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 | |||
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?
Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context beyond the annotations: the data source (CoinGecko), the selection basis (search activity and interest), and the time-sensitive nature ('right now'). It doesn't describe output shape, but for a simple no-argument read tool this is acceptable.
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 front-loaded with a clear one-sentence summary, followed by source detail and a set of example queries. The example list is somewhat long and repetitive, but it serves a useful purpose for matching varied user phrasings; no filler or irrelevant content is present.
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 parameterless, read-only tool with no output schema, the description gives enough context: what it returns, where the data comes from, and what kind of user question it answers. It doesn't specify the exact number of coins or output fields, but those gaps are not critical for an agent deciding whether to call this 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?
The tool has zero parameters and 100% schema description coverage, so there are no parameter semantics to clarify. The description reinforces that only current trending data is returned and no user inputs are needed to narrow the query.
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 states a clear verb ('See what's trending') plus a specific resource: the top trending coins on CoinGecko based on search activity and interest. This distinguishes it from sibling tools like crypto_price, crypto_info, and crypto_compare, which target prices, metadata, and comparisons rather than current market buzz.
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 explicitly says 'Use this for...' and gives many example user phrasings, making the intended invocation context clear. However, it does not mention alternatives or provide when-not-to-use guidance, such as pointing to crypto_price for price questions or crypto_compare for comparisons.
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.
Several tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.
Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.
294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.
The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.