One-time $19 USDC purchase of a 1,000-call API key. Settles to the store payTo on Base via x402 (coinbase-cdp). No Stripe, no new wallet. Retry this GET with X-PAYMENT; a paid request returns ak_live_....
| Name | Required | Description | Default |
|---|---|---|---|
| x_payment | No | Signed x402 payment payload |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
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 reveals key behaviors: the tool is a GET that requires a payment payload, it settles on Base via x402, and it returns an 'ak_live_...' key upon paid request. It also implies mutating behavior (purchase) and payment handling. However, it omits what happens on failed payment or how retries work exactly, but the info provided is substantial.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, packed with essential information in two short paragraphs. Every sentence adds value: the offer, the settlement, the retry mechanism, and the return format. 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 simple purchase tool with one parameter and no output schema, the description is quite complete: it specifies the price, credits, settlement method, payment mechanism, and response format. It doesn't explain failure modes or payment validation details, but given the simplicity and lack of annotations, it's reasonably thorough.
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 description mentions 'Retry this GET with X-PAYMENT; a paid request returns ak_live_....', which explains the purpose of the x_payment parameter. However, the schema description for x_payment already provides info ('Signed x402 payment payload'), and the description doesn't add much beyond that. Since schema coverage is 100% and there's only one parameter, 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 clearly states the tool's purpose: a one-time purchase of a 1,000-call API key for $19 USDC, settling to store payTo on Base. It uses a specific verb (purchase) and resource (API key), and distinguishes itself from sibling tools like 'store_catalog' and 'market_post_task' by specifying the payment method and settlement details.
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 explains when to use this tool: for a one-time purchase of a 1,000-call API key. It also provides clear guidance on the retry mechanism with X-PAYMENT header, and mentions that no Stripe or new wallet is needed, which sets expectations for usage. It effectively guides the agent on the process, distinguishing this from other purchase or payment tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many tools occupy the same conceptual space: web_scrape vs markdown_web_scraper, post_check vs brand_ai_visibility_check, llm_chat_completions vs post_api_v1_chat_completions, chain_transaction_status vs chain_confirmations, and connect_token vs token_security_check + dex_token_data. Descriptions help in places, but for an agent facing 92 tools these near-overlapping endpoints will frequently cause misselection.
Everything is snake_case, but the conventions diverge sharply: get_chain_* and chain_* coexist for the same RPC family, post_* names are HTTP-route artifacts, api_generate reverses noun_verb order, and many names are bare nouns rather than verb_noun. There is no predictable naming pattern an agent can rely on.
At 92 tools this is far beyond the range where an agent can keep the surface coherent, even for a store. The flat tool list mixes products, bundles, aliases, proxies and single-use verticals, so most of the count is noise for any given task. A catalog/search/payment model with fewer exposed tools would fit the storefront purpose better.
The server has impressive breadth and covers key storefront/market workflows: catalog, samples, credits, directory listing, notary, and the task lifecycle. But each domain is shallow: there is no chain transaction broadcast, no task update/cancel/dispute, no AI-visibility history, and many verticals are a single tool with no follow-on operation. The surface is broad but not deeply complete.