') and tying first_degree_cap to the quota. It does not clarify the undocumented max_results parameter, but the algorithm-level explanation goes beyond the schema.

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 states a specific action and resource: it 'Builds a depth-2 collaboration web for one artist' via discography walks, with clear first- and second-degree link construction. It does not explicitly name sibling tools like artist_collab_network or find_collaborations, so some differentiation is left to inference, though the depth-2 framing helps.

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 description explains what the tool does but gives no explicit guidance on when to choose it over alternatives such as artist_collab_network, find_collaborations, or collab_density_report. There is no when-to-use statement, exclusions, or recommended scenarios, so an agent must infer applicability from the function alone.

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 provided, the description carries the behavioral burden. It discloses a significant operational trait: this is a paginated walk requiring a saved-albums membership check, which implies quota cost and multiple API calls. The red-circle quota warning is useful beyond what the schema conveys, though it does not mention read-only status or auth requirements.

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 description is two sentences with no filler. The core purpose is front-loaded, and the quota warning is compact but informative. Every element 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?

The description covers the tool's purpose and a key operational cost, but it omits usage routing and leaves max_results semantics unexplained. Since there is no output schema and sibling tools overlap significantly, the description could be more complete with a note on when to choose this over related gap/completeness tools.

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?

The schema describes artist_id and response_format but leaves max_results undocumented, and the description adds no parameter-level detail. The phrase 'studio albums' clarifies the output domain but does not help an agent understand max_results behavior, defaults, or how response_format affects the result. With 67% schema coverage, the description should compensate for the missing max_results semantics but does not.

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 names a specific verb ('List'), a specific resource ('an artist's studio albums'), and a clear filtering condition ('missing from your saved collection'). It also states the intended outcome ('the exact records to add next'), which distinguishes it from broader catalog or discography tools like get_artist_discography or artist_discography_gaps.

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 description implies a use case — finding what to add next to one's saved albums — but it does not explicitly state when to use this tool versus similar sibling tools such as artist_discography_gaps, artist_album_completeness, or artist_complete_check. There are no alternatives, exclusions, or preconditions mentioned.

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 it does disclose genuinely useful operational traits: a quota of 2 reads (artist albums + saved albums) and page-capping, which helps an agent budget API calls and anticipate truncation. However, 'page-capped' is left vague — no cap size or consequence for very large catalogs is given, and read-only status is only implied, never stated.

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?

Two tight sentences with the core purpose front-loaded and the quota disclosure as a value-add. Minor redundancy: 'your saved albums' appears in the first sentence and again in the quota parenthetical, but overall there is no filler.

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?

The core computation is clear, and the response_format enum in the schema covers output style choices despite the absence of an output schema. But with no return-shape guidance, no sibling differentiation, and the correctness implications of page-capping unexplained, an agent still lacks a few pieces needed to use it confidently in this crowded toolset.

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 four parameters are already documented in the schema, setting the baseline at 3. The description adds only marginal semantic value: '2 reads (artist albums + your saved albums)' clarifies the underlying data sources, and the album/single/compilation breakdown hints at what include_singles controls, but no parameter gains meaning beyond the schema.

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 names a concrete comparison task — the artist's full album list vs the user's saved albums, showing what's missing with an album/single/compilation breakdown. That is a specific resource-and-operation pairing. However, the huge sibling list contains near-twins (artist_completeness_score, artist_album_completeness, artist_collection_gaps, artist_discography_gaps), and nothing here distinguishes this tool from them.

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?

No when-to-use guidance, no exclusions, and no named alternatives. Given the dense cluster of similarly named artist-completeness siblings, an agent receives zero routing help to decide between this tool, artist_completeness_score, artist_album_completeness, or artist_collection_gaps.

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 provided, the description carries the full burden and does disclose the key safety trait ('Read-only') plus the operational cap ('capped at 25 artists'). However, it is silent on the multi-request cost structure implied by dry_run and scan_cap, and on pagination/failure behavior — meaningful gaps for a tool that can issue up to 25 chained requests.

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?

Two sentences, roughly 24 words, with the use case front-loaded and zero filler. Every clause earns its place: scenario, mechanism, output metric, safety trait, and cap.

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?

The core return concept (0–100% completeness score) is stated, which partially compensates for the absent output schema. But for a 5-param, multi-step tool with no annotations, the description leaves the result structure (per-artist breakdown vs aggregate), response_format semantics, and cost profile undescribed. Adequate for basic invocation, incomplete for confident use.

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%, and each parameter already carries a clear description (defaults, max, enum options, cost hints). The tool description only restates 'top N' and the 25 cap that top_n's schema entry already provides, so it adds no meaning beyond the schema — meeting the baseline for high coverage.

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 names a specific verb-resource pair ('fetch each artist's top tracks and score...') and defines the output metric (0–100% of saved top tracks). It also states the scope ceiling ('capped at 25 artists'), making the tool's function unambiguous even among many sibling tools.

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 opening 'For your top N saved-track artists' implies the scenario, but the description never states when to prefer this over the many overlapping siblings (e.g., artist_complete_check, artist_top_vs_saved, artist_collection_gaps, library_coverage_report) nor gives exclusions. Usage is inferable, not explicit.

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 behavioral disclosure burden. It does add useful transparency by stating the underlying paginated albums walk and quota, and it defines the returned content. However, it does not describe the output envelope, pagination behavior beyond 'one walk,' or any edge-case behavior.

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 description is two sentences: the first states the core behavior clearly, and the second gives a concise quota hint. It is front-loaded, memorable, and contains no filler.

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?

The tool has no output schema and no annotations, but the description explains what the tool returns (earliest release and same-year releases) and its quota. The main gap is not routing the agent to the simpler artist_first_release when only the debut release is needed, but the invocation-critical details are otherwise present.

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 the schema already documents both artist_id and response_format. The description adds no new parameter-level meaning beyond restating the overall purpose, so baseline 3 is appropriate.

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 uses a specific verb ('dig up') and gives a precise scope: the artist's earliest release plus everything else from that same year. This clearly distinguishes it from sibling tools like artist_first_release, which would only surface the single earliest release.

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 description does not say when to use this tool instead of related sibling tools such as artist_first_release, artist_latest_release_report, or artist_release_digest. The quota note is useful but does not provide selection guidance.

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 present, the description carries the full behavioral burden and does disclose a meaningful trait: quota cost (🟡) and the underlying 'one paginated /artists/{id}/albums walk'. This helps the agent weigh cost before calling. It stops short of confirming read-only posture or describing response details, but the quota/operation disclosure is genuine added value beyond structured fields.

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?

Two sentences with zero filler: the first front-loads the core deliverable (histogram + dominant decade) and the second adds the quota constraint. Every sentence 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?

For a low-complexity analysis tool with all three parameters documented in the schema and no output schema, the description covers the core computation, the headline insight, and the quota profile. Minor gaps (explicit read-only confirmation, output format across response_format values) are largely mitigated by the schema's enum documentation.

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 the baseline is 3; the description adds little parameter-level detail beyond implying the /artists/{id}/albums walk behind artist_id. It correctly avoids repeating schema docs, and the schema already documents include_groups defaults and response_format enum semantics.

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 states the tool produces a 'Histogram of an artist's releases per decade' with 'the dominant decade called out', which is a specific output over a clear resource. It is reasonably distinctive from siblings like artist_discography_timeline or era_distribution_report, though it doesn't explicitly contrast itself with those alternatives.

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 phrase 'instantly see which era carries the catalog' implies the usage context — quick era-level assessment of an artist's catalog — but there is no explicit when-to-use guidance or named alternatives. The agent must infer when this is preferred over the many sibling artist-analysis and era-report tools in the large sibling list.

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 behavioral disclosure burden. It does a solid job by revealing the exact matching algorithm, the ranking order, and a concrete quota ('2 walks + one chunked /albums?ids= fan-in'). It does not explicitly state read-only status or failure behavior, but the resource-consumption detail is valuable.

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?

Two tightly written sentences with no filler. The core definition is front-loaded, and the quota sentence earns its place by informing an agent about the cost of invocation.

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?

The description is largely complete for a complex analysis tool: it defines deep cuts, explains ranking, and discloses quota behavior. It does not describe edge cases like an artist with no deep cuts, but the response_format parameter in the schema covers the output contract.

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 75%, with artist_id, max_releases, and response_format already documented in the input schema. The description adds context to max_releases through the 'recent albums and singles' definition, but it does not clarify max_results, which lacks a schema description.

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 states a specific verb ('Finds'), a clear resource ('an artist's deep cuts'), and a precise operational definition: tracks whose normalized title appears on exactly one release across recent albums and singles, ranked longest-first. This makes the tool's purpose unambiguous and distinguishes it from generic siblings like deep_cuts_finder or b_sides_finder.

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 description gives no explicit guidance on when to prefer this tool over alternatives. With many sibling tools such as artist_deep_dive, deep_cuts_finder, and artist_discography_stats, an agent is left to infer which is appropriate based on names alone.

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 of behavioral disclosure. It does disclose the API quota ('1 + one paginated /artists/{id}/albums walk') and that the tool performs a multi-step walk, which is useful. However, it does not explicitly state read-only behavior, error cases, or the shape of the returned dossier beyond the field list.

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 description is two sentences with no filler: the first front-loads what the tool builds and lists its key outputs, and the second adds a tight operational constraint. Every phrase contributes, making it easy to scan.

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?

Given that there is no output schema and no annotations, the description compensates by enumerating the output content and stating the quota behavior. It still omits when-to-use versus sibling deep-dive tools, and it does not describe return formatting, but for the stated complexity it provides enough for an agent to make a reasonable call.

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 the schema already documents all three parameters, including accepted formats for artist_id and the response_format enum. The description adds no parameter-specific meaning beyond what the schema provides, which matches the baseline for high schema coverage.

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 uses a specific verb ('Builds') and a clear resource ('one-artist dossier') with an explicit list of computed fields, so an agent knows what the tool produces. However, it does not explicitly distinguish itself from adjacent sibling tools like artist_discography_timeline, artist_collab_network, or artist_discography_stats, so it misses the top score for sibling differentiation.

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 no guidance about when to choose this tool over alternatives, nor are any exclusions or when-not-to-use conditions stated. The quota note implies a cost consideration, but no explicit usage context or recommended alternative is provided.

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 at all, the description carries the full burden and does real work: it discloses the quota cost upfront (🟡 one paginated albums walk), the flexible input resolution behavior ('name → closest search match'), and the aggregated nature of the output. It does not explicitly affirm read-only/no-state-change, which would be the one remaining behavioral gap, but the stat-gathering framing and the disclosed cost make the behavior reasonably transparent.

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?

Two sentences with zero waste: the first front-loads what the tool produces, the second covers input flexibility, resolution behavior, and quota cost. Every clause earns its place and the density is high — output stats, accepted input forms, fallback resolution semantics, and a cost disclosure are all packed in efficiently.

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's list of stats (counts, first/latest, active span, per-year rate) partially compensates by describing return content. But the tool sits among several heavily overlapping siblings (artist_discography_stats, artist_release_type_breakdown, get_artist_discography) with no disambiguation, and the max_results parameter is left undocumented. An agent can call it correctly, but it may pick the wrong sibling and won't know the walk cap semantics without guessing.

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 75%, so the schema already documents artist, include_groups, and response_format. The description adds one genuine nuance beyond the schema — that a name resolves via 'closest search match' — but max_results (25% of params) has no description in either the schema or the tool description, so an agent must infer its purpose from maximum 2000 and the 'releases per active year' output. The description partially compensates but does not fully cover the gap.

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 names a specific verb and resource category ('Overview stats for one artist's discography') and enumerates the concrete outputs: release counts by type, first/latest releases, active span, releases per active year. This is clearly a summary/aggregate tool rather than a list-returning one. However, it never explicitly differentiates itself from near-twin siblings like artist_discography_stats, artist_release_type_breakdown, or get_artist_discography, so it stops short of a 5.

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?

Usage context is implied but not stated: 'Overview stats' signals this is the tool to pick when an agent wants a condensed profile of one artist, and the quota note ('one paginated /artists/{id}/albums walk') hints this is cheaper than a full discography pull. Yet there is no explicit when-to-use vs. when-not-to-use guidance, and no alternatives are named even though the sibling list contains several overlapping tools (artist_discography_stats, get_artist_discography, artist_decade_span).

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 present, the description carries the transparency burden and does add useful behavioral detail: default chronological ordering, a paginated walk, and a concrete quota cost (one walk, several queued calls on long discographies). It does not explicitly state read-only/side-effect status, but the framing clearly describes a read/compute operation.

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?

Two compact sentences front-load the core output and then give the implementation/quota context. Every clause earns its place, with no filler or repetition of schema fields.

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 read-only reporting tool with six well-described parameters and no output schema, the description explains the output shape (table, ordering, gap computation) and cost behavior sufficiently. Minor gaps remain around exact gap semantics (calendar days, missing-date handling) and explicit alternative routing, but these do not block correct invocation.

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 83%, so the schema already documents most parameters. The description only reinforces the order default already in the schema (oldest to newest) and adds no new meaning; max_results, the one undocumented parameter, is not addressed in the description.

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 identifies a specific output — a chronological discography table with per-release gap-in-days versus the previous release — so an agent can infer the tool's core function. It does not name or contrast with similar sibling tools like artist_discography_timeline or artist_discography_stats, so it stops short of full sibling differentiation.

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 description implies use for discography gap analysis by stating the output, and the pagination/quota note signals when it may be expensive. However, it provides no explicit when-to-use or when-not-to-use guidance, nor does it point to alternative tools for timeline or stats needs.

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 provided, the description carries the behavioral burden. It discloses that the tool performs a 'paginated /artists/{id}/albums walk' and communicates quota cost with a yellow indicator. This is useful behavioral context, though it does not explicitly state read-only semantics or result truncation behavior.

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 description is two sentences with no filler: the first front-loads the purpose and use case, the second adds quota/endpoint context. Every clause 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?

For a tool with five parameters and no output schema, the description is largely complete: parameters are covered by the schema, and the quota/endpoint note fills an important operational gap. It could be stronger by describing result shape or how max_results affects output, but the response_format parameter mitigates this.

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 high at 80%, so the schema already documents most parameters. The description adds helpful examples of what query should match ('live album, deluxe edition or collaboration'), but it does not add semantic value for max_results or clarify behavior beyond what the schema provides.

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 states a specific verb and resource: 'Full-text filter over an artist's release titles'. It also differentiates itself from sibling browse tools like get_artist_discography by framing the tool as a way to find specific albums 'without scrolling the whole catalog'.

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?

It clearly communicates when to use the tool: when you are looking for a particular release by title keyword within an artist's catalog. It does not explicitly name alternatives or state when not to use it, but the use case is evident and well-scoped.

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 provided, the description carries the full behavioral disclosure burden. It adds genuine non-obvious context: computation happens locally, and the tool performs an underlying paginated API walk with a disclosed quota cost. This is more informative than typical descriptions. It stops short of explicitly declaring read-only/no-mutation, though the 'walk' framing strongly implies a read traversal.

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?

Two sentences with zero waste. The first front-loads the purpose as a compact metric list, and the second discloses quota in a single parenthetical. Every clause earns its place; there is no fluff, repetition of schema content, or vague filler.

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?

Though there is no output schema, the listed metrics act as a de-facto output contract, and the response_format parameter covers prose-vs-json delivery. Quota and local-compute cover operational expectations. Missing pieces are an explicit read-only declaration and edge-case behavior (artist not found, empty discography), which would be needed for a 5 but are minor for an analytics tool.

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 artist_id ('Spotify artist ID'), include_groups (allowed values and default spelled out), and response_format (enum with per-value semantics) are already fully documented in the schema. The description's metric list only loosely maps to include_groups and adds no parameter-level detail beyond what the schema provides. Baseline 3 is correct here.

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 identifies a specific resource ('discography shape for one artist') and lists concrete computed outputs: release counts by type, first/latest release, per-year rate, and longest silence gap. These metrics are distinctive enough to separate it from siblings like artist_discography_timeline or artist_release_type_breakdown. It lacks an explicit verb ('compute/report' is implied) and does not name a sibling, so it falls just short of a 5.

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 [local-compute] tag and quota disclosure (' one paginated /artists/{id}/albums walk, typically several API calls') provide cost/runtime context that helps an agent decide whether to invoke the tool. However, there is no explicit when-to-use statement, no exclusions, and none of the many competing discography siblings (artist_discography_explorer, artist_discography_search, get_artist_discography) are named or routed around.

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 provided, the description carries the full disclosure burden and does so thoroughly: it discloses [local-compute] behavior, the paginated endpoint walk behind the scenes, newest-first ordering, client-side trimming for since_year, and a concrete quota/cost estimate. These go well beyond what the name or schema reveal and give the agent accurate expectations about performance and side effects.

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?

Three dense sentences with zero filler: the first establishes purpose and source, the second covers ordering and filtering behavior, the third discloses cost. Key facts are front-loaded before the quota caveat, and every clause 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?

For a 5-parameter read tool with no output schema and no annotations, the description covers the essential invocation context: endpoint, output columns, sort order, filter behavior, and quota impact. Combined with the schema's 80% coverage, an agent can select and call it correctly. The only gap is max_results semantics, which is minor since the parameter is optional and self-evident.

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 80%, so the baseline is 3. The description adds genuinely useful nuance — since_year filters client-side, and the table columns hint at output shape — which exceeds the schema's bare 'Only releases from this year onward'. However, max_results has no description in the schema and is not explained in the description either, leaving one parameter underdocumented.

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 names a specific deliverable — a chronological release table with defined columns (year, type, name, tracks) — and states the source endpoint (/artists/{id}/albums walk). The verb and resource are clear enough that an agent knows what it produces. However, it does not differentiate itself from closely related siblings like artist_album_timeline, artist_discography_explorer, or artist_singles_timeline, so the agent must infer the distinction.

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?

Usage context is implied through the purpose (want a year-sorted release table for an artist) and the quota signal ('one paginated walk... on the rate-limited queue'), which hints that this tool is costlier than a single-call op. However, there is no explicit when-to-use vs. when-not-to-use guidance and no mention of alternatives among the many discography-themed siblings.

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 provided, the description carries the full burden and does well: it discloses the clustering behavior, the output shape (one representative album, era spans), and the operational cost (' one paginated /artists/{id}/albums walk'). It does not state edge-case behavior or the selection criterion for the representative album, but the quota and API-walk disclosure is genuinely valuable beyond 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?

Two sentences, both dense and useful: the first states the core function, the second gives quota/API-walk context. No fluff, no repetition of schema content, and the most important information is front-loaded.

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?

Given no output schema and no annotations, the description carries a heavy burden. It covers the algorithm, output shape, and quota, but leaves the 'representative album' selection criterion undefined and omits edge cases (artists with no quiet periods, single-release artists). Adequate for a competent agent, but not complete.

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 the baseline is 3. The description adds only modest value by back-referencing `gap_years` in the algorithm and implying the /artists/{id}/albums call that maps to artist_id and include_groups, but it does not add meaning the schema already lacks.

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 names a specific verb (cluster), a resource (an artist's releases), and a precise method: eras separated by quiet periods longer than `gap_years`, with one representative album per era and era spans. This clearly distinguishes it from siblings like artist_era_sampler, artist_album_timeline, and artist_discography_timeline.

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 algorithm is specific enough that an agent can infer when to call it (when era segmentation by quiet periods is needed), but there is no explicit when-to-use guidance, no exclusions, and no named alternatives. With many era-related siblings in the toolset, explicit routing would materially help.

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 behavioral burden and provides real context: selection is deterministic, albums take precedence with earliest/fullest ordering, and exactly one paginated /artists/{id}/albums walk is used. It does not explicitly state that it performs no mutations, but the 'sampling plan' framing and quota disclosure make the read-only intent reasonably clear.

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?

Two tightly written sentences front-load the main action and selection criteria, then add the quota note. There is no filler or repetition of schema information.

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?

The description covers the algorithm, deterministic ordering, and API quota, and the schema covers all parameters. However, there is no output schema, and the description only minimally indicates what the returned sampling plan looks like; edge cases such as artists with fewer releases than eras are also left unaddressed.

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 the schema already documents artist_id, eras, per_era, and response_format. The description adds selection-rule context but no extra parameter-level semantics, so it meets the baseline without exceeding it.

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?

Description opens with a specific verb+resource: it splits an artist discography into eras and deterministically picks representative releases. It also states the selection rule (albums first, earliest and fullest first) and labels itself a sampling plan, which distinguishes it from timeline/map/report siblings like artist_discography_timeline or artist_era_map.

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 phrase 'as a sampling plan' plus the quota line implies this tool is for producing a representative per-era selection rather than exhaustive discography output. However, it never states when to prefer it over similar planners such as album_representative_plan, decade_sampler_plan, or artist_era_map, nor when it would be inappropriate.

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 provided, the description carries the full burden of behavioral disclosure. It does add value by mentioning the quota ('one paginated walk + 1 GET /albums/{id}'), which indicates resource usage and multiple API calls. However, it does not disclose error behavior, how 'earliest' is determined (e.g., by release date vs. first appearance), or whether the operation is read-only (though implied). This is adequate but not comprehensive.

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 description is two sentences with zero wasted words. The core purpose is front-loaded in the first sentence, and the quota is stated as a second informative note. It is appropriately concise and well-structured.

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?

The tool has 2 simple parameters and no output schema. The description explains what is returned (label, copyright, track listing, runtime) and mentions the quota, which is useful operational context. It does not explicitly define 'earliest release' (e.g., by release date, original vs. reissue), which could be ambiguous, but given the specific name and the mention of 'across all album groups', it is mostly complete. A more precise definition of 'earliest' would push it to a 5.

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 both parameters are already fully documented. The description does not add any meaning beyond the schema, such as clarifying acceptable input formats or the impact of response_format on the output. It simply mentions the return card, which is not parameter-specific. This matches the baseline of 3.

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 clearly states the verb (Locates) and resource (artist's earliest release), and specifies what is returned (full card: label, copyright, track listing, runtime). However, it does not explicitly distinguish from the sibling tool 'artist_debut_release_finder', which likely has a similar purpose, so it falls short of a 5.

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 description provides no guidance on when to use this tool versus alternatives, such as when to prefer it over artist_latest_release_report or artist_debut_release_finder. It does not mention any conditions or exclusions, leaving the agent to infer the appropriate context on its own.

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 provided, the description carries the full disclosure burden. It adds genuinely useful behavior: the 50-ID batch cap, the single-call quota (️ one GET /artists?ids= call), and the two-column output shape. However, it is silent on edge-case behavior such as invalid or unknown IDs, duplicate ID handling, and ordering, which leaves meaningful gaps for a batch tool.

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?

Two tight sentences with zero wasted words. The input→output contract is front-loaded in the first sentence, and the quota disclosure sits cleanly in the second. The arrow and emoji are compact scannability aids, not filler.

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 low-complexity batch projection with 100% schema coverage and no output schema, the description supplies everything essential: input cap, output columns, and API cost. The remaining gaps — edge-case behavior and the exact rendering of the genres field in responses — are minor for this tool class. Slightly above the viable minimum.

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%: artist_ids documents its 50-ID cap and response_format documents each enum value's meaning. The description adds the output-side interpretation ('name·genres two-column projection') that connects artist_ids to its result, but parameter-level semantics are already fully carried by the schema, 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?

The description states a precise contract: up to 50 artist IDs transform into a name·genres two-column projection. The phrase 'compact roster view over sever-al-artists' distinguishes it from the full-data sibling get_several_artists and the single-artist get_artists_genres, so an agent can tell this tool apart without opening their 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?

The 'compact roster view' phrasing implies the intended use case (lightweight, name+genres-only lookup) and the quota note hints at efficiency as a selection factor, but no alternative tool is explicitly named and no when-not-to-use condition is stated. The agent must infer when to prefer this over get_several_artists or get_artists_genres.

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. It does add genuinely useful context: the 'one paginated walk + 1-2 GET /albums/{id}' quota discloses cost and request pattern, and 'reports on' plus the GET references imply a read-only operation. It does not, however, address edge cases (artist with no releases), authentication needs, or what determines 'most recent'.

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?

Two sentences with no wasted words: the main deliverable is front-loaded, the report contents are enumerated compactly, and the quota note at the end earns its place by informing cost-aware tool selection. Every clause adds value.

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 does a reasonable job of enumerating what the report returns, and with no annotations it discloses the request cost. It is still incomplete: it lacks any usage guidance among near-name siblings and no edge-case handling (e.g., artist with no releases, scope of the paginated walk).

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 the schema fully documents all three parameters (artist_id format, include_groups default, response_format enum). The description adds no parameter-level meaning beyond that, so the baseline of 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?

The description states a concrete purpose: reporting on an artist's most recent release, with enumerated report contents (label, copyright, track listing, runtime, days since release, gap to prior release). The 'gap to the release before it' detail differentiates it from similar siblings like artist_latest_releases and get_artist_albums, but the description never names those alternatives, leaving the differentiation implicit.

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?

No guidance on when to choose this tool over the many overlapping siblings (artist_latest_releases, artist_release_digest, artist_first_release, check_artist_releases, get_artist_albums). There is no 'use this when...' statement and no exclusions, so an agent must infer the selection criteria from the name alone.

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?

Annotations are absent, so the description carries the full burden, and it delivers: the quota disclosure ('🟡 one paginated /artists/{id}/albums walk') reveals pagination behavior, underlying API calls, and rate-limit cost, and 'age in days' explains the output computation. The verb 'Fetch' implies a non-destructive read, though it stops short of explicitly declaring read-only status or addressing empty-catalog/error cases.

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?

Two sentences with zero filler: the first fronts the purpose and key output detail, the second delivers the quota cost. Every phrase earns its place, and the structure makes the tool's intent legible at a glance.

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 filtered-list read with fully documented parameters, the description is nearly complete: it states purpose, output flavor (age in days), and rate-limit cost. Gaps are minor — no explicit safety framing (read-only) and no edge-case behavior — but with no annotations and no output schema, a touch more disclosure would make it fully self-sufficient.

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 the schema already documents all four parameters with defaults and enums. The description adds only tangential reinforcement ('most recent N' maps to limit, quota maps to the underlying albums walk) rather than new parameter-level meaning, which matches the baseline 3 for full schema coverage.

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 states a specific verb ('Fetch'), a well-defined resource ('the most recent N releases from an artist's catalog'), and a distinguishing output feature ('age in days'). The 'quick what's new here read' framing clarifies its intent, but it does not explicitly differentiate itself from closely related siblings like artist_latest_release_report, check_artist_releases, or artist_release_digest.

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 'quick what's new here read' phrase implies when to use the tool, and the quota note signals operational cost. However, it names no alternatives and gives no exclusion criteria or conditions for choosing a sibling tool, which is a real gap given the large number of overlapping artist-catalog tools in the toolset.

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 available, the description carries the full behavioral disclosure burden. It does well by stating the quota cost and underlying data source: GET /me/player/recently-played cursor walk. It stops short of explicitly stating read-only/no side effects, though the wording strongly implies a safe read operation.

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?

Two sentences, front-loaded purpose, and no filler. The quota disclosure earns its place because it gives the agent important cost information without bloating the description.

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 low-complexity tool with three optional and fully documented parameters, the description covers purpose, output shape at a high level, default behavior, and quota cost. It does not define the exact 'history window' boundaries, but that is not critical for invoking the tool 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 description coverage is 100%, so the baseline is 3. The description's mention of 'defaults to your most-played artist' mostly restates the artist parameter schema and does not add meaningful syntax, formatting, or behavioral detail beyond what the schema already documents.

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 names a specific verb and resource: it profiles the listening times for one specific artist, with an hour-of-day histogram and daypart split. The 'specific artist' framing clearly distinguishes it from general listening clock/heatmap tools, and the default-to-most-played-artist behavior adds useful scope.

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 use case is reasonably implied by 'one specific artist' and the default behavior, but there is no explicit when-to-use or when-not-to-use guidance, and no alternative tools are named. An agent must infer when to choose this over similar listening-pattern tools like listening_clock or artist_deep_dive.

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 provided, the description carries the full behavioral disclosure burden, and it does so well: it reveals that filtering is heuristic title-pattern matching rather than official release-type metadata, states the chronological ordering, and discloses the underlying paginated endpoint and its quota cost. It stops short of disclosing empty-result behavior, case-insensitivity of pattern matching, or the exact output shape.

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?

Two sentences with zero filler: purpose and filtering rule first, then the quota/endpoint constraint. The 🟡 traffic-light notation is a compact, decision-relevant cost signal that earns its place in a quota-aware toolset.

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 simple 3-param list tool with no output schema, the description covers the purpose, filter heuristic, ordering, and cost profile; response_format behavior is left to the schema's enum descriptions. Remaining gaps (behavior on zero matches, exact return shape) are minor and non-blocking for correct invocation.

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 67% (artist_id and response_format are documented with helpful descriptions), so the schema does most of the work. The description adds the paginated-walk context that gives max_results meaning, but it adds no per-parameter detail beyond that. Mid-level coverage means the description need not fully compensate, and it doesn't.

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 states a specific verb ('List'), a scoped resource (an artist's live releases), the exact filtering rule (titles matching live/unplugged/live-at patterns), and chronological ordering. The 'concert-record shelf' metaphor clearly differentiates it from sibling album-type tools like get_artist_singles and get_artist_albums, and the parenthetical pattern list pins down the matching logic precisely.

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 use case is clearly scoped — an explicit query for an artist's live releases/concert recordings — and the quota note ('one paginated /artists/{id}/albums walk') gives cost-aware selection context in a toolset that clearly tracks quota (quota_probe, backup_first siblings). However, it never names an alternative or states when not to use it, leaving sibling comparison 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 behavioral disclosure burden, and it does well: it explains the multi-step behavior (typed search, candidate profiling, discography probe) and includes a specific quota ('1 /search + 1 small albums call per candidate'). It does not cover edge cases like no candidates found or explicit permissions, but the quota and operation flow are strong.

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 description is two sentences, front-loaded with the core purpose and immediately followed by the operational behavior and quota. Every sentence contributes relevant information with no padding or repetition of schema details.

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?

The tool is moderately complex, and the description covers the workflow, quota, and the kinds of profile data returned. It does not spell out the exact output shape or failure behavior, but the response_format parameter and the candidate profiling detail provide enough for an agent to invoke it correctly. A slight gap is lack of handling guidance for zero or low-confidence candidates.

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 the baseline is 3. The description adds some contextual value by explaining that candidates are profiled and that there is a per-candidate quota, which indirectly relates to candidates_cap. However, it does not add parameter-specific meaning beyond what the schema already provides.

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 states a concrete goal, 'Resolves an ambiguous artist name,' and details the method: typed search plus candidate profiling with genres, active year span, and a sample release. This clearly distinguishes it from raw search tools by emphasizing the disambiguation purpose. It also names the output goal ('so you can pick the right ID'), which is specific and actionable.

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 description clearly implies when to use the tool: when an artist name is ambiguous and you need to select the correct ID. It does not explicitly name alternatives or state 'do not use when...', so it misses the highest bar, but the context is clear enough for an agent to select it for disambiguation rather than a simple search.

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 burden of behavioral disclosure. It adds a useful quota/cost signal: one paginated /artists/{id}/albums walk, which helps the agent anticipate expense. It also explains grouping behavior, though it does not disclose exact return shape or edge cases.

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?

Two sentences, front-loaded with the main action and expected output, followed by a concise quota note. No filler or repetition of schema details.

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 read-style detection tool, the description covers purpose, expected output concept, and quota behavior. There is no output schema, so a bit more detail on the returned grouping structure would be ideal, but the response_format parameter and the grouping statement provide enough for an agent to invoke 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 description coverage is 75%, so most parameters are already documented. The description does not add much param-level detail, but it does imply that artist_id maps to the /artists/{id}/albums walk. The undocumented max_results is not compensated for, but overall the schema carries the parameter burden.

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 states a specific verb ('Detect') and resource (alternate versions in an artist's release titles), and defines the key outcome: group by base title and identify canonical releases vs variants. This distinguishes it from sibling tools like duplicate_saved_versions or album_edition_lint without needing extra context.

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?

Usage is implied clearly: use when you need to find reissues, deluxe, remaster, or lived editions within an artist's discography. However, it does not explicitly state when to prefer this over similar sibling tools or mention exclusions, so the agent must infer the decision from the purpose.

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 present, so the description carries the full burden. The WARNING (N artists = N requests) is a genuinely useful behavioral disclosure about request amplification. The mention of dry_run ('describe exactly what would change without performing it') implies the tool mutates state, but the description never explicitly states that the digest records the last-check time or what side effects occur.

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?

Two sentences with zero waste. The purpose is front-loaded, followed by the cost warning and the mitigation guidance. Every clause 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?

Given there is no annotation coverage and no output schema, and the tool is stateful (since the last check) amid hundreds of siblings, the description is only partially complete. It covers the core purpose and the cost risk well, but omits explicit state-mutation disclosure, sibling routing, and any sense of return semantics beyond the response_format enum in the schema.

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 the baseline is 3; the schema already documents defaults, enums, and bounds for all five parameters. The description adds minor value by framing max_artists as a budget and dry_run as a preview mechanism, but it doesn't clarify parameter semantics beyond that.

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 uses a specific verb (Show) with a clear resource (a digest of new releases since the last check for a watchlist). The watchlist + last-check state makes the purpose distinctive. However, it does not explicitly differentiate from close release-related siblings such as check_artist_releases, whats_new, or artistwatch_new_additions, so an agent must infer the right choice.

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?

It gives direct invocation guidance: 'Use max_artists to budget and dry_run to preview,' plus a cost warning. That is real operational guidance, but there is no when-to-use vs alternatives guidance and no statement of when not to use it (e.g., for single-artist checks use check_artist_releases).

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 of behavioral disclosure. It adds genuine value by disclosing the quota cost — 'one paginated /artists/{id}/albums walk' — which informs the agent about API expense and internal pagination. However, it does not explicitly state that the operation is read-only, nor cover failure modes or behavior on large or empty discographies, leaving disclosure partial.

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?

Two sentences with zero waste: the function and output fields are front-loaded, and the quota sentence adds practical cost information rather than padding. Every sentence 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?

With no output schema, return-value explanation falls to the description, which adequately conveys the result semantics (counts, first/latest per type, sample titles). Combined with full parameter documentation and the quota note, an agent has enough to select and invoke correctly. Minor unspecified gaps — the exact shape of the 'json' output and edge cases like artists with no albums — do not block correct usage.

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%: all three parameters (artist_id, include_groups, response_format) are documented with types, defaults, enums, and value semantics. The description adds only marginal reinforcement by echoing the album-group values and implying output-shape differences across response formats; it does not carry meaning the schema lacks, 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: 'Break an artist's discography down by album type', enumerates the four types (album · single · compilation · appears_on), and specifies the output fields (counts, first and latest per type, sample titles). This level of specificity distinguishes it from raw-list siblings like get_artist_albums and from other aggregation tools like artist_discography_stats.

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?

Usage is implied: an agent wanting a per-type aggregation of an artist's catalog would infer this tool fits. However, the description never names alternatives or states when-not-to-use, and the sibling list contains many overlapping discography tools (artist_discography_stats, artist_catalog_stats, artist_album_timeline), so explicit routing guidance would materially help.

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 present, the description carries the full behavioral burden. It explicitly labels the tool 'Read-only,' which is a critical non-mutation trait. It also clarifies the algorithmic behavior that credited artists are counted for each saved track they appear on, not merely artists you follow or own albums by.

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 entire description is one tight, front-loaded sentence. It defines the resource, the operation, the ranking criterion, and the read-only nature without any filler or redundant restatement.

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 read-only aggregation tool, the description plus the fully documented schema is largely sufficient. It explains the core output concept, and the schema covers caps and response formats. It could be more complete by noting how multi-artist tracks affect counts or what the returned rows look like, but these are not blocking gaps.

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 four parameters are already documented in the schema. The description adds only a small reinforcement of 'top N' aligning with top_n, but it does not otherwise deepen meaning beyond the schema. Baseline of 3 is appropriate.

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 clearly states the operation: censusing saved tracks and ranking credited artists by how many saved tracks they appear on. The verb 'rank' and the scope 'your saved tracks' are specific. It does not explicitly differentiate from near-neighbor siblings such as saved_tracks_by_artist or top_artist_leaderboard, so it misses the top score.

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 description implies when to use the tool: when you want an artist-representation ranking across your saved tracks. However, it does not explicitly state when to prefer this over alternatives, nor does it mention any exclusions or competing tools like saved_tracks_by_artist or top_artist_leaderboard.

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?

Annotations are completely absent, so the description carries the full burden. It discloses that the operation reads a playlist, scans items, and performs membership checks against saved tracks. It also flags quota cost with the 🔴 emoji. It does not specify side effects, but the term 'find new-to-you artists' plus 'membership check' implies a read-only discovery process. It transparently signals the two-pass operation (playlist walk + saved-track checks).

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 description is one compact sentence plus a short quota note. The core behavior is front-loaded, followed immediately by the differentiating value proposition. No redundant phrases. The quota signal earns its place as actionable information.

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?

The tool's complexity is moderate: it takes one required playlist and several optional controls. The description covers the main workflow, the key qualifier (unsaved artists), and an important operational detail (quota). It doesn't describe the output shape, but there is no output schema and the response_format parameter covers output style. Minor gap: it doesn't mention whether the playlist must be owned by the user or whether any playlist works, but 'one of your playlists' implies ownership.

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 description does not add parameter-level detail beyond the schema. The parameters max_items, max_artists, playlist_id, and response_format are all documented in the schema. The description communicates the discovery semantics but not parameter-specific behavior such as defaults or how response_format interacts with output. Baseline 3 is appropriate.

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 uses a specific verb ('pull' and 'rank') and clearly identifies both the resource ('artist roster from one of your playlists') and the core value proposition ('artists you have NOT saved any tracks of'). The phrase 'discovery from your own rotation' further anchors intent. It distinguishes itself from sibling tools like saved_tracks_by_artist or playlist_artist_heat because it explicitly targets unsaved artists.

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 description states that the input is a playlist from the user's own rotation and that the output is newcomers (unsaved artists). It implies this is the right tool for discovery from a playlist, but it does not explicitly say when NOT to use it or name alternative tools (e.g., discovery_digest for broader discovery, saved_tracks_by_artist for saved-artist stats). Still, it gives enough context for an agent to infer appropriate invocation.

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 discloses genuinely valuable traits: the underlying implementation (one paginated /artists/{id}/albums walk), the scope filter (singles group), the cost tier (🟡), and the output shape (date · title · track count). The rate-limit/cost disclosure is exactly the context the rubric credits. It omits edge behaviors like reissue handling or pagination semantics, but the core behavioral profile is transparent.

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?

Two tight sentences with zero filler: the first front-loads the core behavior, the second packs cost and implementation detail. The '45-rpm history' metaphor earns its place by making the deliverable instantly recognizable.

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 read-only tool with no annotations and no output schema, the description conveys the core deliverable, scope, and quota but stops short of covering max_results semantics, response_format behavior, or edge cases (empty catalogs, invalid artist IDs). Adequate for the common path, incomplete for the full call contract.

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?

The schema documents artist_id and response_format well, but max_results has no description in either the schema or the tool description, so an agent cannot know it caps the result set. The tool description contributes zero parameter meaning and does not compensate for the 33% schema coverage gap. Neither source explains the one non-obvious parameter.

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 states a specific deliverable: a chronological singles timeline showing date, title, and track count, with the vivid '45-rpm history in one table' metaphor reinforcing the scope. The singles-only scope distinguishes it from the discography-wide artist_discography_timeline and from the plain listing implied by get_artist_singles.

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 use case is implied — a historical, one-table view of an artist's singles — but no alternatives are named and no when-to-use versus when-not-to-use guidance is given. Close siblings like get_artist_singles and artist_discography_timeline exist, yet the description never routes the agent between them. The quota note hints at cost but not at selection logic.

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 provided, the description carries the full burden and does well: it explicitly declares 'Read-only', states that no popularity fields are used (clarifying what the tool does NOT compute), and discloses the quota cost ('1 /me/top/artists + 2 capped library walks'). This goes beyond a typical description by revealing data sources and resource footprint, though it does not address edge-case behavior such as empty libraries or zero saved items.

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?

Three tight sentences with zero waste: first sentence delivers the core function, second adds the safety profile, third adds operational cost. The most important content is front-loaded, and each sentence earns its place without repetition.

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 read-only comparison tool with no output schema and a fully-documented input schema, the description covers the computation, the output ordering, the safety profile, and the quota. It does not elaborate on the return format (beyond the response_format param) or edge cases like empty results, but the core contract an agent needs to invoke and interpret results is present.

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 five parameters (window, artists_cap, response_format, saved_album_cap, saved_track_cap) are already documented. The description adds minimal param-level meaning — the phrase 'capped library walks' contextualizes the two caps, and 'top artists' links to artists_cap/window — but it does not meaningfully extend the schema's own documentation. Baseline 3 is appropriate.

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 uses a specific verb ('compares') with a clear resource ('top artists from /me/top/artists' vs 'your own library') and states the exact output: saved-album and saved-track counts per top artist, ranked by library presence. This distinguishes it from siblings like get_top_artists (plain retrieval), saved_vs_playlist_coverage (playlist comparison), and artist_representation_census (census-style analysis).

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 description gives implied usage context — it computes library-presence ranking of top artists and is explicitly read-only with a stated quota — but it never names alternatives or conditions for when to choose this over adjacent tools like top_artists_by_range, saved_vs_playlist_coverage, or library_coverage_report. There are no when/when-not statements, so an agent must infer the selection criteria.

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?

There are no annotations, so the description carries the behavioral burden. It discloses that the tool performs 2× GET /me/top/artists, which is useful quota/cost transparency. However, it does not explain whether this is read-only, what the output shape will be, how pagination or max_results interacts with the two windows, or any errors/edge cases (e.g., empty history, ties in ranking).

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 description is concise, one sentence plus a quota note. The core function is front-loaded, and the quota information is useful and not redundant with the schema. It could arguably include a bit more detail on output behavior, but the sentence itself is efficient and well-structured.

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 read-only analytics tool with three parameters and no output schema, the description is decent but incomplete. It tells the agent what it computes and the quota cost, but it does not describe the output structure, how the classification labels map to output values, or how limit/max_results affect the two windows. Given the large sibling set of analytics tools, more contextual guidance would help.

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 parameters are already documented in the schema. The description adds no parameter-specific meaning beyond the schema and does not clarify how limit and max_results apply across the two rank windows (short_term vs long_term). Baseline 3 is appropriate because the schema covers the parameters but the description contributes little extra semantic value.

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 clearly specifies what the tool does: 'Classify each top artist’s momentum by comparing short_term and long_term ranks' and names the classification labels. However, it does not explicitly distinguish itself from sibling analytics tools like top_artist_ranking_delta or taste_shift_report, which may also involve rank comparisons, so differentiation is incomplete.

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 description implies the tool is for momentum analysis of top artists, which gives some usage context. It does not state when to prefer this over similar analytics tools, nor does it mention any prerequisites (e.g., that a user must have listening history or that short/long term data must exist). No explicit exclusions or alternatives are given.

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 behavioral burden and delivers real disclosure: it states it walks /me/following, probes each artist's latest release, and quantifies the quota as 1 cursor walk + 1 small albums call per artist — useful cost and latency context an agent wouldn't get from the schema. It falls short of full transparency by not warning about sequential-call latency when artists_cap is high and not clarifying what the flagged output looks like.

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?

Two tight sentences front-load the purpose ('Watches your FOLLOWED artists for new material') before the mechanics and quota disclosure. Every clause earns its place, including the valuable quota note, and there is zero filler.

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 read tool with no annotations and no output schema, it covers the core behavior and quota cost well. But it never describes the return shape ('flags' is vague), gives no routing among the many watch/check/digest siblings, and omits a latency caveat for high artists_cap values — gaps that matter because neither annotations nor an output schema exist to fill them.

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 applies; the description's 'last N days' and per-artist probe narrative do align conceptually with the days and artists_cap parameters. It adds no syntax, format, or interaction detail beyond the schema, which is acceptable at full coverage but not additive.

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 names a specific action (watches, probes, flags) and a specific scope (FOLLOWED artists via /me/following), tied to a freshness window, so an agent can tell what it does. However, with near-overlapping siblings like check_artist_releases, artist_release_digest, and new_music_from_saved_artists in the toolset, it never states how it differs from them, stopping short of full disambiguation.

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 mechanism and quota note imply a monitoring use case, so an agent can infer roughly when it applies. But there is no explicit when-to-use guidance, no exclusions, and no mention of alternatives despite several close siblings in the same domain, leaving routing largely 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 discloses meaningful behavior: [local-compute] signals where processing happens, and the Quota line reveals the real cost — a GET /audiobooks/{id} call plus a paged GET /audiobooks/{id}/chapters call, implying pagination and a multi-call operation. The market gate is an additional behavioral constraint. It does not confirm read-only semantics or describe error/return behavior, but the cost and gating disclosures go well beyond a bare statement.

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?

Three sentences with the purpose front-loaded and the market gate and quota clearly labeled. Every sentence earns its place, though the telegraphic all-caps labels make it slightly cramped rather than elegant.

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 annotations and no output schema, the description covers the core outputs (duration map, total runtime, midpoint) and the API cost, which partially substitutes for a return-value spec. Gaps remain: max_results semantics, whether response_format changes the computed midpoint or raw payload, and how this tool relates to the raw chapter endpoints.

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 67% — audiobook_id and response_format have descriptions, but max_results has none, and the description adds no per-parameter meaning. The critical question of what max_results caps (chapters fetched, map size, or something else) is left unanswered by both schema and description.

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 states a clear scope ('one audiobook') and three concrete computed outputs: a chapter-by-chapter duration map, total runtime, and the mid-point chapter. This reads as a distinct analytics aggregation rather than a raw listing, functionally separating it from siblings like get_audiobook_chapters and get_audiobook, 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 only usage signal is the MARKET GATE restriction (US/UK/CA/IE/NZ/AU only), which is a platform-level market exclusion rather than a tool-selection rule. There is no guidance on when to choose this over get_audiobook_chapters, audiobook_progress, or list_all_chapters, and no statement of the user need this tool answers.

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 provided, the description carries the behavioral burden. It usefully discloses the quota ('1 + N reads'), market-gating limitations, and error behavior. The read-only nature is implied by the quota wording and the tool's list-like description, which is sufficient for a non-destructive library query.

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 description is compact and front-loaded with the core purpose, followed by two clearly separated operational notes (quota and market gating). Every sentence adds relevant information, and there is no filler or redundant restatement of the tool name.

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 simple read-only tool with four optional parameters and no output schema, the description covers the main output content, sorting, quota, and market gating. However, it does not clarify the distinction between max_audiobooks and max_results, and it lacks sibling differentiation, leaving some selection and invocation ambiguity.

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 50%: max_results and response_format already have descriptions, while sort and max_audiobooks do not. The description adds meaning for sort by emphasizing progress sorting and confirms the tool's scope, but it does not explain max_audiobooks or how it relates to max_results. The compensation for the schema gap is only partial.

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 clearly states the resource ('all saved audiobooks'), the key data shown (percent complete and estimated time remaining), and the default organization ('sorted by progress'). The phrase 'what am I actually reading' conveys the user intent well. It does not explicitly contrast with sibling tools like audiobook_progress or get_saved_audiobooks, so it falls just short of full differentiation.

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 description provides operational details such as quota and market gating, but gives no guidance on when to choose this tool over similar audiobook-related siblings like audiobook_progress, get_saved_audiobooks, or search_audiobooks. There is no explicit 'use this when' or mention of alternatives.

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 behavioral burden and does well: it discloses that the tool performs 2 GET calls (audiobook + chapters) and reports the quota cost. It lists the output fields but doesn't cover error behavior or exact response formatting, which keeps 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?

Two sentences with no waste: the first states what the tool returns, the second states quota cost. It avoids repeating schema content and is easily parsed by an agent.

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?

Without an output schema, the description names all key return fields and the underlying API calls. It doesn't clarify the precise semantics of 'played count' or 'percent complete', but combined with the schema's response_format documentation this is sufficient for correct invocation.

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 baseline 3 applies. The description adds no parameter-specific meaning, but the schema already documents audiobook_id, market, and response_format, so no compensation is required.

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 names the resource ('audiobook progress') and the exact metrics returned: chapters total, played count, current chapter, percent complete. 'Rollup' conveys aggregation, which differentiates it from raw chapter-list tools, though it lacks an explicit verb and direct contrast to sibling audiobook_library_progress.

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?

No guidance is given on when to use this tool versus alternatives like get_audiobook, get_audiobook_chapters, or audiobook_library_progress. The quota note is a cost constraint, not a usage rule or exclusion.

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 provided, the description carries the full burden — and it delivers: it discloses client-side sorting semantics, the release-date fallback when Spotify doesn't expose one (unknown dates sort last), the chapter-count length proxy, the US/UK/CA/IE/NZ/AU market restriction, and a one-GET /search-call quota. It omits read-only safety and failure/empty-result behavior, but these are minor against the concrete operational details it does reveal.

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?

Three sentences with zero filler: purpose+sort mechanics up front, then the market gate, then quota. Every sentence carries distinct operational information, and the structure front-loads the core function before constraints.

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 5-parameter tool with no output schema and no annotations, the description covers the operation, the tricky sort edge cases, the market restriction, and the quota cost — all genuinely useful calling context. Remaining gaps (result shape, behavior on unknown authors) are mitigated by the response_format parameter and the simplicity of the query, so the overall picture is adequate.

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. The description earns extra credit on the `sort` parameter: it explains that release dates apply only 'when Spotify exposes one', that unknown dates sort last, and that length uses chapter count as a proxy — nuances the schema's one-line enum description lacks. The other parameters are adequately served by the schema.

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 states a specific function — an author catalogue built via the `author:` search filter — plus the client-side sorting it applies. The verb+resource (list audiobooks by author) is clear and distinguishes it in scope from generic siblings like search_audiobooks and library-based get_saved_audiobooks, though it never names a sibling 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?

No explicit when-to-use or when-not-to-use guidance is given; there is no mention of preferring this over search_audiobooks or get_saved_audiobooks for broader searches. The usage context is implied by 'Author catalogue', and the market gate is a constraint rather than routing advice. An agent must infer the selection logic.

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