get_cross_venue_fundingPredicted funding spreads between Hyperliquid, Binance, and Bybit per asset (from HL's predictedFundings feed). Surfaces delta-neutral carry: long the venue with lowest funding, short the highest, collect the spread. Sorted by annualized spread.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max rows (default 15) | |
| min_spread_annual_pct | No | Minimum annualized funding spread between venues to report (default 5%) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint and openWorldHint true. The description adds that data comes from HL's predictedFundings feed, results are sorted by annualized spread, and outlines the delta-neutral carry strategy. 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?
The description is two sentences plus a final clause, all front-loaded with the core purpose. Every sentence adds value, with no wasted words. Ideal length for quick comprehension.
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?
Given the tool has two simple parameters, readOnlyHint, and no output schema, the description adequately explains the data source, sorting, and strategy. It does not describe return format but that is mitigated by the schema and clarity of purpose.
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 describes each parameter. The description adds context about sorting and the meaning of min_spread_annual_pct but does not provide substantial new semantics beyond what the schema offers.
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 returns predicted funding spreads between Hyperliquid, Binance, and Bybit per asset. It uses specific verbs like 'get' and 'surfaces', and the context distinguishes it from siblings like get_funding_rates which likely yield per-venue data.
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 implies usage for delta-neutral carry trades but does not explicitly state when to use this tool versus alternatives like get_funding_rates or get_carry_scanner. No 'when not to use' guidance is provided, which leaves some ambiguity.
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 are specialized, but several pairs have fuzzy boundaries: e.g., get_funding_rates vs get_top_funding_rates, get_basic_macro vs get_macro_context, get_simple_iv vs get_options_iv. An agent could easily select the wrong one.
Most tools follow a 'get_X' pattern with descriptive noun phrases. There are a few exceptions like 'create_api_key' and 'search_markets', but overall the convention is consistent and readable.
With 47 tools, the server is overloaded. While the domain is broad, this many tools makes discovery and selection difficult for an agent, reducing coherence.
The tool set covers an impressively wide range: macro data, funding, prediction markets, OI history, whale tracking, risk analytics, position sizing, backtesting, and signal generation. It leaves no obvious gaps for a crypto trading assistant.