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Riley Craig x402 Agent Store

by io.github.rccola990-cloud

chain_transaction_explain

$0.09 via x402: decode any transaction by hash into a plain-English explanation — what actually happened (a swap, transfer, approval, mint, or contract call), who the actors are, and the full list of asset changes (every native coin and token in/out with symbol, amount and direction), plus status, fee and the decoded method. The read agents make to UNDERSTAND a tx, not just confirm it landed: for wallet UX, accounting, risk, dispute and trading agents. Across Base, Ethereum, Optimism, Arbitrum, Polygon, Gnosis. Live from Blockscout; one paid call instead of running your own indexer + ABI decoder.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hashYesTransaction 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
  2. Removed
  3. Added
  4. Removed
  5. First observed

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations provided, the description carries the burden and does a solid job: it discloses the cost ($0.09 via x402), the live data source (Blockscout), and the read-only nature ('read agents'). It does not explicitly say 'does not modify state' or mention auth/error behavior, but the intent is clear and there is no contradiction.

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 dense but uses every clause for useful information. The first sentence is a long run-on but front-loads the core purpose. Subsequent sentences add use cases, chains, and value proposition. It is slightly overloaded but well-structured for an AI agent that needs rich context.

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?

Given there is no output schema, the description fully explains what the tool returns: plain-English explanation, list of asset changes, status, fee, and decoded method. It also covers supported chains and the use case rationale, making it complete for a complex tool with no structured output definition.

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?

Schema coverage is 67%: hash and chain have good descriptions, but x_payment is undocumented. The description adds context by mentioning the cost and 'via x402,' which implies the payment parameter's purpose, but it doesn't explicitly tie this to x_payment. Overall, it adds some meaning beyond the schema but not completely.

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: 'decode any transaction by hash into a plain-English explanation,' then enumerates concrete outputs (swap, transfer, approval, asset changes, status, fee). It also differentiates from sibling tools by saying 'not just confirm it landed,' explicitly contrasting with chain_transaction_status.

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 use this tool: 'The read agents make to UNDERSTAND a tx, not just confirm it landed: for wallet UX, accounting, risk, dispute and trading agents.' It also names the alternative (chain_transaction_status) by implication and lists supported chains, providing clear context without ambiguity.

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.

Resources