Developer kits and design packs for AI agents, paid in USDC over x402. Free API readiness check.
Scored across 4 tools
Each tool has a clearly distinct purpose: buy_product handles purchases, check_agent_readiness scores API specs, get_product retrieves a single product, and list_products lists all products. There is no overlap or ambiguity between them.
All tool names follow a consistent verb_noun pattern in snake_case: buy_product, check_agent_readiness, get_product, list_products. The convention is predictable and easy to understand.
With only 4 tools, the set is well-scoped for a small store that sells products and offers a complementary API readiness checker. Each tool earns its place without redundancy.
The core buyer workflow is covered: list products, get product details, and buy a product. A minor gap exists for retrieving previously purchased items or order status, but the immediate return of the product upon purchase largely mitigates this.
Buy a product with USDC over x402. Without a payment it returns the x402 payment requirements; with one it settles the payment and returns the product (for kits, a personal download link valid for 24 hours).
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Product slug from list_products | |
| account | No | For dashboard-pro only: the dashboard account id the pass is for |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare this is a non-readonly, open-world, non-idempotent mutation, so the safety profile is covered. The description adds genuinely useful behavior beyond that: the unpaid call returns payment requirements rather than erroring, settlement occurs on payment, and kit purchases yield a download link valid only 24 hours. It does not discuss retry/idempotency implications for a non-idempotent purchase, which is the main remaining gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two tightly packed sentences with no filler; the core action and the payment-dependent branching are front-loaded, and the 24-hour download caveat is placed exactly where it matters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description correctly covers the return values for both modes (payment requirements vs. product/download link). For a payment-settling tool it is largely sufficient, though it omits any note on failure handling or what happens to a partially completed payment.
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 slug and account documented in the schema itself (including the dashboard-pro-only condition on account). The description adds no parameter-level detail, so the baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb and resource ('Buy a product') and adds the payment mechanism (USDC over x402), which clearly separates it from read-oriented siblings like get_product and list_products. It stops short of explicitly naming an alternative sibling, so it is clear but not fully differentiated by name.
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 concrete context for both invocation modes: without a payment it returns x402 payment requirements, with one it settles. That effectively tells the agent the two-step flow to expect, though it never states exclusions or points to a sibling tool for non-purchase lookups.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Scores an API 0-100 for how well AI agents will use it as tools, from its OpenAPI 3.0 or 3.1 spec: operationIds, summaries, descriptions, parameter descriptions, enums, examples, JSON bodies, response and error schemas, pagination, auth, idempotency and tool count (the Agentify API kit's rules). Give the spec's https URL or the spec itself. Free; returns the score, each rule's result with how to fix it, and the most costly findings. Use it before turning an API into an MCP server or listing it for agents.
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | https URL of the OpenAPI document (JSON or YAML), e.g. https://api.example.com/openapi.json | |
| spec | No | The OpenAPI document itself, as JSON or YAML text (up to 2 MB). Send url or spec, not both. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive, and openWorld behavior. The description adds that the tool is 'Free' and returns 'the score, each rule's result with how to fix it, and the most costly findings'—useful cost and output context beyond the annotations. It does not mention rate limits or auth requirements, so a 4 rather than 5.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Front-loads the core purpose, then efficiently covers input, output, cost, and usage in four tight sentences. Every sentence carries distinct information, with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no output schema and full annotation coverage, the description supplies all needed context: what it does, how to supply input, what it returns, that it is free, and when to use it. Nothing material 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 the schema already documents both parameters thoroughly, including mutual exclusivity ('Send url or spec, not both'). The description's 'Give the spec's https URL or the spec itself' adds only slight emphasis and no syntax or format details beyond the schema, fitting the baseline 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Specific verb 'Scores' with resource 'API' and precise scope: '0-100 for how well AI agents will use it as tools, from its OpenAPI 3.0 or 3.1 spec.' Lists the evaluated criteria and the source of rules, making the tool's function unmistakable even among unrelated siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use it: 'Use it before turning an API into an MCP server or listing it for agents.' Also tells the agent how to provide input ('Give the spec's https URL or the spec itself'). No when-not-to-use or alternative tools are mentioned, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
One product by slug, with its price and payment options.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Product slug, e.g. "starter-kit" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds useful context about return content (price and payment options), but says nothing about missing-slug behavior, auth needs, or how it relates to buy_product. With annotations doing the heavy lifting, a 3 is appropriate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence with zero waste, front-loading the resource and scope before the return contents. Nothing is redundant or padded.
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 one-parameter read tool with no output schema, the description covers the essentials and partly compensates for the missing output schema by naming the return fields. It stops short of noting not-found behavior or the relationship to buy_product.
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 the single slug parameter is documented with an example ("starter-kit"), so the baseline is 3. The description adds no format, casing, or lookup semantics beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb (get) and resource (one product by slug), and the word "One" implicitly contrasts with the list_products sibling. It does not explicitly name or differentiate against buy_product or list_products, so it falls short of a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is only implied by "One product by slug" – an agent can infer this is the single-item lookup rather than a list or purchase. There is no explicit when-to-use, when-not-to-use, or named alternative such as list_products or buy_product.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Everything Latitude 10 sells to agents, with prices and how to pay: developer kits, and design packs (DESIGN.md design systems for fintech dashboards, developer tools, and editorial sites; $5 per project). Check it before building the UI of a new project.
| Name | Required | Description | Default |
|---|---|---|---|
| framework | No | Only the kits for this backend: a stack (node, python, dotnet, java, ruby) or a framework (express, nextjs, django, fastapi, aspnet-core, spring-boot, rails…) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description usefully adds the commercial dimension ($5 per project, how to pay) but says nothing about return format, pagination, or result size for a catalog listing.
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 practical guidance is front-loaded, but the first sentence is a long run-on cramming categories, a parenthetical example list, and pricing into one breath. The nested parentheses make it harder to scan than it needs to be.
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 zero-required-parameter read tool with no output schema, the description conveys what is returned at a conceptual level (products, prices, payment) and when to consult it. Return-shape specifics are the only real 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% and the single 'framework' parameter is fully documented in the schema with examples, so the baseline is 3. The description mentions kits and design packs but adds no additional meaning about the framework filter.
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 concretely what the catalog contains — developer kits and design packs with prices and payment methods — which is far more specific than the tool name alone. It does not explicitly distinguish itself from the sibling get_product (single product) or buy_product (purchase), leaving that differentiation implicit in the plural 'everything Latitude 10 sells' framing.
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?
'Check it before building the UI of a new project' gives a clear triggering context for calling this tool. However, it names no when-not condition and does not point to get_product or buy_product as the follow-up alternatives.
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.
list_products3 fields changedInput schema / $schemaAdded value: +"http://json-schema.org/draft-07/schema#"Input schema / additionalPropertiesAdded value: +falseInput schema / properties / frameworkAdded value: +{
+ "description": "Only the kits for this backend: a stack (node, python, dotnet, java, ruby) or a framework (express, nextjs, django, fastapi, aspnet-core, spring-boot, rails…)",
+ "type": "string"
+}buy_productcheck_agent_readinessget_productlist_productsGive your AI agent an x402 wallet: discover and pay for services in USDC, or earn from your own.
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