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Signal8

by ai.signal8

Get Top Market Movers

get_top_movers
Read-only

Top stock movers — gainers (largest % up), losers (largest % down), or active (highest volume). Optional session window (premarket / regular / afterhours; regular default; not supported for active). Optional date (YYYY-MM-DD) returns a PAST trade date's gainers/losers on a historical daily close-to-close basis (computed from split-adjusted daily bars, NOT intraday) — session is rejected when date is set, date is not supported for direction=active, and a non-trade date (weekend/holiday) returns an empty list (not an error). Penny-stock artifacts are filtered by default — set includePennyStocks to include sub-$1 movers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoOptional past trade date (YYYY-MM-DD). When set, returns that day's top gainers/losers computed on a historical daily close-to-close basis from split-adjusted daily bars (NOT intraday, NOT session-specific). Rejected with 400 when combined with a non-regular session or with direction="active"; a future or malformed date is also 400. A weekend/holiday date returns an empty list, not an error.
limitNoOptional max rows (1–100). Backend default applied when omitted.
sessionNoSession window: premarket (4:00–9:30 AM ET), regular (RTH close-to-close, default), afterhours (4:00–8:00 PM ET). Live-only — rejected (400) when combined with date.regular
directionYesMover direction: gainers, losers, or active (volume)
includePennyStocksNoLoosen penny-stock artifact guards. Default false enforces prev_close >= $1 and a $1M dollar-volume floor. Set true to allow sub-$1 movers (prev_close >= $0.10, no dollar-volume floor). The ABS(change_pct) <= 500 cap applies in both modes.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNo

TDQS

A4.4/5.0
Behavior5/5

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

The description thoroughly discloses behaviors beyond the readOnlyHint annotation: historical returns are close-to-close and not intraday, non-trade dates yield empty lists, penny-stock filtering thresholds, and rejection combinations. No contradiction with annotations.

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

Conciseness3/5

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

The description is comprehensive but somewhat lengthy and dense. It front-loads the main purpose but includes many conditional details that could be streamlined. A more concise structure would improve readability.

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

Completeness4/5

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

An output schema exists, so return values are likely documented there. The description covers most behavioral aspects and edge cases, leaving few gaps. It provides sufficient context for an AI agent to understand usage.

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 description coverage is 100%, so baseline is 3. The description adds value by explaining nuances like default session, date restrictions, and the penny-stock filtering logic, going beyond the schema.

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 clearly identifies the tool's purpose: returning top stock movers categorized by gainers, losers, or active (volume). It specifies the resource (top movers) and the action (get), distinguishing it from sibling tools that cover other market data.

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 explains when to use optional parameters (session, date, includePennyStocks) and the constraints of each. However, it does not explicitly compare to sibling tools or state when to prefer this tool over alternatives.

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

A3.8/5.0
Disambiguation3/5

Many tools have distinct purposes, but there are several groups of closely related tools (e.g., multiple insider trading and politician tools) that could cause confusion. Overall, an agent can generally distinguish tools, but the boundaries between some are fuzzy.

Naming Consistency5/5

All tool names use a consistent verb_noun pattern (get_, search_, screen_) with snake_case. This makes the tool set predictable and easy to navigate.

Tool Count1/5

With 91 tools, the server is far too large. The number of tools is excessive for a single MCP server, leading to cognitive overload and suggesting it should be split into multiple focused servers.

Completeness4/5

The tool surface covers an impressively wide domain including company data, insider trading, institutional holdings, political trading, and SEC filings. Minor gaps exist (e.g., no options or bonds), but the coverage is comprehensive for the stated purpose.

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