26 pay-per-call AI agent tools for marketing, sales, content, research, verification, and integration testing. Agents pay per call via x402 micropayments (USDC on Base), raw JSON in and out. Includes delivery-acceptance receipts (ACCEPT/REJECT/REVIEW), a webhook capture/replay/conformance lab, and n8n workflow validation with bounded JSON-Patch repair.
Scored across 30 tools
Most tools have clearly distinct purposes, and similar ones are explicitly cross-referenced (analyze-document vs summarize-document, check- vs analyze-email-deliverability, analyze-competitors vs research-competitor-pricing). However, the high volume of analyze-* and generate-* tools makes a few pairs (e.g., analyze-sentiment vs extract-pain-points) require careful reading to select correctly.
All tool names follow a consistent snake_case verb_noun pattern, using verbs like analyze, generate, research, check, extract, score, and verify. Even the longer names (verify-deliverable-and-issue-acceptance-receipt, webhook-lab-capture-simulate-replay) are predictable and readable.
At 30 tools, this server exceeds the 25-tool threshold for a well-scoped set. The broad marketing/automation domain partly justifies the count, but many tools could be consolidated (e.g., separate case-study and testimonial question generators), and the unclustered list will likely overwhelm agents.
The marketing and sales-outreach workflow is covered end to end: research, strategy, positioning, content generation, repurposing, scoring, and deliverability checks. A few adjacent areas are missing (e.g., ad copy, social performance analytics), and the three automation/verification tools feel like a separate domain tacked on, but the core surface has no critical dead ends.
Map a market's competitors: names, strengths, weaknesses, and how each is positioned. Use for landscape and positioning questions ('who else does X and how do they differ'). Pricing is not covered; for plan tiers and price comparison use research-competitor-pricing. Based on model training knowledge, not a live lookup; figures are estimates. Recent entrants may be missing. Pay-per-call: $0.05 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | The product, category, or market (e.g. 'AI meeting note-takers for sales teams'). Example: AI meeting note-takers for sales teams | |
| context | No | Optional: your own product description, so gaps are framed against it. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does so thoroughly: it states the model-training basis, estimates, missing recent entrants, pay-per-call billing, and the error behavior for missing payment headers. This is exceptionally clear about operational behavior and failure modes.
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 information-dense but every sentence serves a purpose: purpose, usage, exclusion, data caveats, and payment behavior. It is front-loaded with the core action, and no words are wasted.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers purpose, alternatives, limitations, and payment. The only notable gap is that neither schema nor description explicitly says whether the 'query' parameter is required or what happens if omitted; the parameter is marked optional only by the schema's lack of a required list. For most uses this is clear enough, but a brief note would make it 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 100%, so both parameters are already well described. The description adds only a small note on 'context' (optional, for framing gaps) which slightly reinforces but does not significantly extend the schema. 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 opens with a specific verb and resource: 'Map a market's competitors' and lists the output aspects (names, strengths, weaknesses, positioning). It also distinguishes itself from research-competitor-pricing by explicitly excluding pricing, so an agent can tell them apart.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives explicit usage context ('Use for landscape and positioning questions') and an exclusion with the sibling alternative ('Pricing is not covered; for plan tiers and price comparison use research-competitor-pricing'). It also sets expectations about data source and recency, which helps an agent decide if this tool is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Evaluate a document or article and say what to do about it: a short summary plus key insights, weaknesses or gaps, and concrete recommendations. Use when you need judgment or next steps. For a neutral digest only, use summarize-document (cheaper). For customer complaints inside a text, use extract-pain-points. URLs are not fetched; paste the text. Pay-per-call: $0.05 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | The document text, or the question the analysis should answer. Example: Paste the document text here, plus the decision it should inform. | |
| context | No | Optional: the document text plus the reader's goal (e.g. 'investor memo - decide whether to follow up'). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the behavioral burden, and it does so thoroughly. It discloses the URL limitation, the pay-per-call cost, the payment-signature header requirement, and what happens if that header is missing (error whose data carries payment terms).
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 dense but efficient: purpose and output are front-loaded, then usage guidance, then alternatives, then input constraint and payment behavior. Every sentence adds distinct information and nothing feels redundant.
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 annotations and no output schema, the description covers what the tool returns, when to use it, what inputs are expected, a key limitation (URLs not fetched), and the payment/error contract. An agent has enough to decide whether and how to call it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both query and context already explained and exemplified. The description adds little new parameter-level meaning beyond reinforcing that text should be pasted rather than a URL, so a baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb and resource ('Evaluate a document or article') and defines the output: summary, key insights, weaknesses/gaps, and concrete recommendations. It also explicitly differentiates from summarize-document and extract-pain-points by naming what they do instead.
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 states exactly when to use the tool ('when you need judgment or next steps') and gives explicit alternates with conditions: 'For a neutral digest only, use summarize-document (cheaper)' and 'For customer complaints inside a text, use extract-pain-points.' It also warns that URLs are not fetched and text must be pasted.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Full deliverability audit of email copy: 0-100 score, every flagged phrase with its reason, subject-line risk, concrete rewrites, and SPF/DKIM/DMARC reminders. Use when a draft failed or warned in check-email-deliverability, or before an important send. For a quick pass/fail on many emails, use check-email-deliverability. Content signals only; no inbox-placement guarantee. Pay-per-call: $0.04 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | The email subject and body to audit. Example: Subject: Last chance - 50% off ends tonight Hi there, ... | |
| context | No | Optional: audience, sending-domain age, or campaign type. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does it thoroughly. It discloses content-only analysis ("no inbox-placement guarantee"), pay-per-call pricing, the payment-signature header requirement, and the error behavior that carries payment terms. This is rich behavioral context beyond the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core function and outputs, then covers usage, exclusions, limitations, and payment behavior. Every sentence adds necessary information; none is filler or redundant with the 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?
The description covers purpose, outputs, when to use, when not to use, limitations, and payment requirements. With only two parameters and no output schema, an agent has everything needed to select and invoke the tool correctly. The payment-signature requirement is especially important because it affects successful invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both query and context parameters. The description adds no new parameter-level semantics beyond the schema, though it does reinforce that analysis is content-only. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb and resource: "Full deliverability audit of email copy," and enumerates concrete outputs (0-100 score, flagged phrases with reasons, subject-line risk, rewrites, SPF/DKIM/DMARC reminders). It explicitly distinguishes itself from the sibling check-email-deliverability by contrasting a full audit with a quick pass/fail.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives explicit when-to-use guidance: "Use when a draft failed or warned in check-email-deliverability, or before an important send." It also names the alternative clearly: "For a quick pass/fail on many emails, use check-email-deliverability." No ambiguity remains about tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Name the top 5 marketing trends for an industry or topic, with a short description of each. Use for trend briefings and idea generation. Based on model training knowledge, not a live lookup; figures are estimates. It may miss very recent shifts. For a full plan built on these, use generate-marketing-strategy. Pay-per-call: $0.05 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | The industry or topic (e.g. 'B2B SaaS demand generation'). Example: B2B SaaS demand generation | |
| context | No | Optional: company stage, audience, or region. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does so thoroughly: it discloses that output is based on training knowledge, figures are estimates, recent shifts may be missed, and it clearly explains the pay-per-call requirement and the error behavior when payment-signature is missing.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Every sentence carries distinct value: output definition, use cases, data-source caveat, sibling routing, and payment/error behavior. The main result is front-loaded and the rest follows logically without padding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers inputs, output shape, limitations, cost, error path, and alternative tool selection. No output schema exists, but the description sufficiently communicates what the agent should expect and how to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both query and context. The description adds the top-5 output expectation and the general 'industry or topic' scope but does not materially explain parameter usage, nor does it clarify that query is effectively required despite there being no required field.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb ('Name'), a precise deliverable ('top 5 marketing trends'), and the target scope ('for an industry or topic'). It further defines the output ('with a short description of each'), making the tool's function unmistakable and distinct from sibling tools like generate-marketing-strategy.
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 says when to use the tool ('Use for trend briefings and idea generation') and names the sibling alternative for adjacent needs ('For a full plan built on these, use generate-marketing-strategy'). Also gives a clear exclusion: it is not a live lookup and may miss very recent shifts.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Judge review momentum from review data you supply (dates, ratings, text): velocity score, sentiment trend, and response recommendations. Use for 'are reviews speeding up or slowing down, and how should we respond'. You must include the reviews; it does not fetch from G2, Capterra, or app stores. For one-off tone, use analyze-sentiment. Pay-per-call: $0.05 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | The reviews with dates and ratings. Example: 2026-09-01 5* 'Fast setup' 2026-09-10 2* 'Support slow' ... | |
| context | No | Optional: the product and the period to compare. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses the pay-per-call cost ($0.05 USDC on Base via x402), the required payment-signature header, and the error behavior when payment is missing. It also clarifies the tool does not fetch external data. It doesn't describe the exact response shape, but the error/payment behavior is valuable 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 compact and front-loaded with the core purpose, then usage guidance, then payment details. Every sentence adds value. Slight room for improvement: the payment/error details could be separated more clearly, but overall it's well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 2-parameter tool with no output schema, the description covers the key operational details: what to supply, what not to expect, the alternative tool, and the payment requirement. It doesn't describe the output format, but with no output schema and a clear purpose, the description is reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters. The description adds context by showing an example format for the query parameter and clarifying that context is optional. It doesn't add much beyond the schema, but the baseline of 3 is appropriate given full coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Judge'), a clear resource ('review momentum from review data you supply'), and the concrete outputs (velocity score, sentiment trend, response recommendations). It also differentiates from the sibling analyze-sentiment by noting this is for momentum/trends, not one-off tone.
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 says when to use it ('are reviews speeding up or slowing down, and how should we respond'), what data must be supplied ('You must include the reviews'), and what it does NOT do ('it does not fetch from G2, Capterra, or app stores'). It also names the alternative for one-off tone: analyze-sentiment.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Classify the overall sentiment of a text and name its key themes and emotional triggers. Use for 'how do people feel about this'. For specific problems to fix, use extract-pain-points. For review momentum over time, use analyze-review-velocity. Pay-per-call: $0.04 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | The text to classify. Example: Paste the reviews, comments, or post text here. | |
| context | No | Optional: what the text is about or who wrote it. |
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 discloses the pay-per-call model and the specific error behavior when the payment-signature header is missing (returns an error containing payment terms). It does not explicitly state read-only semantics or output handling, but the analysis nature makes that implicit. A 4 reflects that it goes beyond the bare minimum without describing every side effect.
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 (four sentences) and front-loads the primary purpose and usage before moving to alternatives and payment details. Every sentence earns its place—purpose, usage, alternatives, and a critical payment caveat—with 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?
For a simple analysis tool with two optional parameters and no output schema, the description covers purpose, usage boundaries, and the payment failure mode. It does not spell out the exact output structure, but it hints at what the result contains (sentiment, themes, triggers). Given the absence of annotations and output schema, this is reasonably complete, though a mention of the response format would push it to 5.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and both parameters (query and context) are already well-described in the schema. The description adds little beyond the schema's own descriptions, but it does reinforce the purpose of the 'query' parameter by implying it holds the text to classify. Baseline 3 is appropriate when the schema handles parameter documentation.
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 specific verb ('Classify') and a defined resource ('overall sentiment of a text') and explicitly lists additional outputs ('key themes and emotional triggers'). It clearly distinguishes itself from sibling tools by naming extract-pain-points and analyze-review-velocity, so the agent can tell them apart without opening their schemas.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides explicit when-to-use guidance ('Use for 'how do people feel about this'') and explicitly names alternative tools for different use cases ('For specific problems to fix, use extract-pain-points. For review momentum over time, use analyze-review-velocity.'). It also discloses a payment prerequisite, which is a concrete usage condition.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Suggest the top 10 SEO keywords for a topic with estimated relative search volume and difficulty. Use for early keyword ideation and content planning. Based on model training knowledge, not a live lookup; figures are estimates. Not live keyword-tool data; validate volumes in an SEO tool before committing budget. For a posting schedule, use generate-content-calendar. Pay-per-call: $0.04 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | The topic or seed keyword (e.g. 'n8n workflow automation'). Example: n8n workflow automation for agencies | |
| context | No | Optional: audience, region, or site type to focus the keywords. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does so excellently. It discloses that it uses model training knowledge rather than live lookup, that figures are estimates, the pay-per-call cost and payment mechanism, and the error behavior when the payment-signature header is missing. All of this is beyond what structured data would provide.
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 and every sentence adds unique information: purpose, usage context, data limitations, alternative tool, pricing, and error handling. No fluff or 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?
For a tool with no output schema, the description informs the agent about the return value (top 10 keywords with relative search volume and difficulty), data freshness, pricing, and failure modes. This is complete for a 2-parameter tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description does not add meaning beyond the schema for the two parameters; it refers to 'a topic' which aligns with the 'query' description but adds no new semantics for '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 states a specific verb and resource: 'Suggest the top 10 SEO keywords for a topic' with 'estimated relative search volume and difficulty.' It also distinguishes itself from siblings by naming generate-content-calendar for posting schedules and by describing its use case for early keyword ideation.
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 provides usage context: 'Use for early keyword ideation and content planning,' and names an alternative for a different scenario: 'For a posting schedule, use generate-content-calendar.' It also cautions that this is not live keyword-tool data and advises validation before budget commitment.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fast, cheap spam-risk gate for one email: pass/warn/fail, a 0-100 score, and up to 3 flagged phrases. Use for a go/no-go before sending or for bulk screening of many drafts. No rewrites. For a full audit with reasons, subject-line risk, and rewritten copy, use analyze-email-deliverability. Content signals only; no inbox-placement guarantee. Pay-per-call: $0.005 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | The email subject and body to check. Example: Subject: Quick question about your onboarding Hi Sam, ... | |
| context | No | Optional: sender type or audience (e.g. 'B2B cold outreach'). |
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 and does so well: no rewrites, content-signal-only scoring with no inbox-placement guarantee, pay-per-call pricing, and the error behavior when the payment-signature header is missing. This goes well beyond a basic 'checks deliverability' statement.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded: the core output and use case come first, followed by the sibling distinction, limitation, and payment specifics. Every sentence adds information, and none of it duplicates the 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?
There is no output schema, so the description correctly specifies the return shape (pass/warn/fail, score, flagged phrases). It also covers payment terms and error behavior, making it complete for an agent to invoke and interpret results without additional 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 coverage is 100%, so the schema already documents both query and context. The description only adds that this checks a single email and is content-signal based; it does not add per-parameter syntax or format guidance, matching the baseline for full schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific function: a spam-risk gate for one email that returns pass/warn/fail, a 0-100 score, and up to 3 flagged phrases. It also explicitly distinguishes itself from the analyze-email-deliverability sibling, so an agent can tell the tools apart without opening schemas.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives explicit when-to-use context: a go/no-go check before sending or bulk screening of many drafts. It also names the alternative (analyze-email-deliverability) for full audits with reasons, subject-line risk, and rewrites, listing what this tool will not do ('No rewrites').
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Pull the top 5 customer pain points out of text you supply (reviews, interview notes, support tickets, forum threads), each with severity and a suggested fix. Use when you have raw customer voice. For overall tone and emotion, use analyze-sentiment. For review trends over time, use analyze-review-velocity. URLs are not fetched; paste the text. Pay-per-call: $0.05 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | The customer text to mine (reviews, tickets, notes). Example: Paste 10-50 customer reviews or support tickets here. | |
| context | No | Optional: the product or segment the text is about. |
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 pay-per-call requirement ($0.05 USDC on Base via x402), the error behavior when payment-signature header is missing, and the fact that URLs are not fetched. It also states the output shape (top 5 pain points with severity and suggested fix). This is strong behavioral disclosure, though it could mention whether the operation is read-only or has side effects 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 compact and front-loaded with the core purpose. Every sentence earns its place: purpose, usage guidance, URL constraint, and payment terms. It's slightly dense with the payment details, but those are essential for correct invocation. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 2 parameters, 100% schema coverage, and no output schema, the description covers the essential invocation details: input type, output shape, payment requirement, and error behavior. It doesn't describe the exact response format (e.g., JSON structure), but with no output schema, a bit more detail on the return format could help. Still, the description is largely complete for an agent to call it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters. The description adds context by explaining what the query parameter should contain ('reviews, interview notes, support tickets, forum threads') and implies the context parameter is optional. However, it doesn't add much beyond the schema's own descriptions, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Pull'), a specific resource ('top 5 customer pain points'), and the input type ('text you supply: reviews, interview notes, support tickets, forum threads'). It also names sibling tools it is not (analyze-sentiment, analyze-review-velocity), which clearly differentiates it from similar 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 explicitly says when to use this tool ('when you have raw customer voice') and names alternatives for other use cases ('For overall tone and emotion, use analyze-sentiment. For review trends over time, use analyze-review-velocity'). It also gives a practical constraint: 'URLs are not fetched; paste the text.' This is explicit when/when-not guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create 3 A/B test variants of existing marketing copy, one variable changed per variant, each with a hypothesis, plus a test plan (primary metric, sample size, duration). Tone stays close to the source. Use when you already have copy to test. To score copy without variants, use score-landing-page or predict-viral-potential. Pay-per-call: $0.05 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | The copy to vary (headline, CTA, email subject, etc.). Example: Headline: 'Get paid 2x faster' | |
| context | No | Optional: the goal metric and current performance. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden flags. It discloses the pay-per-call cost, settlement network, protocol, and the exact failure mode when the payment-signature header is missing. It also states the tonal constraint that output stays close to the source.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Every sentence earns its place: deliverable, tone, usage routing, and payment behavior. The key output and trigger condition are front-loaded before financial 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?
Despite having no output schema, the description enumerates the expected output components clearly. It covers what input to provide, when to use it, what it returns, and what payment prerequisites exist, so an agent has nearly everything needed to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline of 3 applies. The description reinforces that the query is existing marketing copy and that context is optional, but it does not add substantially new parameter syntax or formatting details beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a very specific deliverable: 3 A/B test variants of existing marketing copy with one variable changed per variant, each with a hypothesis, plus a test plan covering metric, sample size, and duration. It also names scoring-oriented siblings so the agent can distinguish variation from evaluation.
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 says 'Use when you already have copy to test', giving a clear trigger condition. It also names alternatives with the precise condition 'To score copy without variants, use score-landing-page or predict-viral-potential.'
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Write 10 open-ended discovery questions, grouped by category, for interviewing a customer for a full written case study (problem, solution, results). For short quotable praise, use generate-testimonial-questions. Pay-per-call: $0.04 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | The customer and what they used the product for. Example: Freight broker that cut invoice delays using our tool | |
| context | No | Optional: the results you want the case study to show. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden. It discloses the exact output shape (10 grouped, open-ended questions), the pay-per-call payment requirement of $0.04 USDC on Base via x402, and the failure behavior when the payment-signature header is missing.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The core purpose is front-loaded in one sentence, followed by a routing sentence and payment details. There is no fluff or repetition of schema fields.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description gives output count, grouping, interview context, alternative tool, and payment authorization behavior. The only small gap is that although query is semantically necessary, the schema lists no required parameters and the description does not explicitly mark it as required.
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 both query and context already described in the schema. The description adds no additional parameter-level detail, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description begins with a specific verb and resource: 'Write 10 open-ended discovery questions, grouped by category, for interviewing a customer for a full written case study.' It clearly distinguishes itself from the sibling generate-testimonial-questions by noting it is for full case studies, not short praise.
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?
'For short quotable praise, use generate-testimonial-questions' explicitly names the alternative and the condition that selects it. It also frames the intended context as discovery interviews for a written case study with problem, solution, and results.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Write a first-touch cold email for a product and audience: subject line, preview text, and a 100-150 word body. Use for the opening message. For the follow-ups after no reply, use generate-follow-up-sequence. To check spam risk before sending, use check-email-deliverability. Pay-per-call: $0.05 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | The product and the recipient type. Example: Invoice tool for freelance designers, to agency ops leads | |
| context | No | Optional: prospect research, proof points, or tone. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and discloses the pay-per-call cost, payment method, and the error behavior when the payment-signature header is missing. This is important behavioral context beyond the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is four sentences, front-loads the main purpose, and packs usage guidance, alternatives, and payment details without unnecessary fluff. It is appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a low-complexity tool with no output schema, the description covers output components, use case, alternatives, and payment/error behavior. An agent has enough context to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and both parameters already have descriptions, so the baseline is 3. The description adds little beyond restating that the email is 'for a product and audience,' which mirrors the query parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and output: 'Write a first-touch cold email' with subject, preview, and 100-150 word body. It distinguishes this from sibling tools by explicitly naming generate-follow-up-sequence and check-email-deliverability.
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 says 'Use for the opening message' and directs to generate-follow-up-sequence after no reply, and check-email-deliverability to check spam risk. This gives an agent clear when-to-use and when-not-to-use guidance with named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Plan a 30-day content calendar: daily themes, post types, and a posting schedule. Use for planning a month of content. For the overall strategy behind it, use generate-marketing-strategy. For the posts themselves, use generate-linkedin-post or repurpose-long-form. Pay-per-call: $0.06 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | The brand or topic and main channel(s). Example: B2B invoicing tool, LinkedIn + newsletter, October | |
| context | No | Optional: goals, posting frequency, or key dates. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It goes beyond the schema by stating the pay-per-call cost ($0.06 USDC on Base via x402) and the error behavior when a payment-signature header is missing (error data carries payment terms). This is valuable operational and error-handling context, though it doesn't mention other traits like rate limits or data retention.
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 three purposeful sentences: definition, usage/alternatives, and payment/error behavior. It is front-loaded with the core purpose, then alternates, then payment details. Every sentence earns its place with no filler or 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?
For a low-complexity tool with only two optional parameters and no output schema, the description adequately covers what the tool produces (a 30-day calendar with themes, post types, schedule), when to use it, and the critical payment/error behavior. Nothing essential for an agent to invoke it correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so both parameters are already described in the input schema. The description adds a helpful example ('B2B invoicing tool, LinkedIn + newsletter, October') but does not materially expand on the schema's parameter semantics. The baseline of 3 is appropriate here.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Plan'), a concrete resource ('30-day content calendar'), and the deliverables ('daily themes, post types, and a posting schedule'). It also differentiates itself from related siblings by explicitly naming generate-marketing-strategy, generate-linkedin-post, and repurpose-long-form, so an agent can immediately tell what this tool does and does not do.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is explicit: 'Use for planning a month of content.' It then names the exact conditions for alternatives: 'For the overall strategy behind it, use generate-marketing-strategy. For the posts themselves, use generate-linkedin-post or repurpose-long-form.' This gives clear when-to-use and when-to-use-something-else guidance with no ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Write a 3-email follow-up sequence (subjects, timing, and body) for prospects who did not reply to a first email. For the first-touch email, use generate-cold-email. Pay-per-call: $0.05 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | The product and the original email or offer. Example: Follow-ups to a cold email offering a free invoice audit | |
| context | No | Optional: the first email's text, audience, and preferred spacing. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for behavioral disclosure. It does this well by revealing that the call is pay-per-call ($0.05 USDC on Base via x402), requires a payment-signature header, and returns an error with payment terms if absent. This is meaningful business-critical context beyond a simple 'generates content' statement.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, each earning its place: the function, the routing rule, and the payment requirement. The core purpose is front-loaded, and there is no fluff or repetition of schema 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 that there is no output schema and no annotations, the description covers the essential behavioral, commercial, and routing context well. It could be slightly more explicit about the query parameter being effectively required despite the schema listing no required parameters, but overall an agent has enough to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3 even though the description adds no parameter-specific detail. The description reinforces the purpose of the query/context parameters at a high level, but it does not go beyond the schema’s own parameter descriptions.
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 specific verb ('Write') and a concrete resource ('3-email follow-up sequence') including its components (subjects, timing, body) and the exact target condition (prospects who did not reply). It also distinguishes itself from generate-cold-email by explicitly stating that first-touch emails belong to 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?
The description gives explicit routing guidance: use generate-cold-email for the first-touch email, and this tool for follow-ups after no reply. This tells the agent when to select this tool versus a closely related sibling without leaving it to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Write one LinkedIn post on a topic: post text, hashtags, and an opening hook. Use for a single original post. To adapt an existing article for LinkedIn and other channels at once, use repurpose-long-form. To score a draft before posting, use predict-viral-potential. Pay-per-call: $0.05 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | The topic or point the post should make. Example: Why we stopped cold calling and what replaced it | |
| context | No | Optional: author role, audience, or a story to include. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does well by disclosing the pay-per-call cost, the required payment-signature header, and the error behavior when it is missing. It stops short of describing the exact response format, but the output contents are at least partially listed.
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 earning its place: purpose, usage routing, and payment behavior. The most important info is front-loaded and there is no filler or 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?
For a simple two-parameter generation tool, the description covers purpose, scope, alternatives, and payment requirements. The only notable gap is the absence of a detailed return structure, but the listed deliverables (text, hashtags, hook) mitigate that.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters well. The description adds only a generic sense of 'topic' and 'context,' which is helpful but not a significant increase over the schema's own examples.
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-resource pair ('Write one LinkedIn post') and names the deliverables: post text, hashtags, and an opening hook. It also distinguishes itself from nearby siblings by explicitly limiting scope to a 'single original post.'
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states exactly when to use this tool and names two alternatives with their conditions: repurpose-long-form for adapting an article, and predict-viral-potential for scoring a draft. This leaves no ambiguity about routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Build a full go-to-market plan for a product: target audience, positioning, channels, and timeline. Use when starting from scratch or needing a whole plan. For only a tagline and value proposition, use generate-positioning-statement. For a 30-day posting schedule, use generate-content-calendar. Pay-per-call: $0.10 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | The product and market (e.g. 'invoice tool for freelance designers'). Example: Invoice tool for freelance designers, $0 marketing budget | |
| context | No | Optional: budget, stage, current channels, or constraints. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral burden and discloses the key operational requirement: the call costs $0.10 USDC on Base via x402 and returns an error with payment terms if the payment-signature header is missing. It also describes the deliverable's contents. It does not explicitly state side effects or return format, but for a generation tool this is sufficient and contains 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?
The description is front-loaded with purpose, then gives usage guidance, sibling alternatives, and the payment/error behavior. Every sentence carries necessary information and there is no filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having no output schema or annotations, the description is complete enough for an agent to decide when to use the tool and how to invoke it. It covers purpose, use cases, alternatives, expected input context, pricing, and error 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?
Schema description coverage is 100%, and both parameters already have meaningful descriptions and an example for query. The description adds no parameter-level detail, but the baseline of 3 applies because the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb and resource: 'Build a full go-to-market plan' and lists the plan's components: target audience, positioning, channels, and timeline. It also distinguishes the tool from siblings by naming generate-positioning-statement and generate-content-calendar, so an agent can tell them apart without opening schemas.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives an explicit use condition: 'Use when starting from scratch or needing a whole plan.' It also provides clear exclusions and alternatives: for only a tagline/value proposition use generate-positioning-statement, and for a 30-day posting schedule use generate-content-calendar. This leaves no ambiguity about when to choose this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Write positioning for one product: tagline, value proposition, and differentiation. Use for a single positioning block. For a full plan with channels and timeline, use generate-marketing-strategy. For competitor research first, use analyze-competitors. Pay-per-call: $0.05 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | The product and who it is for. Example: Invoice tool for freelance designers that auto-chases late payments | |
| context | No | Optional: key competitors or what makes it different. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses the pay-per-call cost, the payment-signature header requirement, and the error behavior (error data carries payment terms). This is substantial operational context beyond the schema, though it leaves out any statement about idempotency or 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?
Five focused sentences, front-loading purpose and scope, then alternatives, then payment details. Every sentence earns its place; no filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter generation tool with no output schema, the description covers purpose, alternatives, output components, and the payment requirement. It could explicitly describe the response format, but the essential calling details are present.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%: both query and context already have descriptions. The tool description adds no parameter-level meaning beyond what the schema provides, so it meets the baseline without going further.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb ('Write') and resource ('positioning for one product') and enumerates the output components (tagline, value proposition, differentiation). It goes further by naming sibling alternatives, making it easily distinguishable from generate-marketing-strategy and analyze-competitors.
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 tells when to use this tool ('Use for a single positioning block') and when not to ('For a full plan with channels and timeline, use generate-marketing-strategy; For competitor research first, use analyze-competitors'). It also mentions a payment prerequisite, which is a clear usage constraint.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Write podcast show notes from a transcript or outline: episode summary, key timestamps, guest bio, and links. Timestamps and links come only from the input; supply a transcript with times for accurate timestamps. URLs are not fetched; paste the text. Pay-per-call: $0.04 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | The episode transcript or outline. Example: [00:00] Intro ... [04:12] Guest on pricing ... | |
| context | No | Optional: guest name and bio details, links to include. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavior. It explicitly states the pay-per-call cost ($0.04 USDC on Base), the required payment-signature header and the resulting error carrying payment terms, and that URLs are not fetched. This is detailed, honest behavioral disclosure that goes beyond what annotations might typically provide.
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 five sentences, each contributing necessary information: purpose, input constraint, URL handling, pricing, and error behavior. It is front-loaded with the primary action and contains no filler, though it is slightly dense with payment and error details that could be considered tangential for invocation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with 2 parameters, full schema coverage, and no output schema or annotations, the description covers the essential aspects: what the tool produces, input requirements, pricing, and failure behavior. It does not specify the output format, but that is not necessary for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description adds meaning beyond the schema by specifying that timestamps and links come only from the input and that supplying a transcript with times yields accurate timestamps. This clarifies how to use the query parameter beyond its generic 'transcript or outline' schema description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description begins with a specific verb 'Write' and resource 'podcast show notes', and enumerates the deliverable components (episode summary, key timestamps, guest bio, links). No sibling tool handles show notes specifically, so the purpose is unambiguous and easily distinguished from the other generate-* 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 gives clear context: it works from a transcript or outline, requires times for accurate timestamps, and instructs to paste text rather than rely on fetched URLs. It does not explicitly name alternatives or exclusion conditions, but no sibling tool directly competes for this function, so the context is sufficient for an agent to choose it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Write 8 short questions that draw out quotable customer testimonials. Use when you need quotes, not a long story. For a full case-study interview, use generate-case-study-questions. Pay-per-call: $0.04 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | The product and the type of customer. Example: Invoice tool, freelance designer customers | |
| context | No | Optional: the benefit you want quotes about. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It discloses the output format (8 questions), the payment requirement ($0.04 USDC on Base via x402), and the error behavior when the payment-signature header is missing. For a generative tool this is solid coverage, though it does not explicitly state that the call is read-only or side-effect-free.
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 earning its place: the deliverable, the usage guidance, and the payment/error behavior. No redundant wording, and key information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple 2-param generator with optional parameters, the description covers the deliverable, usage context, alternative routing, and a critical payment requirement. Nothing an agent needs to invoke it correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so both parameters (query and context) are fully documented in the schema. The description adds minimal new parameter meaning but does reinforce the purpose of the output. Baseline 3 is appropriate per the rubric.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb ('write'), a precise deliverable ('8 short questions that draw out quotable customer testimonials'), and differentiates from a sibling tool by naming the alternative. An agent can distinguish it from generate-case-study-questions without inspecting the schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use ('when you need quotes, not a long story') and names the alternative ('For a full case-study interview, use generate-case-study-questions'). This leaves no ambiguity about tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Write webinar invitation copy: two invite emails and a landing-page blurb. Use when promoting a scheduled webinar or live event. Pay-per-call: $0.05 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | The webinar topic, date, and audience. Example: Pricing teardown webinar, Oct 8 1pm ET, for SaaS founders | |
| context | No | Optional: speakers, agenda, and registration link. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the full behavioral burden. It discloses the payment requirement ($0.05 USDC on Base via x402) and the error behavior when payment is missing, including that the error data carries payment terms. It also implies the output format (two emails and a blurb). It does not mention rate limits or additional side effects, but for a content generation tool with payment gating, this is substantive 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?
The description is three sentences with zero wasted words. The purpose is front-loaded in the first sentence, the usage condition in the second, and the payment/error behavior in the third. Every sentence earns its place, and the 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?
For a tool with 2 parameters, no output schema, and no annotations, the description covers the essential information: what it does, when to use it, and the critical payment requirement. It does not detail the exact structure of the returned copy, but it names the deliverables which is sufficient. Given the moderate complexity, this is complete enough for an agent to call correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, meaning both parameters (query and context) are described in the schema. The tool description does not add any extra semantics beyond what the schema already provides. According to the rubric, a high coverage baseline of 3 is appropriate, and the description does not compensate with additional parameter details, so 3 is correct.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb ('Write') and a specific resource ('webinar invitation copy') with explicit deliverables ('two invite emails and a landing-page blurb'). It also distinguishes itself from siblings by naming the exact use case ('promoting a scheduled webinar or live event'), which is unique among the generation tools listed. No ambiguity about what this tool does.
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 a clear condition for use ('Use when promoting a scheduled webinar or live event'). While it does not explicitly name alternative tools or say when NOT to use it, the sibling list makes it obvious that other tools exist for different content types (e.g., generate-cold-email, generate-linkedin-post), and the stated condition is enough for an agent to route correctly. A named alternative would push it to 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Score a draft post before publishing: a virality score broken down by emotion, specificity, novelty, and shareability, plus the best-fit platform. A heuristic prediction, not a reach forecast. For landing-page copy use score-landing-page; to generate test variants use generate-ab-test-variants. Pay-per-call: $0.05 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | The draft post text. Example: Paste the draft post here. | |
| context | No | Optional: target platform and audience. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral burden and does substantial work: it discloses the heuristic nature, the pay-per-call cost, the required payment-signature header, and the error behavior that surfaces payment terms. It doesn't detail every edge case, but it covers the most operationally critical 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?
Four sentences with zero filler: purpose, output breakdown, limitation, sibling routing, and payment requirement are each clearly separated. The most important information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Complete for the tool's complexity. All needed call information is present: what to pass, what the output contains, key limitations, alternatives, and monetization/auth behavior. No output schema exists, but the description conveys enough about return structure for an agent to interpret results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description reinforces that query is the draft text and context relates to platform/audience, but adds only marginal semantic value beyond the schema's own parameter descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb ('Score'), a clear resource ('a draft post before publishing'), and the exact output dimensions (emotion, specificity, novelty, shareability, best-fit platform). Explicitly distinguishes itself from siblings score-landing-page and generate-ab-test-variants, so an agent can route correctly.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit when-to-use context ('before publishing'), and names the alternatives with their trigger conditions ('For landing-page copy use score-landing-page; to generate test variants use generate-ab-test-variants'). Also clarifies the limitation ('heuristic prediction, not a reach forecast'), preventing misuse.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Repurpose one long piece for several channels at once: a LinkedIn post, an X thread, an email draft, and a blog intro. Use when you need all four. For an X thread only, use thread-from-article (cheaper). URLs are not fetched; paste the text. Pay-per-call: $0.06 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | The long-form text to repurpose. Example: Paste the article, transcript, or report text here. | |
| context | No | Optional: brand voice or audience notes. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations available, the description bears the full burden of behavioral disclosure. It clearly states that URLs are not fetched and that text must be pasted, warns about the pay-per-call requirement ($0.06 USDC on Base via x402), and even notes the exact error behavior when the payment-signature header is absent (error returns payment terms). This is unusually thorough for a tool description.
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 core purpose, then routes to the alternative, then gives the critical operational constraint and payment details. Every sentence adds value: purpose, when-to-use, URL behavior, pricing, and error behavior. No fluff, no 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?
For a 2-parameter tool with no output schema and no annotations, this description covers everything an agent needs to invoke it correctly: what outputs to expect, when to use it, how to supply input, the payment requirement, and even error handling. The only missing hint is about output structure, but the listed channels imply the shape, and the absence of an output schema lowers that burden.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description does not add meaning beyond the schema's parameter descriptions; the only extra nuance is 'paste the text,' which is a usage constraint rather than a semantic clarification. It minimally reinforces the query parameter's purpose but does not elevate 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 opens with a specific verb ('Repurpose') and resource ('one long piece') and enumerates the exact outputs: a LinkedIn post, an X thread, an email draft, and a blog intro. It also distinguishes itself from the sibling thread-from-article by noting that thread-from-article covers only the X thread use case, so an agent can immediately tell which tool fits.
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 states when to use this tool ('Use when you need all four') and names the alternative for a narrower need ('For an X thread only, use thread-from-article (cheaper)'). This is direct routing guidance with a reason (cheaper) that leaves no ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compare competitor pricing in a product category: competitor names, plan tiers and price points, and a value-for-money comparison. Use for pricing and packaging decisions. For strengths, weaknesses, and positioning, use analyze-competitors. Based on model training knowledge, not a live lookup; figures are estimates. Confirm prices on vendors' sites before quoting them. Pay-per-call: $0.06 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | The product category (e.g. 'email warm-up tools'). Example: Email warm-up tools for small agencies | |
| context | No | Optional: your planned price or tiers to compare against. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that results come from model training knowledge rather than live lookups, that figures are estimates, and that pay-per-call costs $0.06 USDC via x402 with a specific failure mode when the payment-signature header is missing. It does not cover every possible failure, but the key behavioral caveats and payment requirements are clearly surfaced.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded with purpose, followed by usage guidance, data caveats, and payment terms. Every sentence adds distinct value; the only minor debit is that payment details could arguably live elsewhere, but with no annotations, their inclusion is justified.
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 low complexity (two params, no output schema, no nested objects), the description is complete: it explains the return contents, how to use it, the alternative tool, the data-source limitation, and the payment requirement. An agent has enough to call it correctly and interpret results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters. The description adds context by mentioning 'product category' as the lookup scope, but it does not deepen the agent's understanding of how the optional context parameter affects the comparison. Baseline 3 is appropriate because the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Compare'), a clear resource ('competitor pricing in a product category'), and the exact output items: competitor names, plan tiers, price points, and value-for-money comparisons. It also explicitly differentiates itself from analyze-competitors, which covers strengths, weaknesses, and positioning, so an agent can distinguish sibling tools without opening schemas.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly says to use this tool for pricing and packaging decisions, and names analyze-competitors as the alternative for strengths, weaknesses, and positioning. This gives clear when-to-use and when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Research a sales prospect company: company profile, decision-maker role profile (role-level, not a named person), likely pain points, and an outreach angle. Use before writing outreach; then use generate-cold-email. Based on model training knowledge, not a live lookup; figures are estimates. Pay-per-call: $0.06 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | The company name and, if known, its website domain or industry. Example: Acme Logistics (acmelogistics.com), mid-market freight broker | |
| context | No | Optional: what you sell, so the angle fits. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden, and it excels: it discloses the training-data limitation ('figures are estimates'), the role-level scope (not a named person), the pay-per-call cost ($0.06 USDC on Base via x402), and the exact failure mode when payment header is missing (error whose data carries payment terms). No annotation contradiction exists.
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: purpose and deliverables, usage routing, data-limitation caveat, and payment/auth behavior. Each sentence adds essential information that is not available elsewhere, and the critical scope/usage info is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 2-parameter tool with no output schema and no annotations, the description fully covers what an agent needs to decide to call it, how to sequence it, what limits to expect, what it costs, and how payment errors surface. The listed deliverables also effectively describe the expected output shape.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters with examples and purpose. The tool description adds no parameter-specific meaning beyond what the schema provides; it only ties context to the outreach angle conceptually. This is the baseline-3 situation where the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: 'Research a sales prospect company,' and enumerates concrete deliverables (company profile, decision-maker role profile, likely pain points, outreach angle). It clearly distinguishes itself from siblings like generate-cold-email by framing it as pre-outreach research, and from extract-pain-points by covering the full prospect research scope rather than one isolated 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 explicitly says 'Use before writing outreach; then use generate-cold-email,' giving both a temporal placement and a named sibling alternative. It also states the tool is 'Based on model training knowledge, not a live lookup,' which implicitly tells the agent when not to use this tool (when live data is needed). This is clear when-to-use guidance with an explicit exclusion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Score landing-page copy for conversion: 0-100 with a 5-part breakdown (headline, value proposition, social proof, CTA, friction), the top 3 issues, and 3 quick-win rewrites. Use for a page's text. URLs are not fetched; paste the text. For social posts, use predict-viral-potential. Pay-per-call: $0.06 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | The landing-page copy. Example: Paste the page headline, subhead, body, and CTA text here. | |
| context | No | Optional: audience and the page's single goal. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description fully carries the transparency burden. It clearly discloses that URLs are not fetched, that the call is pay-per-call at $0.06 USDC on Base via x402, and that a missing payment-signature header produces an error whose data carries the payment terms. This is unusually candid and useful.
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 efficient and well-organized: core deliverable first, then usage scope, then the sibling distinction, then payment/error behavior. Every sentence earns its place, and there is no repetition of schema-provided details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a stateless scoring tool with two simple string parameters and no output schema, the description fully covers input format, expected return structure, the social-post alternative, and payment/auth behavior. Nothing an agent needs to call it correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already covers both parameters at 100%, so the baseline is 3. The description adds a useful behavioral nuance beyond the schema—'URLs are not fetched; paste the text'—and reinforces that context is optional. It doesn't need to say more.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb and resource: 'Score landing-page copy for conversion.' It also specifies the output format (0-100 score, 5-part breakdown, top 3 issues, 3 rewrites), making the tool's function unmistakable and distinct from siblings like predict-viral-potential.
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 says when to use the tool ('for a page's text'), how to provide input ('paste the text', not URLs), and names the alternative for social posts ('use predict-viral-potential'). This gives an agent concrete selection criteria without extra inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Condense a document or article into what it says: main argument, key findings, and supporting evidence. Use when you need a faithful digest with no opinion added. For judgment on top of the content (insights, gaps, recommended actions), use analyze-document. To turn an article into social posts, use thread-from-article or repurpose-long-form. URLs are not fetched; paste the text. Pay-per-call: $0.04 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | The document text to summarize, or a short label if the text is in context. Example: Paste the article or document text here. | |
| context | No | Optional: the full document text, or what the summary is for (audience, length). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral disclosure burden. It clearly discloses that URLs are not fetched, that the call is pay-per-call at $0.04 USDC on Base via x402, and that a missing payment-signature header produces an error carrying payment terms. It does not detail success response format, but the disclosed constraints are substantial and useful.
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 and front-loaded: the core purpose comes first, followed by usage guidance, alternatives, and caveats. Every sentence earns its place, covering purpose, when to use, what not to use, input format, and payment behavior without padding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with two optional parameters and no output schema, the description covers the essential invocation details: what input to provide, that URLs are not allowed, the payment requirement, and the error behavior. It does not describe the success response format, but for a summarization tool a textual summary is reasonably inferable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds minor value by reinforcing that text must be pasted and that URLs are not fetched, and it echoes the schema's distinction between query text and optional context, but it does not meaningfully extend beyond what the schema already documents.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Condense') with a clear resource ('a document or article') and names the expected output content: main argument, key findings, and supporting evidence. It also names sibling tools like analyze-document, thread-from-article, and repurpose-long-form, so the agent can immediately distinguish this tool from alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly states when to use the tool ('when you need a faithful digest with no opinion added') and directly routes to alternatives for judgment tasks and social post generation. It also gives a crucial usage constraint: URLs are not fetched, so the text must be pasted.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Validate and repair an n8n workflow JSON before production. static: deterministic plus model checks of nodes, connections, credentials, expressions, reachability. simulated: synthetic dry run with safe fixtures. repair: minimal RFC 6902 JSON Patch plus a replay fixture, re-validated in-worker. Returns PASS/FAIL/REVIEW with exact node and field, stable reason codes, and an evidence packet. n8n only. Put a JSON string in query: {"mode":"static|simulated|repair", ...}. Price by mode: static $0.10 (default if mode is missing or the JSON is unparseable), simulated $0.50, repair $2.00. The payment challenge reflects the mode you send. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | JSON string with mode and inputs. Example: {"mode":"static","workflow":{"nodes":[...],"connections":{...}}} | |
| context | No | Optional notes. Mode is read from query on this rail, not from context. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility for behavioral disclosure. It clearly states return values (PASS/FAIL/REVIEW with node and field, reason codes, evidence packet), pricing by mode, the default fallback, and the error behavior for missing payment signature. It does not explicitly state whether the tool is read-only or whether repair modifies resources, but the description of repair as producing an RFC 6902 JSON Patch implies it returns a patch rather than executing it, which is useful. Minor gap: no explicit mention of side effects or idempotency, but overall behavior is well disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is longer than average but every sentence carries meaningful information: purpose, modes, return format, scope restriction, input format, pricing, default behavior, and payment handling. It is structured logically and front-loaded with the core purpose. It could be slightly tightened, but it is not wasteful.
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 complexity (three modes, pricing, payment requirements, error handling, return format), the description covers everything an agent needs: how to call it, what each mode does, pricing implications, default behavior, and expected outputs. There is no output schema to rely on, so the description fully compensates by describing the return structure. Nothing critical is missing 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?
The schema already documents both parameters at 100% coverage, with the query parameter described as a JSON string with mode and inputs. The description adds significant value by providing an example structure, enumerating the modes, explaining the meaning of each mode, and clarifying that mode is read from query (not context). This goes beyond the basic schema and helps an agent construct correct inputs and choose the right mode.
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 'Validate and repair an n8n workflow JSON before production,' which states a specific verb (validate and repair), a specific resource (n8n workflow JSON), and the context (before production). It further distinguishes itself from siblings by explicitly limiting to n8n only and describing three distinct modes. No sibling tool even comes close to this functionality, so it is easily separable.
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 explicit when-to-use instructions: 'before production' and 'n8n only.' It details how to invoke each mode (static, simulated, repair) and includes a default mode if mode is missing. It also explains the payment challenge and the error returned without a payment-signature header. This is comprehensive guidance with clear conditions and no ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Turn one article into an X/Twitter thread of 5-10 posts. Use when X is the only target. For LinkedIn, email, and blog versions at once, use repurpose-long-form. URLs are not fetched; paste the text. Pay-per-call: $0.05 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | The article text (or its title if the text is in context). Example: Paste the article text here. | |
| context | No | Optional: the article text, or a desired angle. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully carries the behavioral disclosure burden. It reveals the pay-per-call cost ($0.05 USDC on Base via x402), the required payment-signature header, the error behavior when that header is missing, and the constraint that URLs are not fetched. These are non-obvious traits an agent must know before calling.
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, each earning its place: core action, routing guidance, input form, and payment/error behavior. The purpose is front-loaded and there is no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without an output schema or annotations, the description covers what an agent needs: target platform, output length, input requirements, payment, and error response. It doesn't explicitly state the return payload format, but for a content-generation tool this is a minor gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds a useful clarification about pasting text rather than URLs, which maps to the 'query' parameter, but it doesn't deeply extend the parameter semantics already present in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: 'Turn one article into an X/Twitter thread of 5-10 posts.' It also distinguishes itself from the sibling repurpose-long-form by naming the alternative and the condition ('X is the only target'), so an agent can select it correctly without inspecting either schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly says when to use the tool ('Use when X is the only target') and names the alternative for multi-format needs ('For LinkedIn, email, and blog versions at once, use repurpose-long-form'). It also gives a practical input instruction: 'URLs are not fetched; paste the text.'
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Verify a paid deliverable against its offer, original request, payment receipt, and acceptance rules. Returns ACCEPT/REJECT/REVIEW with stable reason codes, per-check booleans, content hashes, and an evidence packet. JSON-only checks: schema, required fields, freshness, source presence, receipt binding. Use in agent-to-agent purchases before releasing or accepting work. Not a quality review of prose. Pay-per-call: $0.10 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | JSON string with the deliverable, offer, request, receipt, and acceptance rules. Example: {"deliverable":{...},"offer":{...},"request":{...},"receipt":{...},"rules":{...}} | |
| context | No | Optional: extra acceptance rules as JSON. |
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 covers the output format (ACCEPT/REJECT/REVIEW with reason codes, booleans, hashes, evidence packet), the specific checks performed (schema, required fields, freshness, source presence, receipt binding), the limitation (not a quality review), and the payment requirement with an error behavior for missing payment headers. This is exceptionally 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?
The description is dense but well-structured. It front-loads the core purpose and return behavior, then adds checks, use case, and payment details in a logical order. While it is longer than minimal, every sentence contributes necessary information—no redundancy. It earns a 4 rather than 5 because it packs several clauses into single sentences, slightly reducing readability, but overall it is 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?
Given the tool's moderate complexity (2 parameters, no structured output schema, no annotations), the description is quite complete: it explains what it does, what it returns, the checks it performs, and the payment mechanism. It could be improved by specifying the required fields of the query JSON explicitly, but the schema example covers that. The absence of an output schema is compensated by a clear description of the return structure. Overall, it is close to 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 100% for both parameters: query and context have descriptive entries in the schema. The description adds no new parameter-specific meaning beyond what the schema already provides—it mirrors the schema's explanation of the JSON structure. The example in the schema is as informative as the description. Thus, a baseline score of 3 is appropriate; the description doesn't enhance parameter understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a precise verb and resource: 'Verify a paid deliverable against its offer, original request, payment receipt, and acceptance rules.' It clearly identifies the tool's function and distinguishes it from all sibling tools, which focus on analysis, generation, or research rather than verification. The return values are also explicitly listed, making the purpose 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?
The description explicitly states when to use the tool: 'Use in agent-to-agent purchases before releasing or accepting work.' It also provides a clear exclusion: 'Not a quality review of prose.' Even though no specific alternative tool is named, the context is sufficient because no sibling performs a similar function. This leaves no ambiguity about the appropriate use case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Disposable webhook test lab. capture: create a disposable HTTPS callback URL that records raw requests. retrieve: fetch captured requests. replay: send duplicate, out-of-order, delayed, malformed, or invalid-signature scenarios to your target URL. conformance: bounded webhook test battery with a machine-readable PASS/FAIL report. Use to test a webhook receiver before production. Put a JSON string in query: {"mode":"capture|retrieve|replay|conformance", ...}. Price by mode: capture/retrieve $0.05 (default if mode is missing or the JSON is unparseable), replay $0.10, conformance $0.50. The payment challenge reflects the mode you send. Without a payment-signature header the call returns an error whose data carries the payment terms.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | JSON string with mode and inputs. Example: {"mode":"replay","target_url":"https://your-app.example/webhook"} | |
| context | No | Optional notes. Mode is read from query on this rail, not from context. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and discloses substantial behavioral detail: capture creates a disposable URL, replay sends duplicate/out-of-order/delayed/malformed/invalid-signature requests to a target, prices differ by mode, and a missing payment-signature header returns an error carrying the payment terms. It does not cover retention or expiration of the disposable URL, but the most operationally important side effects are clearly 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?
The description is dense but scannable: it opens with a one-line purpose, uses a colon-delimited mode list, and then gives pricing, defaulting, and payment behavior in compact clauses. Every sentence contributes distinct operational information, and there is no filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a paid four-mode tool with no output schema and no annotations, the description covers the essential call flow: mode selection, example JSON, pricing, default mode, and the payment-signature error path. The main gap is that response structures for capture/retrieve/replay are not described, but the error-driven payment onboarding reduces practical risk for a first call.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3, and the tool description adds real value by defining the JSON key 'mode', its allowed values (capture|retrieve|replay|conformance), a concrete example, and an explicit warning that mode is read from query rather than context. It does not enumerate every per-mode required field, but the example and explanatory text compensate enough to score above baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The opening phrase 'Disposable webhook test lab' immediately identifies the resource, and each mode is defined with a specific verb and outcome: capture creates a URL, retrieve fetches requests, replay sends crafted scenarios, conformance runs a test battery. It also states the intended use, 'test a webhook receiver before production,' which distinguishes it clearly from the unrelated marketing/analysis sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives an explicit trigger condition, 'Use to test a webhook receiver before production,' plus concrete invocation instructions: put a JSON string in the query parameter with a mode. It also explains default behavior when mode is missing or unparseable. It does not name specific sibling alternatives or exclusion cases, but none of the sibling tools appears to cover webhook testing, so the omission is minor.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
analyze-competitors2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional: your own product description, so gaps are framed against it."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: AI agent marketplaces"New value: +"The product, category, or market (e.g. 'AI meeting note-takers for sales teams'). Example: AI meeting note-takers for sales teams"analyze-document2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional: the document text plus the reader's goal (e.g. 'investor memo - decide whether to follow up')."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: paste the document text or URL here"New value: +"The document text, or the question the analysis should answer. Example: Paste the document text here, plus the decision it should inform."analyze-email-deliverability2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional: audience, sending-domain age, or campaign type."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: paste the email subject and body here"New value: +"The email subject and body to audit. Example: Subject: Last chance - 50% off ends tonight\n\nHi there, ..."analyze-marketing-trends2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional: company stage, audience, or region."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: AI agent economy"New value: +"The industry or topic (e.g. 'B2B SaaS demand generation'). Example: B2B SaaS demand generation"analyze-review-velocity2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional: the product and the period to compare."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: Sep 3 review: 'Great tool, saved me hours every week' 5 stars. Sep 10: 'Export feature is buggy and support is slow' 2 stars. Sep 18: 'Export bug is fixed, works perfectly now' 4 stars. Sep 20: 'Best coding assistant I have used' 5 stars."New value: +"The reviews with dates and ratings. Example: 2026-09-01 5* 'Fast setup'\n2026-09-10 2* 'Support slow'\n..."analyze-sentiment2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional: what the text is about or who wrote it."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: Sep 3 review: 'Great tool, saved me hours every week' 5 stars. Sep 10: 'Export feature is buggy and support is slow' 2 stars. Sep 18: 'Export bug is fixed, works perfectly now' 4 stars. Sep 20: 'Best coding assistant I have used' 5 stars."New value: +"The text to classify. Example: Paste the reviews, comments, or post text here."analyze-seo-keywords2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional: audience, region, or site type to focus the keywords."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: AI agent tools"New value: +"The topic or seed keyword (e.g. 'n8n workflow automation'). Example: n8n workflow automation for agencies"check-email-deliverability2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional: sender type or audience (e.g. 'B2B cold outreach')."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: paste the email subject and body here"New value: +"The email subject and body to check. Example: Subject: Quick question about your onboarding\n\nHi Sam, ..."extract-pain-points2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional: the product or segment the text is about."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: Thread title: How is anyone billing for agent calls? dev_sam: I spent three weeks on Stripe metered billing instead of building my agent. agent_k: Every API wants a credit card form and an account, my agent cannot do either. ml_ruth: I gave up and made mine free, donations only."New value: +"The customer text to mine (reviews, tickets, notes). Example: Paste 10-50 customer reviews or support tickets here."generate-ab-test-variants2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional: the goal metric and current performance."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: headline and body copy for a pay-per-call API landing page"New value: +"The copy to vary (headline, CTA, email subject, etc.). Example: Headline: 'Get paid 2x faster'"generate-case-study-questions2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional: the results you want the case study to show."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: customer success story for an AI agent startup"New value: +"The customer and what they used the product for. Example: Freight broker that cut invoice delays using our tool"generate-cold-email2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional: prospect research, proof points, or tone."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: pitching x402 pay-per-call APIs to agent developers"New value: +"The product and the recipient type. Example: Invoice tool for freelance designers, to agency ops leads"generate-content-calendar2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional: goals, posting frequency, or key dates."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: AI agent economy publication"New value: +"The brand or topic and main channel(s). Example: B2B invoicing tool, LinkedIn + newsletter, October"generate-follow-up-sequence2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional: the first email's text, audience, and preferred spacing."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: following up with agent developers who tried the free tier"New value: +"The product and the original email or offer. Example: Follow-ups to a cold email offering a free invoice audit"generate-linkedin-post2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional: author role, audience, or a story to include."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: why AI agents need a payment rail"New value: +"The topic or point the post should make. Example: Why we stopped cold calling and what replaced it"generate-marketing-strategy2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional: budget, stage, current channels, or constraints."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: a pay-per-call API marketplace for AI agents"New value: +"The product and market (e.g. 'invoice tool for freelance designers'). Example: Invoice tool for freelance designers, $0 marketing budget"generate-positioning-statement2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional: key competitors or what makes it different."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: pay-per-call marketplace for AI agent tools"New value: +"The product and who it is for. Example: Invoice tool for freelance designers that auto-chases late payments"generate-show-notes2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional: guest name and bio details, links to include."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: Episode 42 transcript excerpt - Host: Why did HTTP 402 sit unused for 30 years? Guest: There was no payment rail machines could use. Cards need humans. Stablecoins settle in seconds for a fraction of a cent. Host: So an agent can just pay for an API call? Guest: That is the demo we just did - no account, no API key, one 402 challenge, one USDC payment."New value: +"The episode transcript or outline. Example: [00:00] Intro ... [04:12] Guest on pricing ..."generate-testimonial-questions2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional: the benefit you want quotes about."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: users of a pay-per-call agent API"New value: +"The product and the type of customer. Example: Invoice tool, freelance designer customers"generate-webinar-invite2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional: speakers, agenda, and registration link."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: webinar: monetizing AI agents with x402 micropayments"New value: +"The webinar topic, date, and audience. Example: Pricing teardown webinar, Oct 8 1pm ET, for SaaS founders"predict-viral-potential2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional: target platform and audience."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: headline: AI agents just got their own money"New value: +"The draft post text. Example: Paste the draft post here."repurpose-long-form2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional: brand voice or audience notes."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: paste the long-form content or URL here"New value: +"The long-form text to repurpose. Example: Paste the article, transcript, or report text here."research-competitor-pricing2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional: your planned price or tiers to compare against."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: AI writing assistants"New value: +"The product category (e.g. 'email warm-up tools'). Example: Email warm-up tools for small agencies"research-prospect2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional: what you sell, so the angle fits."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: a Series A devtools startup building AI agents"New value: +"The company name and, if known, its website domain or industry. Example: Acme Logistics (acmelogistics.com), mid-market freight broker"score-landing-page2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional: audience and the page's single goal."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: paste the landing page copy here"New value: +"The landing-page copy. Example: Paste the page headline, subhead, body, and CTA text here."summarize-document2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional: the full document text, or what the summary is for (audience, length)."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: https://gigsoul.com/articles/x402-protocol-programmable-ai-agent-payments"New value: +"The document text to summarize, or a short label if the text is in context. Example: Paste the article or document text here."test-and-repair-automation-workflow2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional notes. Mode is read from query on this rail, not from context."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: {\"mode\":\"static\",\"workflow\":{\"nodes\":[...],\"connections\":{...}}}"New value: +"JSON string with mode and inputs. Example: {\"mode\":\"static\",\"workflow\":{\"nodes\":[...],\"connections\":{...}}}"thread-from-article2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional: the article text, or a desired angle."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: https://gigsoul.com/articles/x402-protocol-programmable-ai-agent-payments"New value: +"The article text (or its title if the text is in context). Example: Paste the article text here."verify-deliverable-and-issue-acceptance-receipt2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional: extra acceptance rules as JSON."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: {\"offer\":{\"schema\":[\"summary\",\"sources\"]},\"request\":\"summarize X with sources\",\"payment_receipt\":{\"transaction\":\"0x...\"},\"deliverable\":{...},\"acceptance_rules\":[\"must cite sources\"]}"New value: +"JSON string with the deliverable, offer, request, receipt, and acceptance rules. Example: {\"deliverable\":{...},\"offer\":{...},\"request\":{...},\"receipt\":{...},\"rules\":{...}}"webhook-lab-capture-simulate-replay2 fields changedInput schema / properties / context / descriptionPrevious value: -"Optional supporting text or content to analyze"New value: +"Optional notes. Mode is read from query on this rail, not from context."Input schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: {\"mode\":\"capture\"}"New value: +"JSON string with mode and inputs. Example: {\"mode\":\"replay\",\"target_url\":\"https://your-app.example/webhook\"}"analyze-review-velocity1 field changedInput schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: product reviews for an AI coding assistant"New value: +"The question or input for this tool. Example: Sep 3 review: 'Great tool, saved me hours every week' 5 stars. Sep 10: 'Export feature is buggy and support is slow' 2 stars. Sep 18: 'Export bug is fixed, works perfectly now' 4 stars. Sep 20: 'Best coding assistant I have used' 5 stars."analyze-sentiment1 field changedInput schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: customer reviews of an AI agent platform"New value: +"The question or input for this tool. Example: Sep 3 review: 'Great tool, saved me hours every week' 5 stars. Sep 10: 'Export feature is buggy and support is slow' 2 stars. Sep 18: 'Export bug is fixed, works perfectly now' 4 stars. Sep 20: 'Best coding assistant I have used' 5 stars."extract-pain-points1 field changedInput schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: forum thread about developers struggling to monetize AI agents"New value: +"The question or input for this tool. Example: Thread title: How is anyone billing for agent calls? dev_sam: I spent three weeks on Stripe metered billing instead of building my agent. agent_k: Every API wants a credit card form and an account, my agent cannot do either. ml_ruth: I gave up and made mine free, donations only."generate-show-notes1 field changedInput schema / properties / query / descriptionPrevious value: -"The question or input for this tool. Example: episode about the x402 payment protocol"New value: +"The question or input for this tool. Example: Episode 42 transcript excerpt - Host: Why did HTTP 402 sit unused for 30 years? Guest: There was no payment rail machines could use. Cards need humans. Stablecoins settle in seconds for a fraction of a cent. Host: So an agent can just pay for an API call? Guest: That is the demo we just did - no account, no API key, one 402 challenge, one USDC payment."check-email-deliverabilityanalyze-email-deliverabilitygenerate-ab-test-variantsscore-landing-pageanalyze-competitorsanalyze-documentanalyze-marketing-trendsanalyze-review-velocityanalyze-sentimentanalyze-seo-keywordsextract-pain-pointsgenerate-case-study-questionsgenerate-cold-emailgenerate-content-calendargenerate-follow-up-sequencegenerate-linkedin-postgenerate-marketing-strategygenerate-positioning-statementgenerate-show-notesgenerate-testimonial-questionsgenerate-webinar-invitepredict-viral-potentialrepurpose-long-formresearch-competitor-pricingresearch-prospectsummarize-documenttest-and-repair-automation-workflowthread-from-articleverify-deliverable-and-issue-acceptance-receiptwebhook-lab-capture-simulate-replay270 pay-per-call AI tools for agents, paid via x402 (USDC on Base). No signup, no API keys.
63 pay-per-call tools for agents: vision, text, data, web, blockchain. USDC on Base via x402.
x402-paid agent tools: 18 over HTTP, 14 over stdio. USDC per call, no API key.
Pay-per-call tools for autonomous agents, settled in USDC on Base via x402.
Add one secure layer between your agents and this server.