cc0pedia CC0 database + cc0.company agent services as MCP tools, paid per call via x402 on Base.
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Average 4/5 across 14 of 14 tools scored. Lowest: 2.2/5.
Each tool has a clearly distinct purpose: data query (cc0pedia*), market data, daily brief, art generation by specific artists, mfergpt interactions, and random card generation. No two tools overlap in functionality.
Names are consistently lowercase with hyphens, and within each subgroup (e.g., `-gen` for generation, `cc0pedia-` for data tools), patterns are clear. However, there is no single verb_noun pattern across all tools, with some being proper names (cc0-daily-brief) or suffixed with `-ask`, `-lore`, etc.
14 tools is well-scoped for the server's purpose, covering CC0 data retrieval, art generation, and a chatbot. Each tool adds distinct value without overwhelming the set.
The server covers core operations: CC0 data lookup, search, verify, market, daily brief; four distinct art generation styles; mfergpt Q&A, lore, and image transformation; plus random card generation. A minor gap is the lack of a tool to list available art styles or a general-purpose CC0 art explorer, but essential workflows are present.
cc0-daily-briefAInspectHourly-refreshed digest of the top 5 CC0 NFT collections by 24h volume — per-collection metrics (volume, sales, floor, holders), cc0pedia editorial context, and an LLM-synthesized narrative + macro headline. Synchronous JSON (no polling). [PAID: $0.05 USDC per call via x402]
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses key behaviors: 'Hourly-refreshed', 'Synchronous JSON (no polling)', and crucially the cost ('PAID: $0.05 USDC per call via x402'). This goes beyond basic purpose. Could mention error handling or rate limits, but overall strong.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is compact (3-4 sentences) and front-loaded with essential info: refresh rate, content, format, cost. Every sentence adds value without redundancy. Highly efficient.
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 is simple (no parameters, no output schema). Description covers what it returns: 'top 5 CC0 NFT collections by 24h volume', per-collection metrics (volume, sales, floor, holders), cc0pedia editorial context, and LLM-synthesized narrative + macro headline. Missing exact JSON structure, but sufficient for understanding. Could be slightly more precise.
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 (empty object with additionalProperties: true). Description does not need to add parameter info. Baseline for 0 params is 4, and description is sufficient.
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 tool's function: 'Hourly-refreshed digest of the top 5 CC0 NFT collections by 24h volume'. It specifies verb 'digest', resource 'top 5 CC0 NFT collections', and includes detailed content (metrics, editorial context, narrative). This distinguishes it from sibling tools like cc0pedia (search) and cc0pedia-market (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?
Description implies usage for obtaining a concise summary of top CC0 collections, but does not explicitly state when to use vs. alternatives like cc0pedia or cc0pedia-market. There is no 'when-not' guidance. The mention of 'Synchronous JSON' hints at tradeoffs but lacks direct comparison.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
cc0pediaAInspectLook up any CC0 creator, collection, or work by name or slug — provenance, creator, license, on-chain pointers (collection contract addresses / chains / OpenSea), and the full CC0 body. The agentic read API over the largest machine-readable CC0 database. Synchronous JSON (no polling). [PAID: $0.01 USDC per call via x402]
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Name or slug of a CC0 creator, collection, or work (e.g. 'sartoshi', 'mfers', 'xcopy'). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description explicitly states it's a read API, synchronous, and paid ($0.01 USDC per call). It does not contradict any annotations (none provided). However, it does not specify if there are rate limits or authentication needs beyond payment.
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 front-loaded with the essential purpose and output details. It is slightly verbose but every sentence adds value (purpose, positioning, mode, pricing). Could be shortened slightly without losing clarity.
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 one simple parameter and no output schema, the description fully explains what the tool returns (provenance, creator, license, etc.) and how it works (synchronous, paid). No gaps for a lookup 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?
The single parameter 'query' is well-explained with examples, and schema coverage is 100%. The description adds meaningful context 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 resource (CC0 creators, collections, works), the action (look up), and what information is returned (provenance, license, on-chain pointers, etc.), distinguishing it from sibling tools like market, search, and verify.
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 it's the primary lookup tool for CC0 entities, mentioning synchronous JSON and paid model. However, it does not explicitly contrast with siblings (e.g., when to use search instead), so some guidance is missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
cc0pedia-marketAInspectLive market for a CC0 asset by slug or contract — token price/FDV/liquidity/volume (Dexscreener+GeckoTerminal) or NFT floor/volume/owners (OpenSea). Synchronous JSON, $0.01/lookup. [PAID: $0.01 USDC per call via x402]
| Name | Required | Description | Default |
|---|---|---|---|
| slug | No | A cc0pedia entry slug (e.g. 'mfergpt', 'ok-degen'). | |
| chain | No | Optional chain hint (base, ethereum). | |
| contract | No | Or a raw contract address (0x...). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full burden. It discloses synchronous JSON response, $0.01 cost per lookup, payment method (x402), and the data types returned. However, it does not cover error handling (e.g., invalid slug), caching behavior, or the meaning of `additionalProperties: true` in the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two efficient sentences: the first defines function and output, the second adds cost and synchronicity. No redundancy, front-loaded with the most important information. Every sentence 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?
Despite no output schema, the description adequately outlines return data (token vs NFT metrics) and sources. It explains the tool's purpose and cost model. Missing details include error responses, behavior when both slug and contract are provided, and handling of the `additionalProperties` flag.
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 clear descriptions for each parameter (slug, chain, contract). The description adds context about using slug or contract for lookup and chain as a hint, but this does not significantly enhance what the schema already provides, so baseline score 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 it provides 'Live market for a CC0 asset' including specific metrics (price/FDV/liquidity/volume or floor/volume/owners) from named sources (Dexscreener+GeckoTerminal or OpenSea). The verb (query) and resource (market data) are explicit and it distinguishes from sibling reference/search tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use when current market data is needed via slug or contract. It is the only market-oriented tool among siblings, so context is clear, but no explicit when-not-to-use or alternative tool comparisons are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
cc0pedia-searchAInspectFree-text search over the largest machine-readable CC0 database — up to 25 ranked matches (slug, title, kind, license, on-chain contract). The 'find candidates' call before a cc0pedia lookup. Synchronous JSON, $0.01/search. [PAID: $0.01 USDC per call via x402]
| Name | Required | Description | Default |
|---|---|---|---|
| kind | No | Optional filter by entry kind. | |
| limit | No | Max results (1-50, default 25). | |
| query | Yes | Free-text search across cc0pedia titles (case-insensitive). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses synchronous execution, JSON return, cost ($0.01), and mentions the number of matches. It does not cover idempotency, auth requirements, or the inconsistency between 'up to 25 matches' and schema allowing up to 50. Given no annotations, it partially compensates but leaves 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, each providing essential information: purpose, workflow, and cost. No fluff, front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description covers purpose, workflow, result fields, and cost. It could be more complete by detailing the response format or pagination, but it adequately supports agent decision-making.
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 100% schema coverage, the baseline is 3. The description adds no extra semantics beyond what the schema provides for parameters; it only mentions the query implicitly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it performs free-text search over CC0 database, lists output fields, and distinguishes itself as a preliminary step before cc0pedia lookup, making its purpose distinct from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly states it's the 'find candidates' call before a cc0pedia lookup, providing clear context for when to use it. However, it does not explicitly mention when not to use or alternatives, though the sibling tool list is available.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
cc0pedia-verifyAInspectLicense oracle: pass a contract address, get whether it's a documented CC0 work + full provenance (creator, chain, standard, supply, sources). The 'is this safe to reuse?' check before touching on-chain art. Synchronous JSON, $0.01/check. [PAID: $0.01 USDC per call via x402]
| Name | Required | Description | Default |
|---|---|---|---|
| contract | Yes | EVM contract address (0x...) to check for documented CC0 status. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description carries full burden. Discloses synchronous JSON response, cost ($0.01/check), and payment method (x402). Does not specify error handling for invalid addresses, but sufficiently transparent for a simple query.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences covering purpose, use case, and technical details. No wasted words, front-loaded with core functionality.
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, description adequately explains inputs, outputs (CC0 status + provenance), and behavior (synchronous, paid). Minor gap: no mention of handling non-CC0 or unknown contracts.
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?
Single parameter 'contract' is described in schema as 'EVM contract address...'. Description adds 'to check for documented CC0 status' but adds little beyond schema. 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?
Clearly states it's a license oracle that checks if a contract address is a documented CC0 work and returns full provenance. Distinguished from sibling tools like cc0pedia-search by focusing on address-based verification for reuse safety.
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 use case: 'The 'is this safe to reuse?' check before touching on-chain art.' Implies when to use, but lacks explicit when-not-to-use or alternatives. Mentions paid nature.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
darkfarms-genAInspectGenerate crypto Pepe meme art in the style of Darkfarms1 (CC0). Send a prompt, get an image. Output is public domain. [PAID: $0.069 USDC per call via x402; async — returns a job_id to poll]
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Input prompt for this service. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It discloses payment ($0.069 USDC via x402), async execution (returns job_id), and public domain output. However, it omits authentication requirements, how to poll for results, or handling of additional parameters (schema allows additionalProperties).
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—one sentence plus a bracketed note—with no wasted words. All essential information is front-loaded, making it efficient for an agent to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 param, no output schema, no annotations), the description covers the key aspects: purpose, payment, async nature, and licensing. It lacks details on polling response structure, but for a straightforward generative tool, this is adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with one parameter, 'prompt', described as 'Input prompt for this service.' The description adds context about the style and output, but does not elaborate on prompt format or best practices. Baseline for full coverage is 3, and the added context is marginal.
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 the style of Darkfarms1, with a specific verb ('generate') and resource ('crypto Pepe meme art'). It distinguishes from sibling art generators (e.g., hokusai-gen, monet-gen) by mentioning the unique style and CC0 license.
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 usage: send a prompt, get an image. It includes payment details and async behavior. While it does not explicitly say when not to use alternatives, the style and licensing are clear differentiators, so agents can infer appropriate context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
hokusai-genAInspectEdo-period ukiyo-e woodblock prints in the Hokusai style, fine-tuned by cc0toshi on public-domain works — crashing waves, Mount Fuji, fishermen and traders, bold inkwork on natural pigments. CC0 / public domain output. [PAID: $0.069 USDC per call via x402; async — returns a job_id to poll]
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Input prompt for this service. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses cost, async nature, polling requirement, CC0/public domain output. Lacks details on authentication or rate limits, but for a generative tool these are reasonable. No contradiction.
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 sentence with structured information: purpose first, then style details, licensing, and payment/async behavior. 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?
Given 1 parameter, no output schema, and no annotations, the description covers purpose, style, licensing, cost, and async behavior. It does not detail the response format beyond job_id, but this is sufficient for a simple generation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (single 'prompt' parameter with description). The tool description adds context that the prompt should be in Hokusai ukiyo-e style, supplementing the generic schema description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool generates Edo-period ukiyo-e woodblock prints in Hokusai style, with specific examples (crashing waves, Mount Fuji). It distinguishes from siblings like monet-gen or van-gogh-gen by the unique artistic 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?
Description mentions paid ($0.069 USDC) and async (returns job_id to poll), providing context for invocation. However, it does not explicitly state when to use this tool over alternatives, though style differentiation is implied by sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
mfergpt-askAInspectAsk mferGPT anything and get a response in the mfer voice. Re-brokered over x402. [PAID: $0.055 USDC per call via x402]
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Input prompt for this service. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses the payment model ($0.055 USDC via x402) and the re-brokering aspect, but does not explain x402 protocol details, rate limits, or whether the tool 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 concise sentences, front-loaded with the core action and unique selling point, with zero wasted words. Every sentence 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 only one parameter and no output schema, the description covers the essential purpose, voice, and cost. It lacks response format details but is adequate for a simple ask-and-respond tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single parameter 'prompt', so the baseline is 3. The description does not add extra meaning beyond 'Input prompt for this service', lacking guidance on prompt format or constraints.
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 'Ask' and the resource 'mferGPT', and specifies the unique response style 'in the mfer voice', distinguishing it from siblings like 'mfergpt-lore' and 'mfergpt-mferfy'.
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 says 'Ask anything', implying broad usage, but does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives among sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
mfergpt-loreBInspectQuery the mfer lore archive (history, culture, community) by mfergpt. Re-brokered over x402 — pass your search as the prompt. [PAID: $0.025 USDC per call via x402]
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Input prompt for this service. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must convey behavioral traits. It discloses that calls are paid ($0.025 USDC) and re-brokered over x402, which is useful. However, it does not confirm whether the tool is read-only, idempotent, or has side effects, leaving uncertainty about its operational impact.
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 efficiently convey purpose ('query the mfer lore archive'), usage ('pass your search as the prompt'), and cost ('PAID: $0.025 USDC per call'). No superfluous text; every sentence serves a 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?
Despite having only one parameter and no output schema, the description does not mention what the tool returns (e.g., lore texts, summaries). This omission forces the agent to guess the response format, reducing 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 coverage is 100% with one parameter 'prompt' described generically as 'Input prompt'. The description adds value by specifying that the prompt should be a 'search' query, clarifying the intended usage beyond the schema's minimal description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool queries the 'mfer lore archive' for history, culture, and community, which is a specific and distinct purpose. However, it does not explicitly differentiate from sibling tools like 'mfergpt-ask' or 'mfergpt-mferfy', leaving some ambiguity about when to use this particular tool over others.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description only instructs to 'pass your search as the prompt', but does not clarify prerequisites, typical use cases, or scenarios where another sibling tool would be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
mfergpt-mferfyAInspectTurn any image into a mfer in the mferGPT house style. Pass an image URL as the prompt. Re-brokered over x402. [PAID: $0.055 USDC per call via x402; async — returns a job_id to poll]
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Input prompt for this service. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully bears transparency. It discloses paid cost ($0.055 USDC), async nature (returns job_id to poll), and re-brokering over x402. However, it does not detail privacy or data handling, which is a minor gap.
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: two sentences plus a bracketed note. It front-loads the main purpose and provides all key information without redundancy. Every sentence adds value: purpose, input format, cost, and async behavior.
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 tool with one parameter and no output schema, the description is fairly complete. It explains the input (image URL), payment, and async polling. However, it omits details like polling endpoint, output format, or image size limits, which would enhance 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?
The schema has 100% coverage for the only parameter 'prompt' but only describes it as 'Input prompt for this service.' The description adds critical meaning by specifying 'Pass an image URL as the prompt,' which uniquely defines the input format 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's purpose: 'Turn any image into a mfer in the mferGPT house style.' It specifies the action (turn), the input (image), and the output style (mfer), distinguishing it from sibling gen tools that apply different 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 implies usage by saying 'Pass an image URL as the prompt' but does not explicitly state when to use this tool versus alternatives like hokusai-gen or monet-gen. No when-not or exclusion criteria are provided, leaving the agent to infer context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
monet-genAInspectFrench impressionist paintings in the Monet style, fine-tuned by cc0toshi on Art Institute of Chicago archival public-domain works — water lilies, haystacks, Rouen Cathedral, gardens at Giverny, soft natural light and broken color. CC0 / public domain output. [PAID: $0.069 USDC per call via x402; async — returns a job_id to poll]
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Input prompt for this service. |
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 payment ($0.069 USDC per call), async execution (returns job_id), and output licensing (CC0/public domain). Missing info on rate limits or failure handling.
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 two sentences, front-loaded with purpose and style, followed by payment and async details. 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?
Covers purpose, payment, async behavior, and output licensing. Lacks details on polling the job_id or response format, but given simple tool with 1 param, it is fairly 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 input schema has a single 'prompt' parameter with minimal description. The tool description adds semantic value by specifying the type of prompts (e.g., water lilies, haystacks, soft natural light), compensating for the schema's lack of detail.
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 Monet-style French impressionist paintings, listing specific subjects and style elements. It distinguishes from sibling tools like van-gogh-gen by specifying 'Monet style' and 'CC0/public domain output.'
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 for generating Monet-style images and mentions payment and async behavior, but does not explicitly state when to use this tool vs alternatives or provide exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
sartoshi-genAInspectGenerate 1/1 hand-drawn mfer art in the style of sartoshi (CC0) — thin wobbly ink lines, naive doodle style. Output is public domain. [PAID: $0.069 USDC per call via x402; async — returns a job_id to poll]
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Input prompt for this service. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description discloses that output is public domain, paid (via x402), and async (returns job_id). No contradictions detected.
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 with essential details (purpose, style, output license, cost, async behavior), efficiently front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers purpose, style, licensing, cost, and async nature. Lacks details on polling mechanics, but given the single parameter and no output schema, it is fairly 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 100% with the parameter 'prompt' described as 'Input prompt for this service.' The description adds stylistic context but no additional parameter-level semantics beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Generate 1/1 hand-drawn mfer art in the style of sartoshi (CC0)' with specific stylistic details, distinguishing it from sibling art generators.
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 includes cost and async behavior but lacks guidance on when to use this tool versus other art generators like darkfarms-gen or hokusai-gen.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
tcgenerate-randomCInspectGenerate a random AI trading-card image by TCGenerate — a fully autofilled collectible card (name, subject, action, background, art style). Takes no input. Re-brokered over x402; output pinned to IPFS. [PAID: $1.005 USDC per call via x402; async — returns a job_id to poll]
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Input prompt for this service. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must disclose behaviors fully. While it mentions paid async operation, it claims 'Takes no input' but the schema requires a 'prompt' parameter. This severe contradiction undermines transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is relatively brief but wastes space by including a false statement ('Takes no input'). The pricing and async details are useful, but the contradiction reduces efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description should cover return values and usage flow. It mentions returning a job_id but does not explain the final output. The input contradiction also makes it incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3, but the description directly contradicts the schema by stating no input is needed while the schema requires a prompt. This adds confusion rather than value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool 'Generate a random AI trading-card image', which is a specific verb-resource pair. It distinguishes from sibling generation tools by emphasizing 'random' and 'autofilled'. However, the contradiction with the input schema reduces clarity slightly.
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 tool versus alternatives like darkfarms-gen or hokusai-gen. The description notes it is 'random' and 'paid' but fails to provide decision-making context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
van-gogh-genAInspectPost-impressionist oil paintings in the Van Gogh style, fine-tuned by cc0toshi on Art Institute of Chicago archival public-domain works — visible brushstrokes, swirling skies, wheat fields, cypress trees, self-portraits, sunflowers. CC0 / public domain output. [PAID: $0.069 USDC per call via x402; async — returns a job_id to poll]
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Input prompt for this service. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description bears the full burden of behavioral disclosure. It reveals the tool is paid, async, and produces CC0/public domain output. It also mentions the fine-tuning source. However, it lacks details on rate limits, error handling, or what happens with invalid prompts, which would be helpful for an async paid 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, efficient sentence that packs essential information: style, training data, output license, pricing, and async behavior. It is front-loaded with purpose. While it could be broken into clearer segments (e.g., list), it remains concise without burying key details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has only one parameter, no output schema, and no annotations, the description covers the main points: style, pricing, async nature, and output license. It is largely complete for an agent to decide whether to invoke it, though it could mention how to poll the job_id or typical response format.
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 has 100% coverage for its single 'prompt' parameter, so the baseline is 3. The description does not add additional semantics about the prompt (e.g., best practices, constraints, or examples) beyond the schema's generic 'Input prompt for this service.' Thus, it meets the baseline but does not enhance understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as generating post-impressionist oil paintings in Van Gogh style, specifying visible brushstrokes, swirling skies, and other distinctive elements. It differentiates itself from sibling tools like monet-gen and hokusai-gen by explicitly naming 'Van Gogh style' and mentioning fine-tuning on Art Institute of Chicago works.
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 on when to use the tool (for Van Gogh style art generation) and important usage details: it's a paid service ($0.069 USDC per call) and async (returns a job_id to poll). However, it does not explicitly mention when not to use it or suggest alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
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