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Riley Craig x402 Agent Store

by io.github.rccola990-cloud

chain_l1_data_fee

$0.09 via x402: the L1 data-availability fee an OP-stack transaction pays ON TOP of L2 execution gas — usually the larger half of the bill, and the half ordinary gas estimators leave out. Answered by the chain's own GasPriceOracle.getL1Fee rather than a reimplemented formula, so it stays correct across Ecotone, Fjord and later forks. Pass data=0x... to price your actual transaction exactly. Base and Optimism only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNo0x calldata of your actual transaction — priced exactly
bytesNoTransaction size in bytes, 1-200000 (default 1200)
chainNobase|optimism (OP-stack only)
x_paymentNo

Schema Changelog

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

  1. Added

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries the burden. It discloses that it uses the chain's GasPriceOracle.getL1Fee rather than a reimplemented formula and remains correct across forks. However, it does not state read-only/idempotent behavior, auth needs, or output units, leaving notable gaps.

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?

Two dense sentences front-load the core fee concept and chain scope. Every clause adds context, with no filler.

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

Completeness4/5

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

The description covers purpose, usage, key parameters, behavioral source, and limitations. It does not specify the output format/units or behavior when both data and bytes are supplied, but for a low-complexity tool with no output schema it is largely complete.

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 75%, with data/bytes/chain already well described. The description reinforces exact data pricing and chain scope but adds little over the schema. The x_payment parameter is left undescribed in both the schema and the description.

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 identifies the specific resource: the L1 data-availability fee for an OP-stack transaction, contrasting it with ordinary gas estimators. This clearly distinguishes it from sibling tools like chain_gas_price and chain_estimate_gas.

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?

It says to use this when you need the L1 DA fee component that ordinary gas estimators omit, and instructs passing data=0x for exact pricing. It also states Base and Optimism are the only supported chains. It does not name sibling alternatives explicitly, but the context is sufficient.

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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