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FlashAlpha

by com.flashalpha.lab

Earnings Analytics

get_earnings
Read-only

Get earnings analytics for a symbol across six lenses. kind enum values: • expected_move — earnings-implied move decomposition: splits front-expiry straddle into jump vs baseline-diffusion using pre/post-event SVI term structure. • history — past earnings events: EPS/revenue surprises, implied vs actual moves, and realized IV crush per event. • iv_crush — expected + historical IV-crush distribution: live crush estimate and median/p25/p75/best/worst from up to 20 past events. • vrp — earnings vol-risk-premium: implied move vs realized-median, premium ratio, z-score, percentile, richness assessment. • dealer_positioning — event-scoped dealer exposure: gamma flip and walls on event-week expiries, GEX by DTE bucket, charm acceleration. • strategies — earnings strategy-suitability scores: long straddle, short strangle, iron condor, calendar spread, earnings diagonal (0–100 each).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindYesAnalytics kind (required). One of: expected_move, history, iv_crush, vrp, dealer_positioning, strategies.
apiKeyNoFlashAlpha API key. Omit when calling via /mcp-oauth (OAuth flow); required on /mcp.
symbolYesStock/ETF/index ticker (e.g. AAPL, NVDA, SPY)

TDQS

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description goes far beyond the readOnlyHint annotation by disclosing computational methods (e.g., pre/post-event SVI term structure, GEX by DTE bucket) and data utilization (up to 20 past events). This gives a transparent view of what the tool does internally, with no contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with a one-sentence overview, followed by a structured bullet list of six kinds. Each line is dense with purposeful information, and the formatting makes it easy to scan despite the length.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Even without an output schema, the description explains what data each lens returns conceptually (e.g., percentiles, scores 0–100, GEX breakdowns). It covers all parameters and provides enough context for an agent to understand the tool's scope and outputs.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema describes 'kind' as a plain string without enumerating values; the description fills this gap by listing and defining each allowed kind in detail. Symbol and apiKey are straightforward, but the enum documentation is essential and well-covered.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool's function: 'Get earnings analytics for a symbol across six lenses,' and enumerates each lens with precise detail. This clearly distinguishes it from sibling tools like get_earnings_calendar or get_expected_move.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context by listing six analytics kinds, but it does not explicitly state when to prefer this tool over alternatives. The context is strong enough for an agent to infer usage, yet lacks explicit exclusions.

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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TDQS

A3.6/5.0
Disambiguation2/5

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.

Naming Consistency4/5

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.

Tool Count1/5

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.

Completeness5/5

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.

Resources