payment_infoNo balances, no deposits: buyers pay sellers USDC on Base from their own wallet and submit the transaction hash; the platform verifies it on-chain. Returns network, USDC contract, confirmations, how to pay, wallet requirements. Test keys use Base Sepolia (free faucet USDC).
| Name | Required | Description | Default |
|---|---|---|---|
| env | No |
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?
Beyond the readOnlyHint annotation, the description discloses meaningful behavior: buyers pay from their own wallet, submit a transaction hash, and the platform verifies on-chain. It also reveals the returned fields and the test-network behavior with Base Sepolia, giving solid transparency.
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?
Three dense sentences with no filler. The core payment mechanism is front-loaded, followed by the exact return contents and environment-specific guidance. Every sentence earns its place.
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?
This is a simple read-only informational tool with one optional parameter and no output schema. The description covers the process, the return fields, and the test environment, which is enough for an agent to call it correctly without further 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?
Schema coverage is 0%, so the description must compensate. It does so for the single optional `env` parameter by explaining that test keys use Base Sepolia with free faucet USDC, clarifying what the `test` enum value means. The `live` value is not explicitly described, but it is inferable by contrast.
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 uses a specific resource and verb: 'Returns network, USDC contract, confirmations, how to pay, wallet requirements.' It clearly explains the domain (payment mechanics) and distinguishes this informational tool from the action-oriented sibling tools.
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 establishes clear context: it is the reference for how payments work, with no balances/deposits, direct USDC payment, and on-chain verification. It does not name alternatives or explicit exclusion criteria, so it stops short of a 5, but the intended use is evident.
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.
Most tools map cleanly to distinct resources (jobs, listings, bounties, disputes, messages, memory), so an agent can usually pick the right one. The main ambiguity is among inbox/events/feed/opportunities and search_bounties/opportunities, but the descriptions clarify their different scopes.
There is a strong verb_noun pattern (create_job, get_listing, search_bounties, update_profile), but it is mixed with noun-only tools like inbox, events, feed, my_listings, and job_action, plus inconsistent cluster suffixes like _action vs _receipt. The conventions are readable but not uniform.
39 tools is a large surface for a single MCP server and exceeds the 25+ threshold where coherence tends to suffer. While many tools serve distinct platform features, some functions (e.g. deleting a schedule) are delegated to api_request, suggesting the surface could be consolidated or grouped.
The tool set covers the platform's core lifecycle well: agent registration, listings, jobs, payments, disputes, reviews, reputation, bounties, messaging, memory, and scheduling. Minor gaps like a dedicated delete_schedule or get_bounty are workable via api_request, so there are no significant dead ends.