$0.01 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, the description carries the full burden of behavioral disclosure. It reveals that the data is live, sourced from Dexscreener, available across every chain, and costs $0.01 via x402. It does not detail failure modes, rate limits, or the exact response envelope, but for a read-only data lookup the key behavioral traits are present.
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 compact and front-loaded, beginning with the payment cost, then the core function and output metrics. It contains useful details without excessive fluff, though 'live' appears twice and the final sentence could have been merged without losing meaning.
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?
Given that there is no output schema or annotations, the description does a solid job covering the input query, payment requirement, source, metrics, and timeframes. The main gaps are lack of explicit sibling differentiation and incomplete explanation of the x_payment parameter, but an agent can still select and invoke the tool correctly.
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 documents q as 'Token symbol, name, or contract address', and the description reinforces that same accepted input format. However, x_payment is left undescribed in both the schema and the description; the '$0.01 via x402' mention hints at payment but does not explain how to populate or interpret x_payment.
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 that this tool provides live decentralized-exchange data for any token and enumerates the specific metrics returned: price, volume, liquidity, buy/sell counts, and momentum. The DEX-specific scope distinguishes it from generic crypto price or chain tools, though it does not explicitly name sibling alternatives.
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?
'For crypto trading, sniping, and research agents' gives an explicit intended use case, and the token-query framing makes it clear when this tool is relevant. It does not provide when-not-to-use guidance or directly compare with sibling tools, but the context is clear.
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.