payment_info
How to pay, step by step. Read this if you got a 402 and are not sure what to do.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
How to pay, step by step. Read this if you got a 402 and are not sure what to do.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 burden of behavioral disclosure. It clearly implies a non-mutating, informational role ('How to pay, step by step'), which is transparent enough. It does not explicitly confirm there are no side effects, but the phrasing strongly suggests it is a guide rather than a payment execution.
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, front-loaded sentence that states the purpose and usage condition without any filler. Every word 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?
For a zero-parameter, no-output-schema informational tool, the description fully covers purpose and usage. It even includes the contextual trigger (402 error), making it complete for an agent to decide when to invoke it.
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?
The tool has zero parameters, so the baseline is 4. The description adds no parameter-specific details, but none are needed since the schema is empty.
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 provides step-by-step payment instructions, with a specific trigger ('if you got a 402'). This distinguishes it from sibling tools like health or inspect_wallet, which address different concerns.
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 gives an explicit condition for use: 'Read this if you got a 402 and are not sure what to do.' While it does not name alternatives or when-not-to-use scenarios, the context is specific enough to guide an agent.
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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Each tool has a clearly distinct purpose: wallet inspection, token screening, transaction simulation, and fee estimation are separate paid operations, while health, service_info, payment_info, and request_capability serve distinct onboarding/payment/feedback roles. There is no realistic confusion between any two tools.
All names are lowercase snake_case and the paid operational tools consistently use verb_noun patterns like inspect_wallet, screen_token, and simulate_transaction. The informational tools break that pattern slightly with health, payment_info, and service_info, but the split is predictable and readable.
Eight tools is well-scoped for a Solana risk-assessment API: four paid data operations plus four supporting meta tools. Each tool earns its place without bloating the surface or duplicating functionality.
The core read-only workflows are well covered: wallet profiling, token risk screening, transaction simulation, and priority fee estimation, with payment and service docs included. Minor gaps exist such as raw transaction history or token metadata beyond risk screening, but agents can work around these for the stated purpose.