onchain_metricsOn-chain TVL and network fees/revenue (24h/7d/30d) for one chain. Use a chain's own token symbol (e.g. 'ETH', 'SOL', 'BNB') — returns a plain 'not a tracked chain' message for tokens that aren't L1/L2s. Does not cover gas price (gwei).
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | Chain token symbol, e.g. 'ETH' or 'SOL' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already covers safety. The description adds useful behavioral detail: unsupported symbols return a plain message, time ranges are 24h/7d/30d, and gas price is not covered. No contradiction with annotations.
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?
Two sentences with no fluff. The main data scope is front-loaded, followed by input guidance and caveats. Every sentence earns its place.
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 tool with no output schema, the description is complete: what metrics are returned, what input format to use, what unsupported inputs do, and what is excluded.
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%, so the schema already documents the symbol parameter. The description adds meaning by specifying 'own token symbol', giving L1/L2 context, and adding examples like 'BNB'. This goes beyond the schema's brief description.
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 specific verb and resource: returns on-chain TVL and network fees/revenue for one chain. It clearly distinguishes this from market-data siblings by focusing on on-chain metrics and a single-chain scope.
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?
Clear usage context: use a chain's own token symbol, and non-L1/L2 tokens produce a plain 'not a tracked chain' message. It also excludes gas price. It does not explicitly name alternative tools, but the guidance is still actionable.
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.
Tools are grouped by clear prefixes and mostly target distinct resources; market_quotes vs research_token_view, market_top_movers vs market_trending, and funding_current vs market_quotes have some field or purpose overlap, but descriptions draw enough scope boundaries for an agent to choose correctly in most cases. No two tools are truly interchangeable.
All 30 tool names use lowercase snake_case with a consistent domain-prefix convention such as market_, flow_, research_, sentiment_, and catalysts_, making the surface predictable. Even helpers like utc_time and risk_position_size fit the same noun-oriented pattern without style mixing.
At 30 tools this set crosses the 'too many' threshold, and several tools reproduce data already available through broader ones such as research_token_view and market_quotes. The breadth is defensible for a crypto-research platform, but the surface feels heavy and could be consolidated.
The tool set covers the read-only crypto research workflow thoroughly: market data, candles, derivatives, funding, OI, order book, whale flows, ETF flows, on-chain metrics, news, sentiment, prediction odds, technicals, regime/positioning scans, and position sizing. It also includes health and UTC helpers that close practical workflow gaps, with no obvious dead ends.