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Riley Craig x402 Agent Store

by io.github.rccola990-cloud

wallet_portfolio

$0.09 via x402: any wallet's token portfolio — every ERC-20 holding with amount, live USD price and value, portfolio %, total value, top-position concentration, and spam/unpriced filtering. The 'bagcheck' call for copy-trading, whale-watching, risk and research agents. Chains: base, ethereum, optimism, arbitrum, polygon. Live from Blockscout.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chainNobase|ethereum|optimism|arbitrum|polygon (default base)
walletYesWallet address (0x...)
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

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden. It adds meaningful operational context: cost via x402, live data, spam/unpriced filtering, supported chains, and Blockscout as the source. It does not cover failure modes or rate limits, but for a read-only portfolio query the disclosed behavior is solid.

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 two dense sentences with no filler. It front-loads the core value proposition, lists concrete return elements, adds use cases, and then gives chains and data source—everything earns its place.

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?

There is no output schema, so the description's list of returned fields is especially valuable and mostly sufficient. It covers wallet scope, chains, filtering behavior, and pricing, though it could optionally mention result size, sorting, or the meaning of unpriced holdings.

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%, so the baseline is 3. The description adds chain context and implies payment via x402, which helps interpret x_payment, but it does not explicitly explain the x_payment parameter format or how it relates to the $0.09 cost.

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 identifies the tool as a full token-portfolio 'bagcheck' for any wallet, enumerating exact outputs: amounts, live USD prices, values, portfolio percentages, total value, and concentration. This distinguishes it from simpler sibling tools like get_chain_erc20_balance or chain_native_balance, even though no sibling is named explicitly.

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 gives explicit contexts: copy-trading, whale-watching, risk, and research agents. It does not list exclusions or alternative tools, so it falls just short of a perfect 5, but the intended use cases are clear enough for an agent to select this tool appropriately.

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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