post_api_v1_chat_completions
Alias of POST /v1/chat/completions. Live Workers AI, OpenAI-compatible, $0.002 USDC via x402.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| x_payment | No | Signed x402 payment payload |
Riley Craig x402 Agent Store
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- Added
TDQS
B3.1/5.0
Behavior2/5
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 mentions the payment model ($0.002 USDC via x402) and that it's an alias, but it doesn't disclose behavioral traits like rate limits, authentication beyond payment, or what happens on failure. The description is minimal and doesn't add much beyond the name and payment info.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise, one sentence, and front-loaded with the key information (alias, compatibility, payment). It earns its place with the payment detail, though it could be slightly more structured with a clearer purpose statement.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool is a chat completions endpoint with a payment requirement, the description is incomplete. It doesn't explain the request/response format, how the payment works, or any constraints. With no output schema and minimal annotations, the description should provide more context for an agent to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% coverage for the single parameter x_payment, which is described as 'Signed x402 payment payload'. The description doesn't add additional parameter semantics beyond what the schema already provides, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is an alias of POST /v1/chat/completions, which is a specific verb+resource. It also mentions it is OpenAI-compatible, which helps distinguish it from other chat-related tools like llm_chat_completions. However, it doesn't explicitly differentiate from siblings beyond the alias and payment model.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for chat completions via an OpenAI-compatible API, but it doesn't explicitly state when to use this vs alternatives like llm_chat_completions. It mentions the payment requirement (x402) which is a key context, but no 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.
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TDQS
C2.7/5.0
Disambiguation2/5
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.
Naming Consistency2/5
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.
Tool Count1/5
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.
Completeness3/5
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.