FlashAlpha

Screener / Scan & Rank Symbols

post_screener

Read-only

Find, rank, and compare symbols across the whole universe in ONE call. Use this whenever the user does NOT name a single ticker but asks which / what / find / scan / screen / rank / top / most / highest / lowest across stocks (e.g. 'which names have the most negative gamma', 'rank tickers by VRP', 'highest IV stocks right now', 'most pinned symbols today', 'cheap IV with positive gamma'). Prefer this over calling per-symbol tools in a loop. Cross-sectional screen/rank by GEX, VRP, 0DTE dominance, IV/term structure, skew, dealer risk, and strategy scores, with filters, sort, select, and custom formulas. Growth = top 10 symbols; Alpha = ~250 symbols + formulas.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`query`	Yes	JSON body. ALL keys optional; an empty {} returns your whole universe with default columns. Shape: {"filters":<node>,"sort":[{"field":"<f>","direction":"desc\|asc"}],"select":["symbol","<f>"],"limit":50} filters <node> is either a LEAF {"field":"<f>","operator":"<op>","value":<v>} or a GROUP {"op":"and\|or","conditions":[<node>,...]} (nest up to 3 deep, max 20 leaves). operators: eq, neq, gt, gte, lt, lte, between (value=[lo,hi]), in (value=[...]), is_null, is_not_null. common fields: regime (positive_gamma\|negative_gamma), net_gex, net_dex, gamma_flip, call_wall, put_wall, max_pain, zero_dte_magnet, zero_dte_pct_of_total, atm_iv, rv_20d, vrp_20d, skew_25d, term_state, pc_ratio_oi, price. Alpha-only fields: vrp_z_score, vrp_percentile, harvest_score, dealer_flow_risk, iron_condor_score, short_strangle_score, calendar_spread_score (plus `formulas` and `offset`). Examples: - most negative gamma: {"sort":[{"field":"net_gex","direction":"asc"}],"select":["symbol","net_gex","regime","price"],"limit":10} - richest VRP in positive gamma: {"filters":{"op":"and","conditions":[{"field":"regime","operator":"eq","value":"positive_gamma"},{"field":"vrp_20d","operator":"gte","value":2.5}]},"sort":[{"field":"vrp_20d","direction":"desc"}],"limit":15} - highest IV names: {"sort":[{"field":"atm_iv","direction":"desc"}],"select":["symbol","atm_iv","rv_20d","vrp_20d"],"limit":20}
`apiKey`	No	FlashAlpha API key. Omit when calling via /mcp-oauth (OAuth flow); required on /mcp.

TDQS

A4.7/5.0

Behavior4/5

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

The readOnlyHint annotation already signals a non-mutating operation; the description adds meaningful behavioral context about scale and scope: 'ONE call,' cross-universe coverage, and tiered output sizes ('Growth = top 10 symbols; Alpha = ~250 symbols + formulas'). It stops short of detailing the exact response shape or pagination behavior, but the annotation lowers the burden and the extra context is valuable.

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 the core purpose, then organizes usage triggers, alternatives, feature highlights, and tier behavior without redundancy. Every sentence earns its place, and the inclusion of concrete natural-language examples ('highest IV stocks right now') makes it dense yet scannable.

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?

This is a complex tool with a JSON query DSL and many fields, but the description covers intent, when to use, what the call does, tier limits, and main capabilities. The input schema itself documents all parameters in detail, and the no-output-schema gap is mitigated by the query examples that make return columns explicit. Overall, the description is complete for a read-only screener tool.

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

Parameters4/5

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

Schema coverage is 100%, and the query parameter description is already rich with JSON shape, operators, fields, and examples. The description adds complementary semantics not in the schema, notably the tiered result-size differences (Growth vs. Alpha) and the emphasis on a single call, which helps agents frame the query appropriately.

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 opens with a specific action: 'Find, rank, and compare symbols across the whole universe in ONE call.' It then enumerates concrete use-case triggers ('which / what / find / scan / screen / rank / top / most / highest / lowest') and explicitly distinguishes itself from per-symbol tools, making its purpose unmistakable.

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

Usage Guidelines5/5

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

Usage guidance is explicit and actionable: 'Use this whenever the user does NOT name a single ticker' and 'Prefer this over calling per-symbol tools in a loop.' It also names the alternative class of tools ('per-symbol tools') and provides rich examples of when to apply it, such as ranking by GEX or VRP.

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

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