Any wallet's balance of any ERC-20 token (USDC, USDT, DAI, or any contract) via balanceOf, across Base, Ethereum, Optimism, Arbitrum, Polygon, Gnosis. The token-balance read wallet-tracking, copy-trade, treasury and trading agents poll constantly — the most-called RPC read after native balance. Live public RPC; 6 chains vs onesource's Ethereum-only coverage.
| Name | Required | Description | Default |
|---|---|---|---|
| x_payment | No | Signed x402 payment payload |
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?
With no annotations, the description states it is a 'live public RPC' and a read operation via balanceOf, which conveys basic behavioral traits. Yet it does not mention potential rate limits, error behavior, or response format. The addition of 'live public RPC' is useful but the disclosure is incomplete.
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 three sentences and front-loads the core purpose. It efficiently covers scope, use cases, and a comparison, but includes promotional language like 'most-called RPC read after native balance' that adds little functional value.
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?
While the description explains the chains and token types, it omits essential details: how to specify the target wallet/token (especially given the minimal schema) and what the call returns (no output schema). Without these, the agent cannot reliably invoke or interpret the tool.
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 schema only includes x_payment ('Signed x402 payment payload') with no explanation of how the wallet address and token contract are specified. The description says 'any wallet' and 'any ERC-20 token' but gives no guidance on how to map those to the only parameter. This is a critical gap because the agent cannot infer the correct input structure.
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 identifies the tool's function: retrieving any ERC-20 token balance for any wallet via balanceOf across six chains. It distinguishes itself from alternatives like onesource by emphasizing multi-chain coverage, and even names example tokens.
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 clear use-case context (wallet-tracking, copy-trading, treasury agents) and notes it is the most-called RPC read after native balance. It also implicitly contrasts with onesource's Ethereum-only coverage, suggesting when this tool is preferred. However, it does not explicitly name sibling tools or exclusion scenarios.
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.