See what's trending and hot in cryptocurrency right now. Returns the top trending coins on CoinGecko based on search activity and interest. Use this for 'what's trending in crypto?', 'hot cryptocurrencies', 'trending coins', 'what crypto is popular right now?', 'crypto buzz', 'what tokens are people looking at?', or any question about current crypto market interest and momentum.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds source (CoinGecko) and the selection criterion (search activity and interest), but no further behavioral nuance such as freshness, output shape, or rate limits, so it stays at baseline.
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 purpose and source are front-loaded in the first sentences, and the example query list is practical for agent matching. The list is somewhat repetitive (several paraphrases of 'trending'), but the description remains reasonably compact and each section serves a distinct role.
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 zero-parameter, annotation-rich tool with low complexity, the description gives enough context: what it returns, the data source, and when to use it. It does not describe the response structure, but no output schema exists and the simple 'top trending coins' outcome does not demand a detailed contract.
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 tool has zero parameters, so there is nothing for the description to clarify; schema coverage is already 100%. Baseline for a no-parameter tool is 4.
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 names a specific verb and resource: it returns the top trending coins on CoinGecko based on search activity and interest. This is clearly distinct from sibling crypto tools such as crypto_price, crypto_info, and crypto_compare, which would serve price, information, and comparison queries rather than trending/momentum questions.
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 explicitly lists query phrasings this tool should handle ('what's trending in crypto?', 'hot cryptocurrencies', etc.), giving an agent clear positive signals for when to invoke it. It does not name alternatives or state when not to use it, so it misses the full when-not/exclusion guidance that would earn a 5.
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 overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.
The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.
290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.
For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.