$0.001 via x402: EIP-155 chain ID (eth_chainId) for any of 6 chains — confirm which chain an RPC is on before signing. Multi-chain; 6 chains vs OneSource's Ethereum-only (Ethereum-only).
| Name | Required | Description | Default |
|---|---|---|---|
| chain | No | base|ethereum|optimism|arbitrum|polygon|gnosis (default base) | |
| x_payment | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, and the description does not mention whether the tool is read-only, has side effects, requires authentication, or costs anything beyond the implied x402 payment. This leaves behavioral impact entirely unspecified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, conveying the tool's purpose and a key differentiator in two sentences. No unnecessary words or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple, but the description does not mention the output format (e.g., integer, hex) or explain how the payment parameter works. Without an output schema, this missing information leaves the tool incompletely specified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The 'chain' parameter is well described with allowed values and a default. However, 'x_payment' has no description and its role is unclear. Since half the parameters are undocumented, the description only partially adds value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly indicates the tool returns the EIP-155 chain ID for any of 6 chains, and mentions a use case of confirming the chain before signing. It lacks an explicit verb like 'get' or 'retrieve', but the intent is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides a specific scenario (confirming chain before signing) and contrasts with a single-chain alternative, implying use for multi-chain needs. However, it does not explicitly state when to avoid this tool or compare with other sibling tools that might also return chain-related data.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.