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Glama

Riley Craig x402 Agent Store

by io.github.rccola990-cloud

chain_confirmations

$0.09 via x402: did this transaction land, how deep is it buried, and is it safe to treat as FINAL on this chain. Returns status, block, tip, confirmations, gas used, fee paid and log count, compared against the chain's own finality threshold (Ethereum 64, Polygon 128, OP-stack 60). A success with two confirmations is not a settled payment. Also distinguishes 'no receipt yet' from 'failed' — conflating those is how agents double-spend.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hashNoTransaction hash (0x + 64 hex)
chainNobase|ethereum|optimism|arbitrum|polygon|gnosis (default base)
x_paymentNo

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?

With no annotations provided, the description takes on full disclosure duty. It reveals cost ($0.09 via x402), return fields, chain-specific finality thresholds, and the critical distinction between 'no receipt yet' and 'failed'. It warns about double-spending risks, adding valuable behavioral context beyond simple operation.

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 concise yet information-dense: it opens with the core purpose, lists the return fields, provides specific threshold examples, and closes with a critical safety warning. No wasted sentences; structure is logical and easy to scan.

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?

Despite lacking output schema and annotations, the description provides a comprehensive picture: what it does, what it returns, how to interpret results, and why it matters (preventing double-spends). It covers edge cases and gives sufficient context for an agent to decide when and how to use the tool.

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?

Schema coverage is 67%, and the description adds contextual value by explaining the x402 payment cost (relevant to x_payment), listing chain-specific finality thresholds (relevant to chain), and implying the output meaning of hash. It does not exhaustively detail each parameter but compensates with semantic insight.

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's function: checking transaction confirmations, depth, and finality safety. It uses a specific verb ('land', 'buried') and resource ('transaction on this chain'), and distinguishes from generic status tools by focusing on finality thresholds and double-spend risk.

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

Usage Guidelines4/5

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

The description provides clear context on when to use this tool (to determine if a payment is final/settled) and gives interpretative guidance (e.g., two confirmations are not settled). However, it does not explicitly name alternative tools or state when not to use it, so it misses the 'exclusions/alternatives' level of a 5.

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

C2.7/5.0
Disambiguation2/5

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.

Naming Consistency2/5

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.

Tool Count1/5

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.

Completeness3/5

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.

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