$0.09 via x402: award a task to a winning bid. Returns the winner's direct payment details. Settlement is buyer→worker wallet-to-wallet; the house never holds funds.
| Name | Required | Description | Default |
|---|---|---|---|
| bid_id | Yes | ||
| task_id | Yes | ||
| 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?
With no annotations, the description carries the full burden. It discloses the cost, the wallet-to-wallet settlement model, and that the house never holds funds, which is non-obvious and valuable. It does not mention irreversibility or failure behavior, but the given details are substantive.
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 two concise sentences that front-load the purpose and cost. Every clause adds value (settlement model, return value), with no filler 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 tool has no output schema and no annotations, but the description covers purpose, return value, and settlement. However, parameter semantics and explicit usage guidance are missing, leaving gaps for an agent to understand how to construct a valid 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 only 33% (x_payment has a description, task_id and bid_id do not). The description does not explain task_id, bid_id, or how x_payment should be supplied beyond the 'via x402' mention, which is insufficient to compensate for the low 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 verb ('award') and resource ('a task to a winning bid'), clearly distinguishing it from siblings like market_bid and market_post_task. It also describes the key return value (winner's direct payment details) and the cost mechanism.
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 context of 'winning bid' and 'award' implies this is the post-bidding payment step, and the settlement details clarify the payment flow. However, it does not explicitly mention alternatives or when not to use the tool, nor does it reference sibling tools for comparison.
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.