get_accountGet your account info: plan, daily quota limit, usage today, remaining calls.
| Name | Required | Description | Default |
|---|---|---|---|
| apiKey | No | FlashAlpha API key. Omit when calling via /mcp-oauth (OAuth flow); required on /mcp. |
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"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 / requiredRemoved value: -[
- "apiKey"
-]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description adds specific returned fields, which is useful context. However, it does not disclose authentication nuances, quota reset periods, or potential errors, so it adds only modest transparency beyond 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 a single, focused sentence that front-loads the core purpose and key data points. No wasted words 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?
The tool is simple, with one optional parameter and no output schema. The description lists what data is returned, making it complete enough for an agent to understand the tool's role. It could mention whether the apiKey is required in certain flows, but the schema already covers that.
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?
The single parameter apiKey is fully documented in the input schema with a clear description of usage contexts (OAuth vs. /mcp). The tool description does not add additional parameter meaning, but given 100% schema coverage, the baseline of 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 specifies the verb 'Get' and the resource 'account info', enumerating specific fields (plan, quota, usage, remaining calls). It is easily distinguished from sibling tools, which focus on market data rather than account status.
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 this tool is for checking account details and usage, but it does not explicitly state when to use it versus alternatives or mention exclusions. While the topic is distinct from siblings, there is no direct guidance on when to invoke this tool over others.
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.