get_historical_vexReplay vanna exposure (VEX) by strike 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 |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / apiKey / defaultAdded value: +nullInput schema / properties / apiKey / descriptionPrevious value: -"Your FlashAlpha API key (Alpha tier)"New value: +"FlashAlpha API key. Omit when calling via /mcp-oauth (OAuth flow); required on /mcp."Input schema / properties / apiKey / typePrevious value: -"string"New value: +[
+ "string",
+ "null"
+]Input schema / requiredPrevious value: -[
- "symbol",
- "apiKey",
- "at"
-]New value: +[
+ "symbol",
+ "at"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true. The description adds useful context beyond annotations: the data availability window ('since April 2018') and the access tier ('Alpha tier'), which implies potential authorization requirements. However, it does not disclose output format or pagination behavior.
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 short sentences, front-loaded with the key action ('Replay vanna exposure') and scope. 'Alpha tier' is additional but brief and informative. No wasted 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?
Despite lacking an output schema, the description conveys the essential purpose, temporal range, granularity, and access tier. It does not explain return structure or potential restrictions (e.g., data gaps), but given the tool's simplicity and the read-only annotation, the information is reasonably complete.
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 description coverage is 100%, so the schema already documents all parameters (at, apiKey, symbol). The description adds minimal parameter-specific detail—'at any minute' clarifies the meaning of 'at' and 'by strike' hints at output granularity but not parameter semantics. Baseline 3 is appropriate.
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's function: 'Replay vanna exposure (VEX) by strike at any minute since April 2018.' It uses a specific verb ('replay'), identifies the resource ('vanna exposure by strike'), and provides a temporal scope, which distinguishes it from siblings like get_historical_gex and get_historical_chex.
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 historical point-in-time queries via 'since April 2018' and 'at any minute,' but it does not explicitly mention when to choose this tool over alternatives like get_vex or other historical tools. No exclusions or alternative tool names are provided.
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.