LIVE price + market snapshot for a crypto TOKEN/coin (not an NFT) from CoinGecko: current USD price, 24h and 7d change, market cap, 24h volume, all-time high and how far below it is. Use for any 'current price / what's X worth' question and to give live data on a coin someone is weighing. Give it the token name or ticker.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | token name or ticker, e.g. 'cashcat' or 'UNI' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the data source (CoinGecko), the live nature of the data, and lists the exact fields returned. It does not mention failure modes or rate limits, but for a simple read-only lookup, the behavioral transparency is adequate and not misleading.
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 two sentences, front-loaded with the core purpose and return data. The second sentence quickly gives usage guidance and input format. Every word contributes, with no fluff or repetition.
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 one-parameter tool with no output schema, the description is complete: it states the tool's function, the type of data returned, when to use it, and what to pass. It provides enough context for correct invocation without requiring additional documentation.
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 already documents the single 'query' parameter with a clear description and examples, giving 100% schema coverage. The description repeats the instruction to provide name or ticker and adds a 'not an NFT' qualification, which is more about tool scope than parameter format. Thus it adds little 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 it fetches a live price and market snapshot for a crypto token/coin, explicitly excluding NFTs, and enumerates specific data points (USD price, 24h/7d change, market cap, 24h volume, all-time high). This verb+resource+scope structure distinguishes it from sibling tools like NFT floor price or stock quote.
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?
Provides explicit usage guidance: 'Use for any current price / what's X worth question' and 'to give live data on a coin someone is weighing.' It does not explicitly mention alternatives or when-not-to-use, but the context clearly positions it for real-time token pricing, which is sufficient for most AI agents.
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 share the same core purpose (safety checks) differentiated mainly by chain or asset type, and descriptions are detailed enough to distinguish them most of the time. However, pairs like rug_check/rugcheck and deployer_check/deployer_reputation have overlapping purposes that could lead to misselection.
Most tool names follow a lowercase snake_case pattern with clear descriptors, but there are notable exceptions like 'rugcheck', 'defillama', and 'verify'. Additionally, the 'rug_check' vs 'rugcheck' pair is an obvious naming inconsistency.
44 tools is excessive for any server. Even for a broad security/due-diligence purpose, many tools (especially the robinhood_* series) are highly specialized and could be consolidated into fewer actions.
The tool surface is impressively comprehensive, covering token/NFT safety, transaction simulation, whale/address tracking, stock analysis, prediction markets, and project validation. Minor gaps exist (e.g., no ENS resolution or stock price history), but they are not critical for the server's core purpose.
