cc0company-mcp

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

Without annotations, the description fully details behavior: synchronous, hourly-refreshed, paid per call, and contents (metrics, editorial context, narrative). It does not mention authentication or destructive effects, but those are unlikely for a read-only digest.

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 concise sentences, front-loading the key purpose and including essential details like refresh rate and cost. No superfluous 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?

Given no parameters or output schema, the description covers the tool's purpose, refresh rate, contents, and pricing adequately. It could be slightly improved by hinting at the output format, but it is mostly 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?

No parameters exist (0 params, 100% schema coverage baseline). The description adds value by explaining the output without needing to document parameters.

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 provides an 'Hourly-refreshed digest of the top 5 CC0 NFT collections by 24h volume' with specific metrics and narrative content. It distinguishes itself from sibling tools like cc0pedia or cc0pedia-market, which have different purposes.

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

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

The description implies usage context by mentioning synchronous JSON and paid rate, but it does not explicitly guide when to use this tool versus alternatives like cc0pedia or cc0pedia-market. No exclusions or prerequisites are given.

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

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

With no annotations, the description discloses behavioral traits: synchronous JSON, no polling, paid per call ($0.01 USDC), and the type of data returned. It does not mention error handling or rate limits, but the key behaviors are transparent.

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

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

Two sentences: the first states the core function, the second adds context (read API, synchronous, payment). Every sentence is necessary and earns its place.

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

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

For a simple lookup tool with one parameter and no output schema, the description covers the returned data types, synchronicity, and payment, making it sufficiently complete for an agent to understand usage.

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

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

Schema coverage is 100% with a clear parameter description. The tool description reinforces the parameter's purpose but does not add meaningful new information beyond the schema.

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

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

The description clearly states the tool looks up CC0 creators, collections, or works by name or slug, with specific details on the data returned (provenance, creator, license, on-chain pointers, full CC0 body). It distinguishes itself from siblings like cc0pedia-search by being a direct lookup API.

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 this tool is for direct lookups by name/slug and is a read API, but does not explicitly state when to use alternatives like cc0pedia-search. However, it provides clear context for use.

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 states that data comes from Dexscreener+GeckoTerminal or OpenSea, that it is synchronous JSON, and that it is paid. This is sufficient for a read-only lookup 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?

The description is two sentences, front-loaded with the core purpose, and every phrase adds value. 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?

No output schema, but the description specifies what data is returned (token vs NFT metrics). For a market data tool, this covers the essential information, though exact format is omitted.

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

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

Schema coverage is 100% (all three parameters described). The description adds context by explaining the two query modes (slug or contract) and noting that chain is optional. This reinforces schema meaning.

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

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

The description clearly states it is a 'Live market for a CC0 asset by slug or contract' and lists the specific data returned (token price/FDV/liquidity/volume or NFT floor/volume/owners). It distinguishes itself from sibling tools like cc0pedia (likely general info) and cc0pedia-search by focusing solely on market 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?

The description explains the two ways to query (by slug or contract) and mentions an optional chain hint. It also notes the cost ($0.01/lookup via x402). However, it does not explicitly state when to use this tool vs alternatives or provide exclusions for 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?

No annotations are provided, so the description carries full burden. It discloses synchronous execution, cost ($0.01/search), and return fields, but does not mention idempotency, error behavior, rate limits, or authentication requirements. The read-only nature is implied but not explicit.

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

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

The description is concise with two sentences that convey purpose, scope, and cost. Information is front-loaded, but the cost notation is slightly verbose for the main purpose.

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 search tool with no output schema, the description compensates by listing return fields (slug, title, kind, license, on-chain contract). It covers cost, synchronicity, and usage context. Missing details on pagination, no-results handling, and the odd 'additionalProperties: true' in 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?

All three parameters are described in the input schema with 100% coverage. The description reinforces the query's role in free-text search and mentions the default limit of 25, but adds no additional semantic meaning beyond the schema.

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

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

The description clearly states it performs free-text search over a CC0 database, listing return fields and distinguishing itself as the 'find candidates' call before a cc0pedia lookup, which sets it apart from sibling tools like 'cc0pedia' for direct lookup.

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

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

The description explicitly positions the tool as a precursor to cc0pedia lookup, giving clear context for when to use it. It also mentions cost and synchronous execution, but does not explicitly state when not to use or compare to other siblings beyond the one 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?

With no annotations, the description fully bears the burden. It discloses the synchronous JSON format, cost ($0.01/check via x402), and the output fields (creator, chain, standard, supply, sources). It does not cover error states or rate limits, but for a simple oracle, the cost and output details are sufficient.

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

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

The description is concise with three main parts: purpose, use-case analogy, and format/cost. Every sentence adds value, though the pricing note could be integrated to reduce repetition. No unnecessary words.

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

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

For a single-parameter tool with no output schema, the description lists the return fields and format, making it self-contained. It lacks error handling details, but the simplicity of the tool makes this acceptable.

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

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

The input schema already provides a clear description for the only parameter (`contract`), and the description adds no new semantic information beyond what the schema states. With 100% schema coverage, a baseline score of 3 is appropriate.

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

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

The description explicitly states the verb (pass, get) and the resource (contract address, documented CC0 work, full provenance), clearly distinguishing this tool from siblings like cc0pedia-search (search by name) and cc0pedia-market (market data) by focusing on license verification.

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 specifies the input (contract address) and the use case ('check before touching on-chain art'), providing clear context for when to use. It implicitly excludes other sibling tools by being the verification tool, but lacks explicit when-not-to-use instructions or alternative tool names.

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 fully bears the burden of behavioral disclosure. It explicitly states payment ($0.069 USDC via x402), asynchronous operation (returns a job_id to poll), and public domain licensing. These are critical behavioral traits beyond basic input/output.

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 extremely concise: three sentences that front-load the core purpose, then efficiently convey usage instruction and key behavioral traits (payment, async, licensing). Every sentence earns its place without redundancy or 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?

Given the tool's simplicity (one required parameter, no output schema), the description is complete. It explains the return format (job_id for polling), pricing, and async nature. For a generative art tool, it covers all necessary context for correct invocation and expectation setting.

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

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

Input schema coverage is 100% for the single required parameter 'prompt'. The description adds no additional meaning beyond what the schema already provides ('Input prompt for this service'). Baseline score of 3 is appropriate when schema already covers parameter semantics adequately.

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 generates crypto Pepe meme art in a specific style (Darkfarms1 CC0). It uses a specific verb+resource combination ('Generate crypto Pepe meme art') and distinguishes itself from sibling tools like hokusai-gen or monet-gen by naming the unique style.

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 clear context: send a prompt to get an image, and notes the output is public domain. While it doesn't explicitly state when not to use or compare to alternatives, the style specificity inherently guides selection. It lacks explicit exclusions but offers adequate usage direction.

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 provides critical behavioral info: fine-tuned on public-domain works, CC0 output, paid ($0.069 USDC per call via x402), and async (returns job_id to poll). It does not cover failure modes but is sufficient for a simple generation 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?

The description is a single paragraph but packs style, training data, rights, cost, and async behavior concisely. It could be more structured (e.g., bullet points), but every sentence adds 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 low complexity (one parameter, no output schema), the description covers purpose, cost, async behavior, and output rights. It doesn't explicitly state it's image generation, but the style and 'woodblock prints' imply it. Overall, it provides enough context for an AI agent to select and invoke correctly.

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

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

The only parameter 'prompt' has a generic schema description. The tool description adds meaning by specifying the domain (ukiyo-e, Hokusai style) and example subjects, which helps the agent craft relevant prompts. Schema coverage is 100%, so the description compensates nicely.

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 generates Edo-period ukiyo-e woodblock prints in Hokusai style, listing specific subjects (waves, Mount Fuji, fishermen). This distinguishes it from sibling tools like monte-gen or van-gogh-gen which target other artistic styles.

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 implicitly tells when to use this tool (for Hokusai-style generation) versus alternatives (other art styles). It also mentions it's paid and async, but lacks explicit guidance on prompt formulation or prerequisites.

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 discloses the paid nature and re-brokering via x402, which are key behavioral traits. However, it omits details like response format, rate limits, or any side effects.

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

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

The description is a single sentence plus a cost note. It's front-loaded and concise, with no extraneous information. Every word adds value.

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

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

Given the tool's simplicity (single parameter, no output schema, no annotations), the description adequately covers the core functionality and a key behavioral detail (cost). It could mention the response format, but overall it is sufficiently 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?

The single parameter 'prompt' has a schema description of 'Input prompt for this service.' The tool description adds no additional meaning beyond what the schema provides. With 100% schema coverage, baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: ask anything and get a response in the 'mfer voice'. It distinguishes from siblings like mfergpt-lore and mfergpt-mferfy by focusing on general Q&A rather than lore or transformation.

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

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

The description implies the tool is for general prompting but lacks explicit guidance on when to use it versus alternatives. It mentions the cost ($0.055 USDC) as a practical constraint but provides no when-not-to-use criteria.

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

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

Discloses payment requirement and how input is used. However, no annotations are present, and the description does not cover error behavior, rate limits, or return format. Adequate but not thorough.

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 concise sentences: first states purpose, second adds payment and usage. No wasted words, front-loaded with essential information.

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

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

For a simple query tool with one parameter and no output schema, the description covers purpose, input format, and payment. Could mention return type or limitations, but overall sufficient.

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

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

Schema coverage is 100% with one parameter described as 'Input prompt'. The description adds 'pass your search as the prompt', providing slight extra meaning. Baseline 3 is appropriate.

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

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

The description clearly states the tool queries the 'mfer lore archive' with specific scope (history, culture, community), using a specific verb 'Query'. It distinguishes from sibling tools like mfergpt-ask and mfergpt-mferfy by focusing on lore.

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 context about payment ($0.025 per call via x402) and usage instruction ('pass your search as the prompt'). Does not explicitly contrast with alternatives, but the purpose is clear enough for an agent to select appropriately.

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

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

With no annotations provided, the description discloses cost ($0.055 USDC per call), async behavior, and polling for a job_id. However, it lacks details on input constraints (e.g., image format, size limits), side effects, authentication (beyond 're-brokered over x402'), and error handling. Partial transparency but leaves significant gaps.

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

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

Three sentences: one for purpose, one for input specification, one for pricing and async behavior. Each sentence is essential and front-loaded. No redundant or filler 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?

Given the single parameter, no output schema, and sibling tools, the description adequately explains input (image URL), transformation goal (mfer style), cost, and output (job_id to poll). However, it does not specify the polling mechanism or final output format, which could be helpful for a complete usage flow.

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

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

The schema describes 'prompt' generically as 'Input prompt for this service.' The description adds critical context: 'Pass an image URL as the prompt.' This clarifies the intended use (URL, not text) and adds meaning beyond the schema, fully compensating for the generic schema description with 100% coverage.

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

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

Description explicitly states the tool turns any image into a 'mfer' in the mferGPT house style, using an image URL as prompt. This clearly differentiates it from sibling tools like darkfarms-gen, which likely generate images from scratch, and mfergpt-ask/lore, which are text-based.

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?

Description implies usage when you have an existing image to stylize and mentions cost and async nature, aiding decision. However, it does not explicitly mention when not to use it or compare to alternatives like hokusai-gen or monet-gen, leaving some ambiguity.

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 critical behavior: payment model, async nature (poll for job_id), output licensing (CC0/public domain), and model source (fine-tuned on Art Institute of Chicago works). No contradictions.

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, no wasted words. First sentence front-loads purpose and examples; second sentence covers licensing and payment details. Efficient and well-organized.

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 purpose, licensing, payment, and async behavior. Missing explicit output format (e.g., image URL) but sufficient for an agent to invoke and poll. Adequate given simple input schema and no output schema.

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

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

Schema has 100% coverage with single parameter 'prompt' described generically. The description adds meaningful context by specifying the style and examples, helping the agent craft appropriate prompts.

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 generates French impressionist paintings in Monet style, listing examples like water lilies and haystacks. It distinguishes from siblings (van-gogh-gen, hokusai-gen) by specifying the Monet style and training 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?

Provides explicit usage context: paid ($0.069 USDC), async (returns job_id to poll), and CC0 licensing. Implicitly tells when to use (for Monet-style art) versus siblings like van-gogh-gen, but no explicit when-not-to-use.

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 paid and async nature, which are critical behavioral traits. It does not mention rate limits or authentication, but for a generation tool, this is sufficient.

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

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

The description is concise, packing style details, licensing, payment, and async behavior into two sentences. It is front-loaded with the main purpose and avoids any 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?

The tool has one parameter, no output schema, and no annotations. The description covers generation style, licensing, payment, and async behavior. It lacks details on how to poll for the job_id or the response format, but it is largely complete for an agent 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 coverage is 100%, so the description adds no extra meaning beyond the generic schema description. The parameter 'prompt' is described as 'Input prompt for this service.' which does not provide additional guidance on prompt formatting or best practices.

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 generates 1/1 hand-drawn mfer art in sartoshi style (CC0). It uses a specific verb 'Generate' and specific resource, differentiating it from sibling tools like monet-gen or darkfarms-gen.

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 mentions it is a paid service ($0.069 USDC per call via x402) and async (returns job_id to poll). It does not explicitly state when to use this tool versus alternatives, but the style and cost provide context for usage.

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?

Discloses key behavioral traits: paid ($0.069 USDC per call via x402), async (returns job_id), and CC0/public domain output. No annotations provided, so description carries full burden and does so thoroughly.

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 plus a pricing note, no redundancy. First sentence front-loads core purpose; second adds essential behavioral details. Every sentence earns its place.

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

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

For a generative tool with no output schema, the description covers output format (job_id for polling), cost, and licensing. No missing critical information given the tool's simplicity.

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

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

Schema coverage is 100% (one parameter with generic description 'Input prompt for this service'). Tool description adds context about Van Gogh style but does not elaborate on prompt format, limitations, or examples. Baseline 3 due to high schema coverage; minimal added value for parameter semantics.

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

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

Clearly states the tool generates Post-impressionist oil paintings in Van Gogh style, listing specific motifs like brushstrokes and sunflowers. Distinguishes from sibling gen tools by explicitly naming Van Gogh style.

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?

Implied usage for Van Gogh style generation, but no explicit guidance on when to use vs alternatives (e.g., hokusai-gen, monet-gen). No when-not-to-use or alternative suggestions.

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