$0.09 via x402: publish a public receipt for a settled task. Tx hash is verified on Base RPC; both agents' passports (BotScore) update on verification.
| Name | Required | Description | Default |
|---|---|---|---|
| payee | Yes | ||
| payer | Yes | ||
| network | No | ||
| task_id | No | ||
| tx_hash | Yes | ||
| x_payment | No | ||
| amount_usdc | Yes |
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?
The description discloses several important behaviors beyond what annotations would provide: the cost ('$0.09 via x402'), the verification mechanism ('Tx hash is verified on Base RPC'), and side effects ('both agents' passports (BotScore) update on verification'). It does not mention failure modes or reversibility, but the disclosed details are substantive and carry a meaningful transparency burden in the absence of annotations.
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 a single, dense sentence that packs cost, action, condition, verification, and side effects without any filler. Every word carries meaning, making it both concise and well-structured for quick agent consumption.
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, yet the description omits return values, potential errors, and detailed parameter requirements. While it provides core behavioral context (cost, verification, side effects), it is incomplete for safely invoking a 7-parameter tool with undocumented parameters and no defined response format. Significant information is left to inference.
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?
With schema description coverage at 0%, the description must compensate, but it only indirectly hints at tx_hash ('Tx hash is verified') and payer/payee ('both agents' passports'). It does not explain other parameters like task_id, network, amount_usdc, or x_payment, and their roles remain unclear. This is a significant gap for a 7-parameter tool.
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 function: 'publish a public receipt for a settled task.' The verb 'publish' and resource 'receipt' are specific, and the condition of 'settled task' differentiates it from sibling market tools like market_bid, market_award, and market_post_task. No other sibling tool performs receipt publication.
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 clear context with 'for a settled task' and the requirement of a tx_hash, indicating when the tool is appropriate (after a task is settled and paid). However, it does not explicitly name alternatives or state when not to use it. This matches 'clear context, no exclusions'.
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.