$0.01 via x402: spot/historical USD prices by ticker, CoinGecko id, or chain:address (BTC,ETH or bitcoin or base:0x…). Prefer ?coins=.
| Name | Required | Description | Default |
|---|---|---|---|
| at | No | optional unix seconds | |
| ids | No | legacy e.g. bitcoin,ethereum | |
| coins | No | e.g. BTC,ETH,SOL | |
| x_payment | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / atAdded value: +{
+ "description": "optional unix seconds",
+ "type": "integer"
+}Input schema / properties / coinsAdded value: +{
+ "description": "e.g. BTC,ETH,SOL",
+ "type": "string"
+}Input schema / properties / ids / descriptionPrevious value: -"e.g. bitcoin,ethereum"New value: +"legacy e.g. bitcoin,ethereum"Input schema / requiredRemoved value: -[
- "ids"
-]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It usefully discloses the $0.01 x402 cost and the spot/historical nature of the data. However, it does not describe response shape, error behavior, rate limits, or whether this is a safe read-only operation.
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?
One dense sentence covers cost, data type, identifier formats, and examples. Every element earns its place and the most important caveat ('Prefer ?coins=') is placed at the end as a recommendation.
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 description is sufficient for a straightforward price lookup but leaves gaps: no output schema, no annotations, no explanation of how to request historical vs spot data via the 'at' parameter, and no mention of the response format.
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?
Schema coverage is high, but the description adds real value by clarifying that 'coins' is preferred and supports tickers or chain:address formats, while 'ids' is legacy CoinGecko IDs. This goes beyond the schema's terse examples, though x_payment remains unexplained.
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?
Description clearly states a specific function: retrieving spot or historical USD prices. It distinguishes from crypto sibling tools by specifying identifiers (ticker, CoinGecko id, chain:address) and giving concrete examples (BTC, ETH, bitcoin, base:0x…).
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?
No explicit guidance on when to use this tool instead of siblings like crypto_market_metrics, dex_token_data, or get_crypto_liquidations. 'Prefer ?coins=' is parameter guidance, not alternative selection guidance.
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.