Return the LIVE price of a cryptocurrency in USD and COP with its 24h change (real market data). High volume for agents that need prices. input=symbol or id (btc, eth, sol, usdc...). [x402: 0.005 USDC on Base, pay-per-use]
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Símbolo o id de la moneda (btc, eth, sol...) |
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. It transparently mentions the pay-per-use x402 billing (0.005 USDC) and 'real market data' nature, plus 'LIVE price' and '24h change' scope. It doesn't disclose potential failure modes like rate limits, stale-data incidents, or invalid symbol handling, but the core behavior is reasonably clear for this simple read 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?
The description is compact and front-loads the key purpose (live price), then scope (USD/COP, 24h change), audience, and cost. The [x402] note is useful but slightly interrupts the natural reading flow; still, every sentence serves a purpose and there's no wasteful padding.
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 single-parameter, read-only price lookup with no output schema, the description covers the essential inputs, output currencies, and cost. Minor gaps include no explanation of the return value structure (JSON keys) and no examples of valid IDs beyond tickers, but these are acceptable for a simple 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?
Schema coverage is 100% and the schema describes the input as a symbol or id. The description reinforces this with examples and specifies the exact output currency (USD/COP). It doesn't add detail about accepted symbol normalization or syntax beyond examples, so it stays at the baseline for fully-covered parameters.
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's function: returning live cryptocurrency prices in USD and COP with 24h change, and specifies example inputs (btc, eth, sol, usdc). It distinguishes itself from broad financial tools like market-data and fx by specifying the crypto focus and live data nature, though it doesn't explicitly 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?
The description notes this is for 'agents that need prices' and indicates the input format (symbol or id), giving clear context for when to use it. However, it does not explicitly state when not to use it (e.g., for historical data or fiat conversions) nor name alternative tools like market-data or fx for such cases.
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.
The set contains many trivially indistinct tools: ai-inference/inference, compress/comprimir, count-tokens/contar-tokens, detect-language/language-detect, and multiple overlapping OCR receipt variants. With 160 tools and pairs that differ only by language or suffix, an agent cannot reliably distinguish several capabilities.
Most names are readable lower-hyphen identifiers, but they mix action verbs, noun phrases, domain prefixes, pipeline suffixes, Spanish/English, and arbitrary demo/batch labels. There is a loose convention, but no consistent verb_noun pattern.
160 tools on one server is an extreme count and clearly unwieldy. Even as a marketplace, exposing every variant, demo, and composed bundle as a top-level MCP tool overwhelms agent selection and adds little distinct capability.
The set covers a huge range of text, image, audio, code, market, compliance, and content-workflow tasks, so many intents have some available tool. However, it is a grab-bag rather than a defined service surface, and the arbitrary demo/specialized variants make it unclear whether a needed operation truly exists or is just a duplicate.