get_historical_zero_dteReplay 0DTE analytics (pin risk, expected move, gamma acceleration, dealer hedging estimates for same-day expiry) at any minute since April 2018. Alpha tier.
| Name | Required | Description | Default |
|---|---|---|---|
| at | Yes | As-of timestamp: YYYY-MM-DDTHH:mm:ss (ET) or YYYY-MM-DD | |
| apiKey | No | FlashAlpha API key. Omit when calling via /mcp-oauth (OAuth flow); required on /mcp. | |
| symbol | Yes | Stock/ETF ticker |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already signals a read operation. The description adds the temporal range (since April 2018), the list of analytics returned, and the 'Alpha tier' access distinction, which provide useful behavioral context beyond structured data.
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 sentence, front-loaded with the main verb and object, and packs the analytics list and temporal scope without unnecessary words.
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 no output schema, the description compensates by enumerating the analytics categories and the replay capability. It does not explain response format, but the listed components give a solid expectation. The 'Alpha tier' note adds a prerequisite caveat.
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% for all three parameters. The description adds meaning to 'at' by specifying the minimum timestamp (April 2018) and clarifies the scope of 'symbol' via 'same-day expiry' analytics context.
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 'Replay' and identifies the resource as '0DTE analytics' with concrete components (pin risk, expected move, gamma acceleration, dealer hedging estimates). It also specifies the temporal scope ('since April 2018'), which distinguishes it from current-data tools like get_zero_dte.
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 does not explicitly mention alternatives or exclusion criteria. The phrase 'at any minute since April 2018' implies this is for historical queries, but it does not contrast with real-time tools like get_zero_dte. Usage is implied rather than stated.
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 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.