$0.09 via x402: live network gas prices (slow/average/fast, in gwei) for any chain — the pre-transaction read every trading, sniping and settlement agent makes before sending a tx to size the fee. Across Base, Ethereum, Optimism, Arbitrum, Polygon, Gnosis. Live from Blockscout; one paid call instead of running your own RPC.
| Name | Required | Description | Default |
|---|---|---|---|
| chain | No | base|ethereum|optimism|arbitrum|polygon|gnosis (default ethereum) | |
| 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 provided, so the description carries the full burden. It discloses the cost ($0.09 via x402), the live data source (Blockscout), the supported chains, and the return shape (slow/average/fast, in gwei). It does not cover error handling or unsupported-chain behavior, but the exposed behavior is reasonably transparent for a simple paid read.
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 front-loaded with the cost and the core result, then adds supported chains, data source, and use case in a single dense sentence. Every clause adds useful information; there is no filler or redundancy.
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?
There is no output schema, but the description states the output fields (slow/average/fast) and unit (gwei), which is enough for an agent to understand the return. The remaining gap is field-level documentation of x_payment and behavior on unsupported chains, but the overall context is strong for a simple read 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 chain parameter is already described in the schema with allowed values and a default, and the description reinforces that with a list of chains. The x_payment parameter is only indirectly referenced by '$0.09 via x402' and 'one paid call,' but no field-level semantics or format are given. With 50% schema coverage, the description partially compensates but not fully.
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 states the tool returns 'live network gas prices (slow/average/fast, in gwei)' for supported chains, which is a specific resource and result. It also lists the supported chains, making it easy to tell apart from other chain_* read tools.
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 gives strong contextual guidance: 'the pre-transaction read every trading, sniping and settlement agent makes before sending a tx to size the fee.' This implies when to use it, but it does not explicitly contrast with sibling tools like chain_fee_history or chain_estimate_gas, so it stops short of a 5.
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.