$0.09 via x402: live decentralized-exchange data for any token (symbol, name, or contract) — USD price, 24h volume, liquidity, buy/sell counts, and momentum across 5m/1h/6h/24h, across every chain. For crypto trading, sniping, and research agents. Live from Dexscreener.
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | Token symbol, name, or contract address | |
| 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?
With no annotations provided, the description carries the full burden of behavioral disclosure and does add notable context: the $0.09 x402 payment requirement and the Dexscreener source. It does not, however, disclose payment mechanics, rate limits, failure modes, or how ambiguous token queries are resolved.
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 a single dense sentence containing the cost, data scope, metric list, chain coverage, target users, and source, with no filler. It is slightly back-loaded with the use-case phrase, but every clause is informative and it remains easily scannable.
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?
For a tool with only two parameters, no output schema, and no annotations, the description covers the core data delivered, the source, and the payment cost, which is a reasonable foundation. It falls short on the meaning and required format of x_payment, ambiguous-token behavior, and the return structure an agent should expect.
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 description coverage is only 50%: q is documented but x_payment is not, and the description does not explain how to populate x_payment beyond the vague '$0.09 via x402' cost hint. The q parameter is essentially restated in the description rather than enriched, so it adds little semantic 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 states the tool provides live decentralized-exchange data for any token, with a concrete list of metrics: USD price, 24h volume, liquidity, buy/sell counts, and momentum across multiple timeframes. This is specific enough to distinguish it from generic price or chain data tools, though it lacks an explicit action verb and does not name a sibling alternative.
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 a target audience and use-case context ('For crypto trading, sniping, and research agents') and emphasizes the live, cross-chain nature, implying when an agent would want it. However, it does not explicitly state when to prefer this tool over closely related siblings such as crypto_prices, connect_token, or token_security_check, nor does it describe when not to use it.
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.