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Licence audit, page read and endpoint check (AI-operated)

get_payment_terms

Returns what the paid operations cost, on which chains, and exactly how to pay: a standard x402 EIP-3009 authorization on Base is verified and settled through the facilitator.xpay.sh facilitator in the same request (no gas for the buyer, transaction hash returned), and a transaction the buyer broadcast themselves is accepted too. Also returns the free worked examples.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the behavioral burden. It explicitly says 'Returns' and describes the content, indicating a read-only operation. It also discloses the return includes payment instructions and examples, but it does not explicitly state 'no side effects' or 'read-only'. However, the verb 'Returns' strongly implies a safe, non-mutating operation. The description is transparent about what the tool returns, which is the primary behavioral trait.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is moderately long but well-structured: it opens with the primary return value (costs, chains, payment methods), then details the payment mechanisms, and ends with examples. It is not overly verbose, but some sentences are dense. The key information is front-loaded, and every clause adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Since there is no output schema, the description must fully explain the return content. It does so: it specifies what is returned (costs, chains, payment methods, examples), the exact payment processes (facilitator-based x402 with no gas and transaction hash, or self-broadcast transaction), and the fact that free worked examples are included. For a parameterless tool, this is complete and sufficient for an agent to understand and invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has no parameters, so schema coverage is trivially 100%. The description adds no parameter-specific information, but none is needed. Baseline for 0 parameters is 4, which is appropriate because the tool's behavior is fully described without parameter details.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns payment terms: costs, chains, payment methods (x402 EIP-3009 via facilitator or self-broadcast transaction), and free worked examples. It is specific and distinct from sibling preview tools (endpoint_check_preview, licence_audit_preview, page_read_preview) which are all about checking or reading previews, not payment information.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for retrieving payment terms and examples, but it does not explicitly state when to use this tool over others or mention alternatives. Since siblings are semantically different, the usage context is clear by content, but no explicit guidance or exclusion is provided. An agent would infer based on the topic, but it lacks direct routing instructions.

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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