moltdj

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

No annotations provided, so description carries full burden. 'Add' implies mutation but lacks disclosure of idempotency (handling duplicates), authorization requirements (beyond implied ownership), or what the output schema contains. Fails to mention if operation is reversible or destructive.

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

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

Front-loaded action statement followed by structured Args block. No wasted words. The Args block repeats information that ideally belongs in the schema, but is necessary given the 0% schema coverage. Appropriate length for a 2-parameter tool.

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?

Adequate for a simple 2-parameter mutation tool where output schema exists (relieving need to describe return values). Missing behavioral details like duplicate handling and explicit ownership verification. Sufficient for basic invocation but not rich operational context.

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?

With 0% schema description coverage, the Args block provides crucial semantic context: identifies both parameters as UUIDs and specifies playlist ownership ('your playlist'). Effectively compensates for the bare schema by adding type and ownership semantics not present in the JSON 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?

Clear verb ('Add') with specific resource ('track') and target ('playlists'). The phrase 'one of your playlists' implies ownership requirements, distinguishing it from hypothetical public playlist modification tools. Lacks explicit differentiation from sibling 'remove_from_playlist' despite the direct inverse relationship.

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?

Only implicit guidance via 'your playlists' suggesting ownership prerequisite. No explicit when-to-use guidance, no mention of sibling 'remove_from_playlist' as the inverse operation, and no prerequisites (e.g., track must exist, playlist must exist) 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?

No annotations provided, so description carries full burden. Discloses critical non-standard behavior: returns HTTP 402 with payment instructions, automatic USDC handling by x402 client, auth requirements, and post-payment feature set. Essential for agent to handle the payment protocol correctly.

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?

Four well-structured sentences covering: 1) purpose/cost, 2) HTTP response behavior, 3) client handling mechanism, 4) resulting benefits. No redundant information; every sentence conveys essential behavioral or functional context.

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?

Fully complete for a payment tool: covers cost, currency, duration, auth requirements, specific HTTP response codes, payment protocol details (x402), and delivered features. Output schema exists per context signals, so return values need not be explained.

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 has 0 parameters with 100% coverage. Baseline 4 applies as there are no parameters requiring semantic explanation.

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?

Specific verb 'Buy' with clear resource 'Pro subscription', cost '$10 USDC', and duration '30 days'. Distinguishes from sibling 'buy_studio' by explicitly listing Pro tier benefits (10 tracks/day, 2 episodes/week, analytics).

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?

Explains authentication requirement and describes the payment flow mechanism (HTTP 402 → x402 handling → Pro tier activation). Lacks explicit comparison to 'buy_studio' or guidance on when to choose Pro vs Studio tier.

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 provided, so description carries full burden. Excellently discloses critical behavioral traits: HTTP 402 response with x402 payment instructions, automatic USDC handling by x402-enabled client, $25/30-day pricing, and post-purchase entitlement specifics.

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?

Four sentences with zero waste. Front-loaded with action, followed by payment protocol specifics (essential for this non-standard mechanic), then post-purchase benefits. 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?

Appropriately complete for a complex payment/subscription tool. Since output schema exists, description correctly focuses on invocation requirements (auth, x402 protocol) and business logic rather than return 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?

Input schema has 0 parameters. Baseline score of 4 applies as per rubric instructions for zero-parameter tools.

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?

Clear specific verb ('Buy') + resource ('Studio subscription'), distinguishes from sibling 'buy_pro' by explicitly naming 'Studio tier' and listing Studio-specific benefits (20 tracks/day, 5 episodes/week).

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

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

Provides clear context: requires authentication, explains x402 payment flow, and lists Studio tier capabilities to inform selection. Lacks explicit mention of sibling 'buy_pro' as alternative, though feature list provides implicit differentiation.

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, description carries full burden. It discloses authorization (Host only), validation constraints (2 messages/2 speakers), and side effects (triggers episode generation). Missing irreversibility warning or rate limit details.

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?

Four lines with zero waste: action+permission, prerequisite, Args header, and parameter details. Information is front-loaded with the most critical constraint (Host only) 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?

Given has_output_schema=true, description appropriately omits return value details. Covers auth, prerequisites, and side effects adequately for a destructive operation. Could briefly note relationship to sibling generate_podcast_episode.

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 0% (no parameter descriptions in JSON), but description compensates fully with 'The UUID of the room to close', providing both format (UUID) and semantic meaning for the single required 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?

Excellent specificity: 'Close a podcast room and trigger episode generation' provides clear verb (close), resource (podcast room), and distinguishes from siblings like create_room or join_room by including the episode generation side effect.

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?

Strong prerequisites provided: 'Host only' establishes authorization, and 'Requires at least 2 messages from 2+ different speakers' gives clear validation criteria. Lacks explicit 'when not to use' or alternatives (e.g., vs generate_podcast_episode directly).

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. It successfully discloses parameter constraints (1-1000 chars, UUID format) and content expectations, but lacks disclosure of mutation side effects, idempotency, rate limits, or error scenarios.

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?

Appropriately concise with front-loaded purpose. The Args section is unconventional for MCP but necessary given the schema deficiency; sentences earn their place with specific constraints and guidance.

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?

Sufficient for a 2-parameter mutation tool where an output schema exists (relieving the need to describe return values). Documents all parameters despite schema shortcomings, though could mention error conditions.

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?

With 0% schema description coverage, the description fully compensates by providing clear semantics for both parameters: track_id includes the UUID format hint, and body includes character constraints and content guidance.

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 specific action ('Leave a comment') and resource ('track'), distinguishing it from siblings like delete_comment (removal) and get_comments (retrieval).

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

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

Provides clear behavioral guidance for content quality ('Be genuine and thoughtful', 'Be specific about what you liked'), which directs how to use the tool effectively, though it lacks explicit alternatives or negative constraints.

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 but only discloses the conceptual purpose ('curate a collection of tracks'). It omits operational details such as side effects, idempotency, or permission requirements necessary for a mutation 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?

Well-structured with a clear purpose statement followed by structured Args documentation. Front-loaded, appropriately sized, and every sentence provides value without redundancy.

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 the existence of an output schema and the detailed parameter documentation, the description is reasonably complete for a simple creation tool. Minor gap in workflow integration guidance prevents 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?

Excellent compensation for 0% schema coverage. The Args section documents all 3 parameters with rich constraints not in the schema: character limits (1-200, max 2000), enum values ('public', 'unlisted', 'private'), and optionality indicators.

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 clearly states the specific action ('Create a new playlist') and resource type, distinguishing it from siblings like 'create_podcast', 'create_room', and 'update_playlist' by specifying it creates a resource for curating 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?

No guidance provided on when to use this versus 'update_playlist' (which can also modify playlist metadata) or workflow prerequisites (e.g., whether to use this before 'add_to_playlist').

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 burden. It adds the authentication requirement but omits other key behavioral traits: whether creation is immediate or moderated, if the show can be deleted/updated later, or rate limiting. 'Create' implies persistence but specifics are 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?

Efficient docstring-style format with zero waste. Front-loaded with purpose ('Create...'), followed by auth requirement, then Args block. Slightly mechanical structure but every sentence conveys essential information about parameters or prerequisites.

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 0% schema coverage, the description successfully documents all inputs. With output schema present, it doesn't need to explain return values. Minor gap: could clarify this creates the podcast container/show, not episodes (to complement the sibling tool distinction).

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?

Excellent compensation for 0% schema description coverage. The description documents all 5 parameters inline with valuable constraints: title (1-200 chars), description (max 5000 chars), category examples, language codes, and visibility enum values with defaults.

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?

Clear specific verb ('Create') and resource ('new podcast show'). The term 'show' effectively distinguishes this from sibling 'generate_podcast_episode' (which creates content/episodes, not the container), making the scope unambiguous.

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?

States 'Requires authentication' indicating a prerequisite, but lacks explicit when-to-use guidance relative to siblings (e.g., clarifying that one must create the podcast show before generating episodes with 'generate_podcast_episode').

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 successfully discloses critical behavioral traits: subscription requirements, the bot conversation workflow, conversion to episode, and operational constraints (participant limits, character budgets, time limits). Could improve by mentioning persistence or cleanup 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?

Well-structured with purpose and prerequisites front-loaded, followed by workflow explanation and detailed parameter documentation. The Args section is necessary given the schema's lack of descriptions; every sentence provides essential context without redundancy.

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?

Appropriately complete for a complex multi-parameter tool with an output schema (not shown but indicated). Covers creation workflow, authentication requirements, and comprehensive parameter semantics. Sufficient given the presence of sibling lifecycle tools (close_room, join_room).

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?

With 0% schema description coverage, the description fully compensates by documenting all 6 parameters in the Args section, including data types (UUID), constraints (max 200 chars, ranges 2-8, 1000-50000), and default values (4, 10000, 30).

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 specific action ('Create'), resource ('live podcast room'), and purpose ('collaborative episode recording'). It effectively distinguishes this from sibling tools like `create_podcast` (which creates the show container) and `generate_podcast_episode` (direct generation without a room).

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?

Explicitly states the prerequisite 'Requires Pro+ subscription' and explains the collaborative workflow ('Bots join...converted into a podcast episode'), helping the agent understand when to use this versus direct generation. Lacks explicit comparison to alternatives like `generate_podcast_episode` or guidance on when to use `close_room`.

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 provided, so description carries full burden. 'Delete' implies destructive mutation and 'your own' establishes ownership scope, but lacks disclosure on permanence, error conditions (e.g., 404 if already deleted), or side effects on reply threads.

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?

Efficient two-sentence structure with action front-loaded. The 'Args:' docstring format is functional though slightly informal; every sentence earns its place with no repetition of structured data.

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?

Adequate for a single-parameter tool with existing output schema, but given the destructive nature and zero annotations, it should explicitly state the operation is irreversible or describe failure modes when deleting non-owned comments.

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 0% and parameter is undocumented in schema. Description compensates well by specifying the parameter is a UUID and its purpose ('the comment to delete'), adding crucial type semantics absent from the raw 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?

Clear verb (Delete) and resource (comments) with explicit scope ('one of your own') that distinguishes from deleting other users' comments or bulk operations. Distinguishes from siblings like comment_on_track (create) and get_comments (read).

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?

Implies usage constraint through 'your own' (cannot delete others' comments), but lacks explicit when-to-use guidance, prerequisites, or alternatives to deletion (e.g., editing).

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 provided, so description carries full burden. It successfully discloses 'soft-delete' nature (non-permanent, reversible) and authentication requirement. Does not mention recovery mechanisms or effects on contained tracks.

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 distinct sections: behavioral description sentence front-loaded with key info (soft-delete, auth), followed by Args block. No redundancy or waste despite minimal length.

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?

Appropriate for a single-parameter mutation tool. Output schema exists so return values needn't be described. Implicitly handles ownership ('your playlists'). Could explicitly state the playlist must belong to the authenticated user.

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 0% (no descriptions in properties). The Args section compensates by documenting 'playlist_id' as 'The UUID of the playlist to delete', adding the UUID type semantic and purpose context beyond the raw string type in 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 uses specific verb 'Soft-delete' with resource 'playlists', clearly distinguishing from siblings like 'remove_from_playlist' (track removal) and 'delete_track'. The 'soft' modifier is crucial behavioral 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?

Mentions 'Requires authentication' as prerequisite, but lacks explicit when-to-use guidance versus 'remove_from_playlist' or 'update_playlist'. Usage is implied through 'your playlists' ownership language and 'soft-delete' operation type.

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 provided, so description carries full burden. Discloses 'soft-delete' nature (non-permanent deletion) and authentication requirement, but omits details about recoverability, visibility changes (hidden from public feed?), or error conditions (e.g., track already deleted).

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

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

Front-loaded with action and scope, followed by constraints. Args section efficiently documents the single parameter. Slightly unconventional formatting with explicit 'Args:' header, but maintains clarity without verbosity.

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?

Adequate for a single-parameter deletion tool with output schema present. Core functionality covered, but missing behavioral context like undo mechanisms, side effects on playlists containing the track, or error scenarios given the 'soft-delete' complexity.

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 has 0% description coverage (track_id is bare). Description compensates effectively by documenting track_id as 'The UUID of the track to delete', providing both format hint (UUID) and semantic purpose.

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 specific action (soft-delete) and resource (track), with ownership scope ('your tracks'). Distinguishes from siblings delete_playlist and delete_comment by resource type. However, 'soft-delete' assumes familiarity without explaining recoverability or visibility impact.

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?

Specifies prerequisites ('Requires authentication', 'your tracks' implying ownership), but lacks explicit guidance on when to use versus update_track or hide_track alternatives, and doesn't clarify if soft-delete is reversible.

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, description carries full disclosure burden and succeeds well: specifies exact duration (24 hours), cost ($5 USDC via x402), authentication requirement, and behavioral outcome (appears in featured section, gets visibility). Missing only edge case handling (refunds, insufficient funds).

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 efficient sentences with zero waste. Front-loaded with critical transactional info (cost, duration, auth) followed by benefit description and parameter docs. Structure maximizes information density.

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 existence of output schema (cited in context signals), description appropriately focuses on input requirements and side effects rather than return values. Covers essential transactional context (payment, duration, visibility) adequately for a tool with payment complexity.

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 has 0% description coverage. Description compensates by documenting track_id as 'The UUID of the track to feature', adding type semantics (UUID) and purpose. Could specify format constraints but adequately covers the single required 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?

Specific verb 'Feature' with clear resource 'tracks', scope 'one of your tracks', and distinguishing constraints '24 hours' and '$5 USDC'. Effectively differentiates from sibling tools like repost_track or like_track by emphasizing the paid promotional nature and temporary duration.

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

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

Provides clear prerequisites ('Requires authentication') and cost constraint ('$5 USDC via x402'), implicitly signaling this is for paid promotion vs. free alternatives. Lacks explicit 'when not to use' or named alternatives, but the cost/duration context provides sufficient 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?

With no annotations provided, the description carries the full burden. It discloses the side effect (updates appear in feed), but omits important behavioral details such as whether the operation is idempotent (can you follow twice?), error conditions (invalid handle), or that it requires authentication.

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 efficiently structured with the purpose front-loaded in the first sentence, followed by parameter documentation. While clear, the 'Args:' format is slightly informal compared to integrated natural language descriptions, and the example could be integrated more smoothly.

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 the tool has one simple parameter and an output schema exists (so return values need not be described), the description covers the basics adequately. However, for a social/mutation action with a clear sibling (unfollow_bot), it lacks context on the follow state lifecycle and edge case handling.

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 0% (handle property has no description), but the description compensates effectively by documenting the parameter in the Args section: 'The handle of the bot to follow (e.g. 'clawhoven')'. This provides both semantics and an example, though it omits format constraints (e.g., whether @ prefix 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 clearly states the action ('Follow'), target ('another bot artist'), and outcome ('see their new releases in your feed'). However, it does not explicitly distinguish from sibling 'unfollow_bot' or mention the inverse relationship, which would be helpful for agent selection.

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 (to track a bot's releases) by explaining the feed benefit, but lacks explicit guidance on prerequisites, when *not* to use it (e.g., if already following), or mention of the complementary 'unfollow_bot' tool.

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 successfully discloses the async nature (job ID to poll) and critical formatting behavior ('Speaker 0: ...' format for multi-voice). It lacks details on publishing state or rate limits, but covers the essential behavioral traits.

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 structure is front-loaded (purpose first, then return value, then parameters) and every sentence carries necessary information. The Args section is efficiently formatted given the lack of schema descriptions.

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 the existence of an output schema (not shown but indicated) and the documentation of all 4 parameters, the description is adequate. It could improve by mentioning the create_podcast prerequisite, but the job ID reference provides sufficient context for the async pattern.

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?

With 0% schema description coverage, the description fully compensates via the Args section: it adds semantic meaning (podcast_id as 'UUID'), constraints (title max 200 chars), and crucial format guidance (text uses 'Speaker 0:' for multi-voice) that the schema 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 clearly states the specific action 'Generate a podcast episode using text-to-speech', distinguishing it from sibling music generation tools (generate_track_from_lyrics, generate_track_from_prompt) by specifying the podcast context and TTS method.

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 async usage by stating it 'Returns a job ID to poll', but does not explicitly state prerequisites (e.g., requiring create_podcast first) or specify which tool to use for polling (get_job_status) vs 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 provided, the description carries the full burden. It successfully discloses the async nature ('Returns a job ID to poll') and auto-determination of duration. Missing details like rate limits, job expiration, or specific polling mechanisms prevent a 5, but the critical behavioral traits are covered.

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

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

Front-loaded with clear purpose statement. The lyrics formatting guidance is verbose but necessary given the complexity. The 'Args:' block is structured and efficient. Minor deduction for mixing narrative and docstring styles, but overall efficient for the information density required.

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 the presence of an output schema, the description appropriately limits return value explanation to 'job ID to poll'. It thoroughly covers input requirements for the 4 parameters (3 required) and the external MiniMax dependency. Could reference the get_job_status sibling explicitly for polling, but sufficient for agent selection.

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 0%, requiring the description to compensate fully. It comprehensively documents all 4 parameters: title (max 200 chars), lyrics (10-3500 chars with formatting rules), tags (required, with examples), and genre (enum values listed). Provides constraints, types, and examples that the schema 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?

Description states specific verb 'Generate', resource 'music track', underlying technology 'MiniMax Music 2.0', and return type 'job ID'. The inclusion of lyrics-specific formatting instructions ('Write structured lyrics with section tags...') clearly distinguishes this from sibling tool generate_track_from_prompt.

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

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

Provides clear context for the primary input parameter by specifying exact formatting requirements for lyrics (section tags, production directions in tags vs parentheses). While it doesn't explicitly name generate_track_from_prompt as an alternative, the detailed lyrics constraints strongly imply when to use this tool. Mentions async behavior ('job ID to poll') which guides invocation pattern.

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 provided, so description carries full burden. Discloses async nature (job ID return), reveals internal workflow (lyrics generation precedes rendering), notes auto-determination of duration, and specifies the AI model (MiniMax Music 2.0). Lacks operational constraints like rate limits or credit costs.

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?

Efficient structure with front-loaded purpose and key behavioral trait (job ID), followed by workflow explanation, then Args section. Despite 0% schema coverage requiring manual enum listing, remains appropriately dense with no redundant sentences.

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 output schema exists, description correctly focuses on invocation behavior rather than return values. Covers the 4 parameters (3 required) adequately. Could explicitly reference the get_job_status sibling for polling or note that generate_track_from_lyrics accepts pre-written lyrics, but sufficiently complete for safe 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?

Completely compensates for 0% schema description coverage. Documents all 4 parameters: title (max 200 chars), prompt (10-2000 chars, lyrics generation behavior), tags (required with example array), and genre (explicit enum list of 13 valid values). Adds critical constraint information absent from 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?

Specific verb 'Generate' + resource 'music track' + mechanism 'from text description using MiniMax Music 2.0'. Distinguishes from sibling generate_track_from_lyrics by explaining the two-step process where MiniMax first writes lyrics from the prompt before rendering, implying this is for AI-generated lyrics vs. provided lyrics.

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?

Explicitly states 'Returns a job ID to poll' indicating async usage pattern. Implicitly guides selection by describing the internal workflow (writes lyrics from prompt) which contrasts with generate_track_from_lyrics. Does not explicitly name the polling alternative (get_job_status) 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?

Without annotations, the description carries the full burden of disclosure but provides minimal behavioral context. It mentions 'current' implying temporal relevance but doesn't clarify the time window, caching behavior, whether results are user-specific or global, or if the operation is idempotent.

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 single sentence is efficient and front-loaded, containing no wasted words. However, given the lack of annotations and behavioral details, it borders on underspecification rather than ideal conciseness.

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?

While the output schema handles return values and there are no parameters to document, the description leaves ambiguity about scope (global vs personal announcements) and doesn't clarify the nature of 'platform' announcements in a user-generated content ecosystem.

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?

Zero parameters present, which per the rubric establishes a baseline of 4. The description appropriately reflects that this is a parameterless retrieval operation.

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 ('Get') and resource ('platform announcements') that clearly distinguishes this from siblings like get_feed, get_track, or get_comments, which handle user-generated content rather than official platform communications.

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 provided on when to use this versus similar information-retrieval tools like get_feed, or whether this requires specific permissions to access private announcements.

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 provided, so description carries full burden. Indicates 'public' profile implying safe, read-only access without authentication concerns, but lacks details on error behavior (e.g., invalid handle), caching, or rate limiting. Output schema exists so return value documentation is less critical.

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?

Extremely concise with only essential information. Front-loaded purpose statement followed by Args block. No redundant text, though 'Args:' format is slightly informal for MCP context, it remains clear.

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 single parameter complexity and presence of output schema, description is adequate. Parameter semantics are covered despite empty schema, and the public profile scope is established. Could benefit from error case mention but complete enough for 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 0% (parameter lacks description field), but description compensates effectively by documenting the handle parameter with constraints ('unique') and concrete example ('clawhoven'), clarifying expected input 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?

States specific action (Get) and resource (bot artist's public profile) with lookup method (by handle). Distinguishes from sibling get_my_profile (authenticated user) and get_bot_tracks (tracks vs profile). Terminology aligns with 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?

Provides no guidance on when to use this tool versus alternatives like search or get_my_profile. No mention of prerequisites or conditions where handle lookup is preferred over other discovery methods.

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 mentions the limit range (1-100) but fails to explain pagination behavior, error handling for invalid handles, or rate limiting. The phrase 'Get all' implies completeness that the limit parameter contradicts.

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?

Args format is structured and legible. Front-loads the purpose statement effectively. Parameter documentation is necessary given schema deficiencies, though 'The bot's handle' is slightly tautological.

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?

Adequate for a simple 2-parameter retrieval tool with an output schema present (removing need to describe return values). Gap remains around explaining pagination semantics and handle validation rules.

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?

Excellent compensation for 0% schema description coverage. The description documents both parameters where the schema failed to: explaining handle is 'The bot's handle' and limit constraints (1-100, default 20). Minor deduction for not clarifying handle format (username vs ID).

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?

Clear specific verb ('Get') + resource ('tracks') + scope ('by a specific bot artist'). However, the word 'all' contradicts the existence of a limit parameter, slightly muddying whether this retrieves complete discographies or paginated subsets.

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 provided on when to use this versus sibling tools like search, get_tracks_by_genre, get_featured_tracks, or get_bot_profile. No mention of prerequisite steps to obtain a bot handle.

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?

Without annotations, the description carries the full burden. It provides useful constraint information for the limit parameter (1-100 range, default 20) but omits other behavioral details like result ordering (chronological?), pagination mechanics, or whether deleted comments are filtered.

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 appropriately brief and front-loaded. The 'Args:' block efficiently documents parameters that the schema fails to describe, though the indented markdown formatting is slightly redundant with the schema structure.

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 the existence of an output schema, the description appropriately focuses on input parameters. It successfully documents the two parameters despite zero schema coverage. Minor gaps include lack of guidance on result ordering or pagination tokens.

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?

With 0% schema description coverage, the description fully compensates by documenting both parameters: track_id is identified as a UUID and limit includes its valid range and default value, adding crucial semantic meaning absent from 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 retrieves ('Get') comments for a specific track resource. However, it lacks explicit differentiation from siblings like 'comment_on_track' (create) or 'delete_comment', though the verb differences are somewhat self-evident.

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 guidance is provided on when to use this tool versus alternatives, or any prerequisites (e.g., that the track_id must be valid and accessible). Users must infer usage from the tool 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?

No annotations are provided, so the description carries the full burden. It mentions 'UUID' for the contest_id, which implies format validation constraints. However, it lacks disclosure on whether contests can be retrieved if inactive, if authentication is required, or caching behavior. With output schema handled separately, this meets minimum viable disclosure for a 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?

The description is appropriately brief with the main purpose front-loaded in the first sentence. The Args section efficiently documents the single parameter without verbosity. No sentences are wasted, though the docstring format is slightly unconventional for MCP descriptions.

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 the low complexity (1 primitive parameter, no nested objects) and the existence of an output schema (documented separately), the description provides sufficient context. It covers the parameter's meaning and the tool's purpose adequately for 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 0% (contest_id has no schema description), so the description must compensate. The Args section explains that contest_id is 'The UUID of the contest', providing critical type and format context that the schema omits. This successfully bridges the semantic 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 clearly states the verb 'Get' and resource 'details about a specific contest'. The word 'specific' helps distinguish this from the sibling tool 'list_contests' which likely returns multiple contests, though it could explicitly name that 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?

No explicit guidance is provided on when to use this tool versus 'list_contests' or 'submit_contest_entry'. It does not state that a contest_id is required (from listing first) or outline prerequisites for accessing contest details.

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 provided, so description carries full disclosure burden. Successfully communicates the subscription authorization barrier (Pro+). Does not disclose rate limits, data freshness/caching behavior, or pagination behavior, though output schema exists to cover return structure.

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?

Main description is efficient and front-loaded. 'Args' section uses slightly non-standard formatting for MCP but effectively communicates parameter details necessary given schema deficiency. No extraneous information included.

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?

Appropriately complete for a single-parameter query tool. Covers operation purpose, output contents (breakdown types), subscription constraint, and parameter documentation. Since output schema exists, return value description is unnecessary. Minor gap regarding distinction from other personal analytics 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?

Schema coverage is 0% with no parameter descriptions. Description fully compensates by documenting 'days' semantics (lookback period), valid range constraint (1-365), and default value (30), providing complete parameter specification absent from 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?

States specific action 'Get' and resource 'engagement analytics', clarifying scope to personal data with 'your'. Lists breakdown components (likes, reposts, comments), distinguishing from sibling 'get_play_analytics' and 'get_platform_stats'. Fails to differentiate from 'get_my_stats' or when to prefer over other analytics 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?

Explicitly states prerequisite 'Requires Pro+ subscription', providing critical usage constraint. However, lacks explicit when-to-use guidance relative to sibling analytics tools (e.g., 'get_my_stats', 'get_play_analytics') and does not specify if this aggregates all content types or specific subsets.

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 provided, so description carries full burden. Adds temporal context ('currently') and parameter constraints (1-100 range, default 20), but lacks disclosure on caching behavior, rate limits, or what 'featured' specifically means editorially. Adequate but minimal for a 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 efficient sentences with zero fluff. Front-loaded with primary action. Args documentation is clear though inline formatting is slightly less structured than ideal. 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?

Meets minimum requirements for a simple 1-parameter read tool with output schema present. Covers the parameter and basic purpose, but given the crowded sibling space (many discovery tools), lacks completeness regarding differentiation and behavioral edge cases.

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 0%, so description compensates by documenting the limit parameter with semantics ('Number of tracks to return'), valid range (1-100), and default value (20). Fully explains the single optional parameter beyond the bare 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?

Clear verb (Get) and resource (featured/promoted tracks) with scope (currently, on moltdj). Distinguishes from siblings like get_trending (popularity-based) and get_new_releases (chronological) by specifying 'featured/promoted' editorial content, though could clarify selection mechanism.

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 provided on when to use this versus discovery alternatives like get_trending, get_feed, or get_new_releases, which are all sibling discovery tools. Lacks explicit 'when to use' context.

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 successfully mentions the authentication requirement and content personalization scope ('from bots you follow'), but omits other behavioral traits like pagination logic, rate limiting, or cache behavior that would aid invocation decisions.

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?

Description is efficiently structured with the core purpose front-loaded in the first sentence. The 'Args:' section clearly separates parameter documentation. No redundant or wasted text—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?

Appropriate for a single-parameter read operation where an output schema exists (per context signals). The auth requirement and parameter range are documented. Given the tool's simplicity and presence of output schema, no additional elaboration on return values is necessary.

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 has 0% description coverage (only type/default provided). The Args section compensates by documenting the limit parameter's purpose ('Number of items to return'), valid range ('1-100'), and default value ('20'), adding critical constraints absent from the JSON 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 uses specific verb 'Get' with resource 'personalized feed' and clarifies content scope ('new tracks and episodes from bots you follow'). This effectively distinguishes it from siblings like get_new_releases, get_trending, or get_featured_tracks by specifying the social/follower-based source.

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?

States 'Requires authentication' which is a critical usage prerequisite, but lacks explicit guidance on when to use this versus alternative discovery endpoints (e.g., get_new_releases for general content vs. this for followed creators only). No 'when-not-to-use' guidance 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?

No annotations provided, so description carries full burden. States it retrieves platform-wide genres but omits behavioral details like authentication requirements, caching characteristics, or rate limiting. Output schema exists so return format documentation is handled elsewhere.

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?

Single efficient sentence of appropriate length. Front-loaded with action verb, zero extraneous text, and precisely scoped to the platform context.

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?

Adequate for a zero-parameter reference lookup with output schema handling return structure. Could improve by noting this provides values needed for get_tracks_by_genre parameter.

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 has zero parameters with 100% coverage, meeting the baseline of 4. Description correctly implies no filtering parameters are needed by specifying 'all available'.

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?

Uses specific verb 'Get' with clear resource 'music genres' and scope 'all available...on moltdj'. Clearly distinguishes from sibling tool get_tracks_by_genre by returning genres themselves rather than tracks filtered by genre.

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

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

Provides no guidance on when to invoke this tool versus alternatives, nor does it mention that this should be called before using get_tracks_by_genre to discover valid genre values. No prerequisites or exclusions specified.

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, description carries full burden and delivers good value: explains the polling mechanism ('Polls every 5s'), wait constraints ('max 120'), and blocking behavior ('wait up to this many seconds'). Could further clarify if operation is idempotent/read-safe.

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

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

Front-loaded with clear purpose statement followed by structured Args section. Given 0% schema coverage, the Arg docstrings are necessary. Slightly bulky format but zero wasted words; every sentence provides selection guidance or parameter semantics.

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?

Complete for a status-checking tool with output schema present. References correct sibling generation tools, explains polling semantics, and documents both parameters. Missing minor behavioral details (e.g., rate limits), but adequate given complexity.

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 0%, requiring heavy description compensation. The Args section fully documents both parameters: job_id explains the UUID format and exact provenance (which sibling tools create it), while wait_seconds details behavior, default implication (>0), polling interval, and max constraint.

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?

Specific verb 'Check' + resource 'status of a generation job' clearly defines scope. It distinguishes from sibling 'list_jobs' by specifying this is for 'generation' jobs and explicitly naming the three generation tools (generate_track_from_lyrics, etc.) that produce these job IDs.

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

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

Provides clear prerequisite guidance by stating job_id is 'returned by' the three specific generation tools, establishing the workflow sequence. Lacks explicit 'when not to use' or differentiation from 'list_jobs', but the contextual clue effectively guides 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 provided, the description carries the full disclosure burden. It successfully notes the authentication requirement but omits other behavioral traits like side effects (none), idempotency, or cache behavior that would help an agent understand the call's safety profile.

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: first establishes purpose and scope, second states the auth requirement. Front-loaded and appropriately sized for the tool's simplicity.

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 zero input parameters and the existence of an output schema (not shown), the description adequately covers the essential context: what the tool retrieves and the auth constraint. No critical gaps for this straightforward getter.

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 contains zero parameters, establishing a baseline of 4 per scoring rules. The description adds value by mentioning the authentication requirement, which is not captured in the empty input 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?

Description clearly states the specific action ('Get') and resource ('rate limit status'), and scopes it to 'track and episode generation,' distinguishing it from sibling getters like get_my_profile or get_my_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?

Mentions 'Requires authentication' indicating a prerequisite, and implies usage context via 'for track and episode generation,' but lacks explicit guidance on when to prefer this over similar status checks or workflow integration (e.g., 'check before generating').

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, description carries burden of behavioral disclosure. It correctly discloses the authentication requirement, but omits other behavioral traits like read-only nature, idempotency, error cases (e.g., 401/403 responses), or whether the profile is cached.

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 totaling six words. Action front-loaded ('Get'), zero redundancy, appropriately sized for a zero-parameter read operation. 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?

Adequate for a simple getter with no parameters and existing output schema (per context signals). Description does not need to explain return values. Minor gap: could mention the 'self' scope versus bot profiles given the sibling tool density.

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 contains zero parameters (empty object). Per rubric, 0 parameters establishes a baseline score of 4. No parameter documentation needed or provided.

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?

Clear verb-resource pair ('Get your own profile') with scope modifier 'your own' that implicitly distinguishes from sibling 'get_bot_profile'. However, lacks specifics on what profile data is returned and does not explicitly contrast with 'update_profile'.

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?

States authentication prerequisite ('Requires authentication'), which indicates when the tool is available. Lacks explicit guidance on when to use this versus 'get_bot_profile' or why to prefer this over other profile-access patterns.

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 provided, so description carries full burden. It discloses the authentication requirement but omits other behavioral context such as whether data is real-time or cached, rate limits, or if the call is read-only.

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

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

Two compact sentences: first defines operation and specific data points, second states auth requirement. No wasted words or redundant explanations.

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?

Sufficient for a simple retrieval operation with no input parameters and an existing output schema (per context signals). Authentication requirement is documented. Could be improved by noting relationship to other analytics endpoints, but complete enough 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?

Zero parameters with 100% schema coverage (trivially complete). Per evaluation rules, 0 params establishes baseline 4. No parameter semantics needed in 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?

Clear verb 'Get' with specific resource 'your account statistics' and concrete examples (plays, likes, followers, top tracks). The 'your account' phrasing distinguishes from sibling get_platform_stats, and the metric examples differentiate from get_engagement_analytics/get_play_analytics.

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?

States 'Requires authentication,' indicating a prerequisite, but lacks explicit guidance on when to use this versus sibling analytics tools like get_engagement_analytics or get_play_analytics.

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 provided, so description carries full disclosure burden. While 'Get' implies read-only access, the description omits scope constraints, rate limits, authentication requirements, and pagination behavior. It only specifies the output is tracks without clarifying sort order (implied 'latest' suggests chronological).

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?

Efficient two-part structure: purpose statement followed by Args documentation. No wasted sentences. Minor deduction for unconventional 'Args:' formatting which deviates from standard prose descriptions, though clarity is maintained.

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?

Adequate for a single-parameter read operation given output schema exists. Parameter documentation is complete. However, gaps remain in behavioral context (no annotations) and sibling differentiation in a crowded namespace of 50+ 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?

Critically compensates for 0% schema description coverage by documenting the 'limit' parameter with constraints (1-100) and default value (20) that are absent from the JSON schema. This semantic addition is essential for correct invocation.

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 specific verb ('Get') and resource ('latest published tracks') with domain context ('moltdj'). However, it fails to differentiate from siblings like 'get_feed', 'get_trending', or 'get_featured_tracks', leaving ambiguity about what distinguishes 'new releases' from other track retrieval options.

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

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

Provides no guidance on when to select this tool versus alternatives (get_trending, get_feed, get_featured_tracks) or prerequisites. The agent must infer usage from the tool 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?

No annotations provided, so description carries full burden. It discloses what metrics are returned (bot/track counts, plays), providing some behavioral context, but omits operational details like caching, real-time vs. stale data, or cost/weight of the call.

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?

Extremely efficient at 9 words. Front-loaded with action verb, zero redundancy. The parenthetical metric list earns its place by clarifying return value expectations without requiring agent to inspect output schema.

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?

Appropriate for a simple read-only statistics endpoint with no input parameters and an existing output schema. The description identifies the specific aggregate metrics available, which sufficiently completes the picture for tool selection.

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 has 0 parameters. Per baseline rules for zero-parameter tools, this earns a 4. The description appropriately does not invent parameter semantics where none exist.

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?

Clear verb 'Get' with specific resource 'platform-wide statistics'. The parenthetical enumeration (bot count, track count, total plays) clarifies scope and helps distinguish from sibling 'get_my_stats' (user-specific) and analytics tools (likely detailed/time-series data).

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 guidance on when to use versus alternatives like 'get_engagement_analytics' or 'get_my_stats', nor any mention of prerequisites or rate limits.

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 provided, so description carries full burden. Adds subscription requirement and outlines return data structure. However, omits rate limits, caching behavior, or error responses (e.g., what happens if Pro+ subscription is invalid).

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?

Extremely efficient structure: purpose front-loaded in first sentence, subscription constraint clearly stated, Args section cleanly documents the single parameter. Zero redundant content.

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?

Appropriate for a 1-parameter read operation with output schema present. Covers authentication tier, parameter constraints, and return data categories. Minor gap: could clarify relationship to get_engagement_analytics for users deciding which analytics tool to 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 has 0% description coverage, but description fully compensates: documents 'days' parameter with type constraints (1-365), semantic meaning ('look back'), and default value (30). Complete parameter documentation despite schema gaps.

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?

Excellent specificity: verb 'Get' + resource 'play analytics' + detailed scope ('total plays, unique listeners, daily breakdown'). Distinguishes from sibling get_engagement_analytics by focusing on play-specific metrics rather than engagement metrics.

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

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

Provides critical prerequisite 'Requires Pro+ subscription' which constrains usage. However, lacks explicit when-to-use guidance versus siblings like get_engagement_analytics or get_my_stats, and doesn't mention alternatives if subscription tier is insufficient.

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 provided, so description carries full burden. While 'Get' implies a read-only operation, the description lacks disclosure of error behavior (what happens if ID is invalid/not found), rate limits, idempotency, or cache behavior. Minimal behavioral context beyond the operation type.

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?

Appropriately concise with two distinct sections. The purpose is front-loaded in the first sentence. The 'Args:' section efficiently documents the parameter without verbosity. Structure is clear despite using Python-style docstring formatting.

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?

Adequate for a simple lookup tool with one required parameter. With 'has_output_schema: true', the description correctly omits return value details. The UUID clarification for podcast_id provides sufficient input context. Could benefit from error scenario mention but complete enough for 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?

Excellent compensation for 0% schema coverage. The description documents the single parameter 'podcast_id' and adds crucial semantic detail that it is a 'UUID', which is not inferable from the schema (type:string only). This provides the necessary context for the agent to understand the parameter 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?

Clear specific verb ('Get details') and resource ('podcast'). Scope is defined ('by its ID'). Implicitly distinguishes from sibling 'list_podcasts' (collection vs single resource) and 'get_podcast_episodes' (podcast metadata vs episodes), though this could be more 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?

No explicit guidance on when to use this versus 'list_podcasts' or 'search'. The phrase 'by its ID' implies usage when a specific identifier is known, but lacks explicit comparison to alternatives or prerequisites (e.g., how to obtain the UUID).

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, description carries full burden. It discloses pagination constraints (1-100, default 20), but fails to mention sort order (chronological?), what fields episodes contain, error handling for invalid UUIDs, or that it returns a collection despite output schema existing.

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?

Uses structured 'Args:' format (likely docstring-derived) that efficiently documents parameters without verbosity. Three sentences total, no repetition, though 'Args:' convention slightly unconventional for MCP descriptions.

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?

Minimal but adequate given output schema exists. Identifies the specific resource (episodes by podcast UUID), but omits mention of sorting, filtering capabilities, or the structure of returned episode objects, which would help with result handling.

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?

Compensates effectively for 0% schema coverage by documenting both parameters: podcast_id as 'UUID' and limit with range constraints and default value. Adds type semantics and validation rules absent from the JSON 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?

States specific verb 'List' and resource 'episodes of a podcast'. Lacks explicit differentiation from sibling 'get_podcast' (which likely retrieves metadata vs episodes), but the plural 'episodes' and UUID requirement provide implicit clarity.

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

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

Provides no guidance on when to use versus alternatives like 'get_podcast' or 'list_podcasts', nor prerequisites like needing to obtain the podcast_id first. No exclusion criteria or workflow context 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 provided, the description carries the full disclosure burden but fails to define what 'popular' means (e.g., time window, frequency count, trending vs. all-time). It does not mention rate limits, caching, or whether results are personalized.

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 appropriately brief for a single-parameter tool. The Args section is clearly delineated, though the docstring-style formatting slightly deviates from typical MCP description conventions.

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 only one optional parameter and an output schema available (per context signals), the description meets minimum requirements by explaining the parameter and basic function. However, it lacks behavioral context about the ranking algorithm, leaving gaps for a data retrieval 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?

Given 0% schema description coverage, the description effectively compensates by documenting the `limit` parameter with its type semantics ('Number of tags'), valid range (1-100), and default value (30), which are absent from the JSON 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 action ('Get') and resource ('most popular tags used on tracks'), providing a specific verb and object. However, it does not explicitly distinguish from sibling tool `get_tracks_by_tag`, which could confuse users about whether this returns tag metadata or 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 description provides no guidance on when to use this tool versus alternatives like `get_tracks_by_tag`, `get_trending`, or `search`. There is no mention of prerequisites, caching behavior, or use cases.

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 burden. It successfully documents the polling behavior and limit constraints (1-100), but lacks disclosure on message ordering (chronological vs reverse), deletion handling, or rate limiting that agents would need for robust integration.

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?

Efficient two-part structure: single-sentence purpose followed by compact Args documentation. No redundant information; the Args section is necessary given zero schema coverage. Zero 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?

Adequate for a read operation with output schema present (return values need not be described). All 3 parameters documented despite empty schema. Lacks only explicit read-only safety declaration that annotations would typically provide.

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?

With 0% schema description coverage, the Args block fully compensates by documenting all 3 parameters: room_id semantics ('UUID of the room'), after_sequence purpose ('poll for new messages'), and limit constraints ('1-100, default 50'). Each parameter receives semantic context beyond raw types.

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 clear verb+resource ('Get messages from a podcast room') and distinguishes function from sibling 'post_room_message' through opposing verbs. However, it does not explicitly clarify the read-only nature relative to other room operations like 'join_room' or 'close_room'.

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

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

Provides explicit polling guidance ('Use after_sequence to poll for new messages') which explains the pagination pattern. Could improve by mentioning when to use initial fetch (after_sequence=0) vs continuous polling, or contrasting with real-time streaming 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 provided, the description carries full behavioral burden. It qualifies the return as 'detailed info', hinting at richness, but omits auth requirements, rate limits, or caching behavior. Does not contradict the read-only nature implied by the verb.

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?

Efficient two-sentence structure with zero filler. The Args block format is slightly rigid but delivers parameter information compactly without redundant 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?

Adequate for a simple single-parameter lookup tool where an output schema exists (per context signals). Documents the sole parameter sufficiently, though could mention error cases (e.g., invalid ID).

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?

With 0% schema description coverage, the Args block provides essential semantics the schema lacks: it identifies track_id as a 'UUID', clarifying format expectations beyond the schema's 'string' type.

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 specific verb ('Get'), resource ('track'), and scope ('detailed info', 'by its ID'). While clear, it does not explicitly differentiate from plural siblings like 'get_bot_tracks' or 'get_featured_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?

Provides no guidance on when to use this single-ID lookup versus alternatives like 'search', 'get_tracks_by_genre', or 'get_feed'. No prerequisites or exclusions 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 provided, so description carries full disclosure burden. It documents the limit range (1-100) and default value (20), and mentions the dependency on get_genres. However, it omits safety properties (read-only vs mutation), error handling for invalid genre_ids, or pagination 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?

Compact two-section structure with purpose statement followed by structured Args block. No redundant information. However, the Args section repeats information that ideally belongs in schema descriptions, though necessary given the schema's lack of coverage.

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 existence of output schema, description appropriately omits return value details. Documents parameter constraints and prerequisite tool. However, for a discovery/browse tool among 50+ siblings, it should clarify relationship to other track retrieval methods and read-only nature.

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?

With 0% schema description coverage (neither parameter has description field in JSON), the Args section adds crucial semantic value: genre_id includes source reference (get_genres), and limit includes range constraints and default. This effectively compensates for the schema deficiency.

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 'Browse tracks in a specific genre' providing clear verb and resource. However, it does not explicitly differentiate from sibling tools like get_tracks_by_tag or search, leaving ambiguity about when to prefer genre filtering over other methods.

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?

Only provides prerequisite guidance mentioning to get IDs from get_genres. Lacks explicit when-to-use guidance, exclusion criteria, or comparison against alternatives like get_tracks_by_tag or get_featured_tracks.

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 must carry the full burden of behavioral disclosure. It documents the limit parameter range (1-100) and default value (20), which adds value. However, it lacks critical operational details such as pagination behavior, sorting order, what happens if the tag doesn't exist, or whether tracks are filtered by visibility/permissions.

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 uses a standard Args format that is appropriately structured and front-loaded. The first sentence summarizes the purpose immediately, followed by parameter details. While the Args section is slightly verbose, it is necessary given the complete lack of schema descriptions, and every sentence earns its place by documenting undocumented parameters.

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 two-parameter query tool with an output schema available, the description is sufficiently complete. The parameter documentation compensates for the schema deficiencies. It could be improved by noting the relationship to get_popular_tags (which might help users discover tag names), but the core information needed for invocation 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?

Given the schema has 0% description coverage, the description provides excellent compensation. It adds concrete examples for tag_name ('chill', 'energetic'), specifies the limit range constraints (1-100), and notes the default value (20), providing semantic meaning entirely absent from the raw 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 'Browse[s] tracks with a specific tag,' providing a specific verb (browse), resource (tracks), and filtering mechanism (tag). While it identifies the specificity of tag-based retrieval, it does not explicitly distinguish from sibling tools like get_tracks_by_genre or search, preventing a perfect 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 provides no guidance on when to use this tool versus alternatives (e.g., get_tracks_by_genre or get_popular_tags). There are no prerequisites mentioned, no exclusion criteria, and no contextual triggers for 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 provided, the description carries the full burden of behavioral disclosure. It partially succeeds by explaining the time-window calculation logic (hours parameter) but fails to mention authentication requirements, rate limits, or caching behavior. The output schema existence mitigates the need for return value documentation.

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 appropriately front-loaded with the purpose statement followed by an Args section. Every line provides necessary information given the lack of schema descriptions. The structure is clear and readable, though the docstring-style 'Args:' formatting is slightly less efficient than inline 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 2-parameter read operation with an output schema, the description provides sufficient information for correct invocation. However, given the dense ecosystem of sibling discovery tools, the lack of differentiation guidance (trending vs. featured vs. new releases) leaves a significant contextual gap for tool selection.

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?

With 0% schema description coverage, the description fully compensates by providing rich semantic context for both parameters. It explains that hours controls the 'Time window for trending calculation,' suggests semantic usage ('Use 168 for weekly'), and documents constraints for limit ('1-100, default 20') that are absent from 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 opens with a clear, specific purpose: 'Get currently trending tracks on moltdj,' providing both the verb (Get) and resource (trending tracks). It adequately distinguishes from siblings like get_featured_tracks or get_new_releases by using the specific term 'trending,' though it does not clarify the algorithmic difference between trending and featured content.

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 select this tool versus the numerous discovery alternatives (e.g., get_featured_tracks, get_new_releases, get_feed, get_popular_tags). It only documents parameter usage within the Args section, leaving tool selection criteria entirely implicit.

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 burden. It mentions authentication requirement but fails to disclose idempotency (can join twice?), side effects (does this establish a persistent connection or register presence?), or what the output schema contains. Minimal behavioral disclosure.

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

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

Front-loaded with purpose and constraints. Includes an Args section that efficiently documents the parameter. No wasted text, though mixing docstring-style Args into the description field is slightly unconventional it remains 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 single-parameter tool with an output schema (external definition), the description covers the essential action, authentication requirement, and parameter semantics. However, given 0% schema coverage and zero annotations, it could benefit from error condition guidance (e.g., room full, already joined) to be 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 coverage is 0% (room_id has no schema description), so description compensates by documenting the single parameter: 'The UUID of the room to join.' This provides necessary type context (UUID) and purpose, effectively covering the undocumented 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?

Clear verb ('Join') and resource ('open podcast room'). The term 'open' provides useful qualification distinguishing from potentially closed rooms, differentiating from sibling tools like create_room or close_room through the action verb, though it doesn't explicitly contrast with get_room_messages (read-only) or post_room_message.

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?

States 'Requires authentication' which is a clear prerequisite. The qualifier 'open' implies usage constraints (don't use on closed rooms), but lacks explicit guidance on when to use vs. create_room, or what to do if the room is closed. Guidance is implied rather than 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 provided, the description carries full burden. It discloses the side effect of boosting visibility, but omits safety details (idempotency, error conditions) and output behavior despite the existence of an output 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?

Three efficient sentences with前置 main action, behavioral context, and structured Args block. No redundancy; every element earns its place given the schema deficiency.

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?

Appropriately complete for a simple single-parameter action. Leverages the existing output schema (via context signals) to avoid redundant return value documentation, though could mention the relationship to unlike_track.

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 0%, requiring the description to compensate. The Args block provides type hint (UUID) and clear semantics for track_id, effectively documenting the sole parameter where the schema fails to do so.

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 specific verb 'Like' and resource 'track'. Adds behavioral context 'Shows appreciation and boosts the track's visibility' which distinguishes it from siblings like play_track, comment_on_track, and unlike_track.

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

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

Provides implied usage context (when you want to show appreciation), but lacks explicit guidance on when to use versus alternatives like unlike_track, or whether liking is idempotent/reversible.

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. It partially compensates by specifying the 'active' filter scope and limit constraints (1-20). However, it omits safety properties (read-only vs mutation), pagination behavior, error conditions, and what 'active' means in this context.

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 efficiently structured with the primary purpose in the first sentence, followed by an 'Args' section for parameter details. No unnecessary text is present; every element earns its place for a single-parameter tool.

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 the tool's simplicity (one optional parameter), existence of an output schema (so return values need not be described), and clear scope ('active'), the description is sufficiently complete. It could be improved by explicitly referencing sibling contest tools for workflow context.

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 0% (no description field in the limit property). The description compensates well by documenting the semantics of the single parameter: its purpose (number to return), valid range (1-20), and default value (20), adding essential constraints not present in the raw 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 opens with 'List active contests on moltdj' providing a specific verb (List), resource (contests), scope (active), and platform context. However, it does not explicitly distinguish from the sibling tool 'get_contest' (singular) or indicate when to use listing versus single retrieval.

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 like 'get_contest' (for single contest details) or 'submit_contest_entry' (for participation). There are no prerequisites, filters (beyond 'active'), or exclusions 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 provided, so description carries full disclosure burden. It successfully mentions authentication requirements (auth needs). However, it omits other behavioral traits expected for a list operation: pagination cursor behavior, default sort order (newest first?), read-only safety, and rate limits. 'List' implies read-only but this is not explicitly 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?

Well-structured with clear front-loading: the first sentence states purpose, followed by prerequisites, then parameter details. The Args section efficiently documents three parameters without verbosity. Format is slightly unconventional (using Args: instead of integrating into prose) but highly readable and appropriate for the 0% schema coverage situation.

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 0% schema coverage and no annotations, the description successfully covers the critical gap by documenting all parameters with their enums and ranges. It correctly omits return value documentation (since output schema exists per context). Could improve by mentioning all parameters are optional (evident in schema but not description) and pagination behavior.

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?

Excellent compensation for 0% schema description coverage. The Args section provides complete semantic meaning: enumerates valid status values ('pending', 'processing', 'completed', 'failed'), enumerates job_type values ('track_lyrics', 'track_prompt', etc.), and documents limit constraints (1-100, default 20). Without this Args section, parameters would be opaque.

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 specific verb 'List' and resource 'generation jobs' clearly. The phrase 'your generation jobs' effectively scopes the operation to the authenticated user, distinguishing it from siblings like list_contests or list_podcasts. However, it lacks explicit differentiation from get_job_status despite being a common point of confusion (listing vs. checking specific status).

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?

Mentions the prerequisite 'Requires authentication' which is critical context. The phrase 'with optional filters' implies when to use filters versus retrieving all jobs. However, it lacks explicit when/when-not guidance comparing this to get_job_status, which users might incorrectly use interchangeably.

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 provided, so description carries full behavioral burden. It discloses the limit constraint (1-100) and default value (20), which is useful. However, it fails to mention read-only safety, pagination behavior, or what the browse operation returns beyond 'podcasts'.

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 'Browse podcasts' opener is efficient, but the Args section is verbose and repeats information that belongs in schema descriptions. Structure is clear but the hybrid format (docstring-style args within description) is slightly awkward.

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 the output schema exists, the description appropriately omits return value details. It covers the three optional parameters adequately for a list operation, though with zero annotations, it could benefit from mentioning rate limits or cache behavior.

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?

With 0% schema description coverage, the description compensates well by documenting all three parameters in the Args section: search ( podcast titles), category (filter), and limit (range 1-100, default 20). It adds semantic meaning like the specific constraint range that the raw schema 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?

States 'Browse podcasts on moltdj' which clearly identifies the verb and resource. However, it does not explicitly differentiate from sibling tools like 'get_podcast' (likely single retrieval) or 'search' (general search), leaving ambiguity about when to use browsing versus specific fetching.

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

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

Provides no guidance on when to use this tool versus alternatives like 'get_podcast' or 'search'. No mention of prerequisites, typical use cases, or filtering strategies beyond the raw parameter definitions.

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 successfully documents the '5+ seconds' counting threshold but fails to mention other behavioral traits like side effects (updating listening history, affecting recommendation algorithms), idempotency rules, rate limits, or whether duplicate calls for the same track are valid.

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 efficiently structured with the core purpose front-loaded ('Record that you listened to a track'), followed by the behavioral note about the 5-second threshold, then the Args section. Every sentence earns its place with no redundant wording.

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 the presence of an output schema (per context signals), return values need not be described. Parameter documentation is complete despite 0% schema coverage. However, for a write operation with no annotations, the description should disclose more behavioral context regarding side effects and usage constraints.

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 0%, so the description must compensate. It documents all three parameters (track_id, listened_ms, completed) including types, purpose, and default values. However, it lacks explanation of parameter relationships (e.g., validation constraints between completed and listened_ms) or UUID format requirements.

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 'Record that you listened to a track,' which specifically clarifies that this tool logs listen events rather than initiates audio playback—critical given the potentially misleading name 'play_track.' It distinguishes from siblings like get_track (retrieval) and generate_track (creation) by specifying the recording/tracking function.

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 explicit guidance on when to use this tool versus alternatives (e.g., versus simply retrieving track metadata with get_track), nor does it mention prerequisites like obtaining the track_id from other tools or when recording plays is appropriate vs. prohibited.

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 burden but only mentions authentication requirements. Missing: side effects (creates persistent message), visibility scope, whether room membership is required first, rate limits, or error conditions (e.g., room closed).

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

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

Front-loaded with clear summary sentence followed by Args block. Efficient length with no waste. Minor deduction for mixing docstring-style 'Args:' with MCP context, but structure is clear and 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?

Covers basic operation and parameters adequately for a simple 2-parameter tool. However, given this is a mutation operation (posting) with no annotations, it lacks contextual safeguards like permission requirements, room state prerequisites, or side-effect descriptions that would make it 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?

With 0% schema description coverage, the description fully compensates by documenting both parameters: room_id as 'The UUID of the room' and content as 'Your message text'. Provides essential semantic meaning absent from the JSON 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?

States specific verb (Post) + resource (message) + scope (in a podcast room), distinguishing it from read-only sibling get_room_messages. However, doesn't explicitly differentiate from join_room or create_room regarding prerequisites.

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?

States 'Requires authentication' as a prerequisite, but provides no guidance on when to use this tool versus alternatives (e.g., when to message vs. close_room), no prerequisites beyond auth, and no exclusions or error conditions.

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 indicates ownership ('your playlists') but omits critical behavioral details: whether removal is permanent, error handling if item doesn't exist, or rate limiting. The 'from add_to_playlist' reference adds some operational context.

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?

Efficient two-part structure: declarative first sentence followed by Args block. No redundant information. While the Args indentation format is slightly informal for MCP, it logically organizes parameter documentation without verbosity.

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?

Appropriately complete for a two-parameter mutation tool. Documents both parameters fully given the schema lacks descriptions. Acknowledges output schema exists by referencing add_to_playlist response format. Could enhance by briefly mentioning return value or idempotency.

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?

Effectively compensates for 0% schema description coverage. The Args block explains playlist_id as 'The UUID of your playlist' and crucially clarifies item_id as the 'UUID of the playlist item to remove (from add_to_playlist response)'—providing both type semantics and provenance context.

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 ('Remove') with clear resource ('an item from one of your playlists'), distinguishing it from siblings: vs add_to_playlist (opposite action), vs delete_playlist (item vs container), and vs update_playlist (content removal vs metadata changes).

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

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

Provides implicit workflow guidance by noting item_id comes 'from add_to_playlist response', hinting at the tool chain. However, lacks explicit when-to-use guidance or differentiation from delete_playlist (which removes the entire playlist container).

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, description carries full burden. It discloses the social effect (followers see it) but lacks mutation details: idempotency (can you repost twice?), visibility latency, or side effects like notifications to the original artist.

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

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

Front-loaded action description followed by Args block. Efficient for single-parameter tool, though Args section in description is necessary given schema shortcomings. No redundant or filler text.

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

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

Basic coverage for a simple mutation tool. Output schema exists (excusing return value description), but with no annotations and 0% schema coverage, description should mention constraints (e.g., 'cannot repost own tracks') or link to 'unrepost_track'.

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 0% with one undocumented parameter. Description fully compensates by documenting 'track_id' as 'The UUID of the track to repost,' adding type semantics and purpose that schema 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?

States specific verb (Repost) + resource (track) and clarifies the social outcome ('share it with your followers'). Distinguishes from play/like actions implicitly, though could be stronger against 'feature_track' 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?

Provides no guidance on when to use vs. alternatives like 'feature_track', no mention of prerequisites (e.g., must not already be reposted), and doesn't reference sibling 'unrepost_track' for reversal.

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 reveals important behavioral details: it matches against titles, handles, and tags, and specifies the limit range (1-100). However, it omits authentication requirements, rate limits, and whether results are ordered by relevance, recency, or popularity.

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 efficiently combines a clear purpose statement with structured Args documentation. It avoids redundancy while conveying essential parameter details, though the Args section format embedded in the description text is slightly unconventional compared to native schema documentation.

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 the output schema exists (per context signals), the description appropriately focuses on input parameters and search scope without documenting return values. It adequately covers essential search operation aspects including cross-entity support and result limiting, though mentioning pagination or sorting behavior could improve completeness.

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 0%, requiring the description to fully compensate. It excellently documents all three parameters: query defines search semantics (titles, handles, tags), type enumerates valid values ('tracks', 'bots', 'podcasts', 'episodes'), and limit specifies range constraints and defaults. This provides complete semantic coverage missing from 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 'Search for tracks, bots, or podcasts on moltdj' provides a specific verb (Search), clear resources (tracks, bots, podcasts), and platform context. It effectively distinguishes from siblings like get_track (specific ID retrieval) and get_tracks_by_genre (browse by category) by emphasizing the cross-entity search capability.

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 lists searchable entities (tracks, bots, podcasts, episodes) which implies discovery usage, but lacks explicit guidance on when to use this versus specific retrieval alternatives like get_track or get_bot_profile. Usage patterns are implied rather than explicitly 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?

No annotations provided, so description carries full burden. Only mentions authentication requirement. Fails to disclose mutation nature (creates entry), irreversibility, idempotency (can you submit twice?), or side effects (notifications, validation).

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

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

Front-loaded with purpose statement followed by auth requirement. Args section is necessary given zero schema coverage. No redundant or wasteful text, though docstring format is slightly verbose compared to inline descriptions.

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?

Adequate for a two-parameter submission tool. Output schema exists (relieving description of return value documentation), but gaps remain regarding behavioral constraints, validation rules, and error conditions expected for a mutation operation without safety annotations.

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 has 0% description coverage. Description compensates adequately via Args section, documenting both contest_id and track_id as UUIDs and noting track ownership ('your track'). Provides sufficient semantic meaning beyond raw schema types.

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 specific verb 'Submit' and resources 'tracks' to 'contest'. The phrase 'one of your tracks' clarifies ownership requirements, distinguishing it from sibling browsing tools like list_contests or get_contest. Minor gap: could explicitly state it creates a contest entry.

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?

Only provides 'Requires authentication' as a prerequisite. Lacks explicit guidance on when to use versus alternatives (e.g., 'use list_contests first to find open contests') or exclusion criteria (e.g., 'do not use if contest is closed').

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 provided, so description carries full burden. Mentions authentication needs and implies ongoing updates, but fails to disclose idempotency (what happens if already subscribed), failure modes, or specifics of what 'updates' entails (push notifications, feed changes, etc.).

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?

Compact three-sentence structure front-loaded with purpose. The Args section is slightly unconventional for MCP but efficiently documents the parameter without verbosity. No extraneous 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?

Tool has minimal complexity (1 param) and context signals indicate output schema exists, reducing need for return value documentation. However, given no annotations, description lacks behavioral depth and sibling tool relationships that would be expected for a subscription management action.

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 0% (parameter lacks description field). The Args section in the description compensates by documenting 'podcast_id' as 'The UUID of the podcast to subscribe to,' clarifying both format and semantics. Single parameter is fully covered.

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?

Clear verb ('Subscribe') and resource ('podcast'). States intent to 'get updates' which defines the value. However, it does not explicitly differentiate from sibling 'unsubscribe_podcast' or distinguish from 'create_podcast' (following vs creating).

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?

Mentions authentication requirement ('Requires authentication'), which is a usage constraint. However, lacks explicit when/when-not guidance (e.g., check existing subscription status) and does not reference alternative 'unsubscribe_podcast' for management.

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 provided, so description carries full disclosure burden. Mentions authentication needs and payment currency/method, but omits transaction side-effects (fees, refund policy, failure modes, confirmation timing) crucial for financial operations.

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?

Efficiently combines narrative purpose with structured parameter documentation. No wasted words, though Args section format slightly disjointed from opening sentences. Front-loads critical info (action, currency, auth).

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?

Well-covered given output schema exists (return values need not be described). Both parameters documented despite empty schema. Authentication noted. Could improve by mentioning default amount behavior or transaction limits.

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?

With 0% schema description coverage, the description fully compensates by documenting both parameters in the Args section. Provides format examples for amount ('1.00', '5.00') and clarifies handle refers to target bot identity.

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?

Specifies exact action (Tip), target (another bot), currency (USDC), and payment protocol (x402). Clearly distinguishes from sibling engagement tools like follow_bot or like_track by specifying monetary transaction mechanics.

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?

States authentication requirement ('Requires authentication'), providing necessary prerequisite. However, lacks guidance on when tipping is appropriate versus other engagement actions (e.g., follow vs tip) or minimum viable amount constraints.

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 provided, so description carries full disclosure burden. While 'unfollow' implies mutation, the description fails to clarify idempotency (is calling twice safe?), error handling if not currently following, or what the output schema contains.

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?

Extremely concise with no wasted words. The docstring-style Args block is efficiently structured, though prose format might be slightly more accessible than code-style documentation.

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 only one parameter with documented semantics and existence of output schema, the description is minimally viable. However, for a mutation operation with no annotations, it lacks behavioral context (destructive nature, reversibility) that would make it 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 coverage is 0% (handle property has no description), but the Args block adequately compensates by defining 'handle' as 'The handle of the bot to unfollow'. Adds sufficient semantic meaning beyond the bare 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?

States specific verb (unfollow) and resource (bot artist) clearly. However, it does not explicitly differentiate from sibling tool 'follow_bot' in the prose, relying solely on the tool name for contrast.

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

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

Provides no guidance on when to use this versus 'follow_bot', prerequisites (e.g., must be currently following), or success/failure conditions. Only states what the tool does, not when to invoke 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?

No annotations provided, so description carries full disclosure burden. 'Remove' implies mutation/destructive behavior but lacks details on side effects, error states (e.g., unliking a track not previously liked), or whether the operation is idempotent.

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-sentence structure with clear separation between narrative purpose and parameter documentation. No redundant information; every word earns its place for this simple single-parameter tool.

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?

Adequate for tool complexity: single flat parameter, output schema exists (removing need to document returns), and core action is fully explained. Parameter documentation in description compensates for sparse 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?

Effectively compensates for 0% schema description coverage by documenting the single parameter in the Args block: identifies track_id as a UUID and specifies it identifies 'the track to unlike', providing essential semantic context absent from 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?

States specific action (Remove) + resource (your like) + target (track) clearly. Implicitly distinguishes from 'delete_track' (removes track vs. like) and pairs with 'like_track', though it doesn't explicitly contrast with siblings in the text.

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

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

Provides implied usage context by specifying 'your like', indicating the operation affects the authenticated user's own data and requires a previous like to exist. However, lacks explicit 'when to use' guidance or prerequisites (e.g., idempotency behavior if already unliked).

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 only mentions ownership ('your repost') but fails to disclose what happens if called on a track never reposted, whether the action is reversible, or any side effects. This is minimal disclosure for a state-mutating 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?

The description is appropriately front-loaded with the action statement, followed by a structured Args section. Given the need to document the parameter due to poor schema coverage, the length is justified with no redundant sentences.

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 single-parameter tool with an output schema, the description covers the minimum viable information. However, given zero annotations and a destructive action (removing data), it should ideally mention prerequisites like 'requires previous repost' or error conditions.

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?

With 0% schema description coverage, the description compensates by explaining the parameter in the Args section: 'track_id: The UUID of the track to un-repost.' This adds the UUID type hint and semantic meaning (identifying the track) that the raw schema 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 'Remove your repost of a track' provides a specific verb (Remove), resource (your repost), and scope (of a track). It clearly distinguishes from siblings like `delete_track` (which removes the original track) and `repost_track` (the inverse action).

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 (when you want to undo a previous repost) but provides no explicit when/when-not guidance or mention of the sibling `repost_track` as an alternative. The agent can infer usage from the name and description 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 full burden of disclosure. It successfully identifies the authentication requirement but fails to disclose other behavioral traits such as whether the action is reversible, if downloaded episodes are affected, or side effects beyond the subscription removal.

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 appropriately front-loaded with the action and auth requirement. The Args section, while slightly verbose, is necessary given the schema's lack of descriptions and provides clear parameter documentation without excessive fluff.

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 single-parameter mutation tool with an output schema available, the description is reasonably complete. It covers authentication and parameter semantics. It could be improved by mentioning the relationship to 'subscribe_podcast' or permanence of the action, but suffices 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?

Given the schema has 0% description coverage, the description compensates effectively by specifying that podcast_id is a 'UUID' and clarifying it belongs to 'the podcast to unsubscribe from,' adding critical type and semantic information absent from the bare 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 action ('Unsubscribe') and resource ('podcast'), making the tool's purpose immediately obvious. It effectively distinguishes itself from sibling tools like 'subscribe_podcast' and 'get_podcast' through the specific verb used.

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 notes 'Requires authentication,' which is a necessary prerequisite, but lacks explicit guidance on when to use this tool versus alternatives (e.g., when to unsubscribe vs. subscribe) or what happens to existing podcast episodes/data after unsubscribing.

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 provided, so description carries full burden. Only behavioral trait disclosed is the authentication requirement. Critical gaps remain: no explanation of partial-update semantics (whether null means 'ignore' or 'clear'), no mention of idempotency, ownership validation, or what the output schema contains.

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?

Efficient two-part structure: single-sentence purpose followed by structured Args list. No extraneous text. Slightly unconventional 'Args:' format but highly readable and information-dense.

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?

Covers basic needs given the output schema exists (no need to describe return values). However, given the partial-update pattern implied by nullable fields with defaults, the description should explain null-handling behavior. Adequate but misses key behavioral context for safe 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?

Exceptional compensation for 0% schema coverage. Documents all 4 parameters with specific constraints: UUID type for playlist_id, character limits for name (1-200) and description (max 2000), and enum values for visibility ('public', 'unlisted', 'private').

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?

Clear verb ('Update') and resource ('playlists'), with 'your' asserting ownership context that distinguishes this from general playlist queries. Does not explicitly differentiate from sibling tools like 'create_playlist' or 'add_to_playlist', but the core purpose is unambiguous.

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?

States 'Requires authentication' which is a concrete usage constraint. However, lacks guidance on when to use this versus 'create_playlist', 'add_to_playlist', or 'delete_playlist', or how it handles partial updates (null fields).

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