get_zero_dteGet zero-days-to-expiration (0DTE) analytics: intraday gamma, time decay acceleration, pin risk, dealer hedging pressure for contracts expiring today.
| Name | Required | Description | Default |
|---|---|---|---|
| apiKey | No | FlashAlpha API key. Omit when calling via /mcp-oauth (OAuth flow); required on /mcp. | |
| symbol | Yes | Stock/ETF ticker | |
| strike_range | No | Strike range as decimal fraction of spot (default 0.03 = 3%) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotation readOnlyHint=true already discloses the safe read-only nature, so the description only needs to add context beyond that. It does add the scope (0DTE contracts) and lists the delivered metrics, which is useful. It does not discuss error behavior or rate limits, but given the annotation coverage, 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 a single front-loaded sentence beginning with the action and resource, followed by a colon and a concise list of analytics. Every word earns its place with no fluff or repetition of schema details.
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 read-only analytics tool with three well-documented parameters, the description adequately covers what the tool does and its 0DTE scope. It lacks an output schema, but the description lists the return content areas (gamma, time decay, pin risk, dealer pressure), which gives the agent a good sense of expected results.
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?
All three parameters have full schema descriptions (100% coverage), so the schema already explains apiKey, symbol, and strike_range. The description adds no additional parameter semantics, but with baseline 3 for high schema coverage, it meets the minimum.
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 uses a specific verb ('Get') and resource ('zero-days-to-expiration (0DTE) analytics') and enumerates the exact analytics covered (intraday gamma, time decay acceleration, pin risk, dealer hedging pressure). This clearly distinguishes it from sibling tools like get_gex or get_zero_dte_flow, which focus on narrower metrics.
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 states it is for 'contracts expiring today,' providing clear context for when to use this tool. It does not explicitly name alternatives or exclusions, but the 0DTE scope narrows usage sufficiently. A brief mention of compare/differentiate from get_zero_dte_flow would have made it a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Many tools have overlapping scopes: get_stock_summary, get_volatility, get_vrp, and get_exposure_summary all return comprehensive analytics with shared metrics, making it hard to pick the right one. The flow family (get_flow_live, get_flow_summary, get_flow_scan, get_flow_signals, etc.) has significant redundancy — get_flow_live bundles data also available via separate tools.
Tool names mostly follow a consistent get_<noun> pattern, with clear subgroups like get_historical_* and get_*_exposure. Minor deviations exist: post_screener, post_structure_pnl, calculate_greeks, and solve_iv break the get_ convention, but they are still predictable and readable.
With 73 tools, this is far beyond the 3–15 tool sweet spot and even the 50+ extreme mismatch threshold. While the domain is broad, the enormous surface is bloated by near-duplicate historical replay variants (18 get_historical_* tools) and multiple overlapping summary endpoints, making it unwieldy for an agent.
The tool set provides thorough coverage of options analytics: quotes, chains, greeks, volatility surface, VRP, exposure, flow, historical replay, screening, and strategy analysis. There are no obvious dead ends — core workflows like calculating greeks, getting exposure, and screening the universe are all supported.