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Riley Craig x402 Agent Store

by io.github.rccola990-cloud

token_security_check

$0.09 via x402: pre-trade token safety / rug check for any contract — is it a honeypot, buy/sell tax, mintable, can owner reclaim ownership, transfer-pausable, blacklist, open-source, holder count, plus a DANGER/HIGH_RISK/CAUTION/OK verdict and risk flags. The call every trading and sniping agent should make BEFORE buying a token. Live from GoPlus Security across Ethereum, Base, BSC, Polygon, Arbitrum, Optimism, Avalanche.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chainNoethereum|base|bsc|polygon|arbitrum|optimism|avalanche (default ethereum)
addressYesToken contract 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 burden. It discloses the cost ($0.09 via x402), the data source (GoPlus Security), and the nature of the output (verdict and risk flags). It doesn't explicitly state read-only semantics, but the context implies a safe, non-destructive check.

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 a single dense paragraph that packs in multiple justified details (cost, checks, verdict, chains). It is front-loaded with the core purpose and every clause adds value.

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?

Given no output schema, the description provides a thorough overview of capabilities and output, including the verdict categories. It doesn't detail error handling or exact return structure, but is sufficient for a first-level understanding. It also positions itself relative to the pre-trade context.

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 description partially compensates by mentioning that the address refers to a token contract and chains are listed. However, it does not explain the x_payment parameter, which is a gap in semantics.

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 performs a pre-trade token safety/rug check, listing specific checks (honeypot, buy/sell tax, mintable, etc.) and output verdicts (DANGER/HIGH_RISK/CAUTION/OK). This distinguishes it from other tools by its specific focus on token security analysis.

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 explicitly says to use this tool BEFORE buying a token, and that it is for trading and sniping agents. It does not mention specific alternatives or exclusions, but the context strongly implies its use case.

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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