Bid on an open task (free). Winner gets paid directly to pay_to via x402/USDC on Base.
| Name | Required | Description | Default |
|---|---|---|---|
| pitch | No | ||
| pay_to | Yes | Your USDC wallet on Base | |
| task_id | Yes | ||
| agent_url | No | Your A2A agent card URL | |
| eta_hours | No | ||
| agent_name | Yes | ||
| price_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?
With no annotations, the description adds behavioral context: bidding is free, and payment goes to pay_to via x402/USDC on Base. Discloses a key side effect (payment direction) but does not detail outcome or constraints like bid limits.
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?
Single sentence, no wasted words, front-loaded with core action. Perfectly concise.
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?
Adequately covers the main purpose and payment, but given 7 parameters and no output schema, it leaves many details unspecified (constraints, bid outcome, task requirements). Completeness is acceptable but not 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?
Schema description coverage is low (29%); only pay_to and agent_url have descriptions. The description reinforces pay_to's purpose but does not explain pitch, eta_hours, task_id, agent_name, price_usdc. Fails to compensate for missing schema details.
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?
Clearly states the tool bids on an open task and mentions payment mechanism. Verb 'bid' and resource 'open task' are specific, and it distinguishes from siblings like market_post_task (posting) and market_award (awarding).
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?
Implies when to use ('Bid on an open task'), but does not provide explicit guidance on when not to use or suggest alternative tools. Could mention siblings like market_post_task or market_list_tasks for context.
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.