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scan_market

Scan all US stocks on a date for custom criteria (gaps, volume, change). Returns pct_change, range_pct, pct_gap, true_range_pct, volume and volume_ratio per match — criteria in percent (5 = 5%), outputs fractional (0.05 = 5%). $0.10 USDC.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateYesYYYY-MM-DD
limitNoMax results (default 50)
gap_up_pctNoMin gap up % from prior close
volume_minNoMin total volume
gap_down_pctNoMin gap down % from prior close
range_pct_minNoMin intraday range (high-low)/open as a percent, e.g. 5 for 5%
change_pct_maxNoMax intraday change %
change_pct_minNoMin intraday change %
volume_ratio_minNoMin volume vs prior day (e.g. 2.0)

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description discloses key behaviors: it lists the exact return fields (pct_change, range_pct, pct_gap, true_range_pct, volume, volume_ratio), clarifies unit conventions (criteria in percent, outputs fractional), and notes the cost ($0.10 USDC). This goes beyond the schema, though it does not mention pagination or rate limits.

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 three sentences, each conveying essential information: purpose, return fields, and unit/cost details. It is front-loaded, efficient, and contains no redundancy or filler.

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?

Given no output schema and no annotations, the description compensates by listing the return fields and unit conventions. It covers the main use case (custom criteria, date-based scan) and cost, but lacks details on pagination or edge cases like conflicting criteria. The schema covers parameter meanings and default limit, so overall it is fairly complete.

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?

The schema already documents all 9 parameters with descriptions (100% coverage), providing a baseline of 3. The description adds valuable cross-parameter context: criteria are in percent (5 = 5%) while outputs are fractional (0.05 = 5%), which clarifies unit conversions not fully evident from individual schema descriptions.

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 states 'Scan all US stocks on a date for custom criteria (gaps, volume, change)', identifying the exact action and scope. It distinguishes from sibling data-retrieval tools by focusing on market-wide screening rather than per-ticker queries.

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 for when to use this tool: when you need to screen all US stocks on a specific date with custom criteria. It does not explicitly mention alternatives, but the market-wide scope inherently contrasts with sibling tools like get_bars or query_range, making the intended usage evident.

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

B3/5.0
Disambiguation2/5

Several tools overlap significantly: get_bars, query_minute_bars, query_range, and query_batch all provide intraday bar data, making it hard to distinguish when to use which. Additionally, get_brief and get_fundamentals overlap in fundamentals coverage. The descriptions do not clearly delineate boundaries between these tools.

Naming Consistency3/5

The naming pattern is a mix of get_* and query_* prefixes, with list_ and scan_ as exceptions. While each individual name is readable, similar operations like get_bars and query_minute_bars use different verbs, making the overall convention inconsistent.

Tool Count4/5

15 tools is a reasonable count for a comprehensive financial data server, covering market data, fundamentals, filings, insiders, and scanning. The count is not excessive, though the overlapping intraday bar tools suggest some redundancy that could be consolidated.

Completeness4/5

The tool surface is fairly complete for its domain, offering intraday and daily market data, fundamentals, SEC filings, insider transactions, company profiles, and market scanning. Minor gaps exist (e.g., no dedicated dividend/split tool), but these are not critical and can be worked around via existing tools like get_brief.

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