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Glama

Pronto

by stream.pronto.api

settle_payment

Idempotent

Redeem a payment you have made and activate your plan. Step two of two, after open_payment and the transfer.

The payment is verified by reading Base: the transfer must have reached the settlement address, from the wallet this purchase was opened with, for at least the amount owed, and be five blocks deep. A payment that is real but not yet deep enough returns a pending error naming that — the money has moved, so call this again with the same hash rather than paying twice. One transaction settles one subscription.

On success your existing API key resolves to the new plan with nothing to change on your side: you become your own principal, and no person holds the subscription.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
transaction_hashYesThe Base transaction hash of your USDC transfer: 0x followed by 64 hex characters.

Schema Changelog

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

  1. Added

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the annotations, the description discloses concrete verification criteria (settlement address, originating wallet, amount owed, five blocks deep), the pending-error behavior, that one transaction settles one subscription, and the success outcome for the API key. None of this contradicts the readOnlyHint=false, openWorldHint=true, or idempotentHint=true annotations.

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

Conciseness5/5

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

The description is structured in three tight paragraphs: what the tool does, how verification works and what to do on pending, and what happens on success. Each sentence carries distinct operational information, and the highest-level purpose is front-loaded.

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?

For a one-parameter mutation tool with no output schema, the description is complete: it explains prerequisites, verification rules, error behavior, retry semantics, cardinality, and postconditions. An agent has enough context to invoke it correctly and to distinguish a pending outcome from a failure.

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

Parameters3/5

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

The input schema already provides 100% coverage for the single parameter, including the exact format ('0x followed by 64 hex characters'). The description reinforces that the same hash should be reused on retry, but adds no new syntactic meaning beyond what the schema already documents.

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 opens with a specific verb and resource: 'Redeem a payment you have made and activate your plan.' It also clarifies its place in a workflow as 'Step two of two, after open_payment and the transfer,' which clearly distinguishes it from sibling tools like open_payment.

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

Usage Guidelines5/5

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

The description states exactly when to call this tool: only after open_payment and the transfer, and when the transfer has reached the settlement address at sufficient depth. It also gives explicit retry guidance by telling users to call it again with the same hash instead of paying twice when a pending error is returned.

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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TDQS

A4.6/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: get_signal re-reads a known row, get_latest_signals fetches by time, scope_signals filters by facets, search_signals matches words, and get_related_signals follows links. The overlapping pairs like get_facet_manifest/list_facets and get_fused_signal/list_fusion_products are explicitly differentiated in their descriptions, so an agent should not confuse them.

Naming Consistency5/5

The naming follows a consistent snake_case verb_noun pattern: get_ for direct fetches, list_ for catalog-style enumeration, register_ for identity creation, and scope_/search_ for query actions. The slight difference between get_fused_signal and list_fusion_products is meaningful and the verbs remain predictable.

Tool Count5/5

Thirteen tools is well within the sweet spot and each one covers a distinct capability: live reads, lookup by id, lexical search, facet filtering, related signals, fused products, catalogues, plans, billing, and agent registration. There is no obvious padding or excessive fragmentation.

Completeness5/5

The surface fully covers the domain: discovering the vocabulary, selecting signals, searching, fetching by id, following relationships, computing derived products, listing sources, and checking billing/plans. The only gaps would be account claiming and credential rotation, but those are explicitly deferred to external parties, so they are not tool-set gaps.

Resources