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PatternsRadar — NSE stock screener

by com.patternsradar

hitrate

Replay a Sift query across past sessions and report what its matches did over user-chosen forward horizons: win rate, payoff, excess return vs an index, a monthly breakdown and an equity-curve approximation. A sketch, not a backtest: no costs or slippage, and universe membership measured as of today. Everything it reports describes past matches, never a prediction.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fullNoinclude the per-session equity-curve points, histogram bins and per-trade list (large; default false)
sourceYesthe Sift query to replay
horizonsNoforward windows in bars, each 1-60, at most 4 (default 1, 5, 20)
sessionsNohow many past sessions to replay (default 250)
universeNoliquidity universe: 100, 500 (default) or 2000
benchmarkNoindex for excess returns: a NIFTY index name (default NIFTY 50) or none
entryModeNoentry price: close (signal bar, default) or nextOpen (next session's open)
targetPctNosimulate a profit target this many percent above entry; 0/absent means none
stopLossPctNosimulate a stop-loss this many percent below entry; 0/absent means none
detailHorizonNowhich horizon the monthly/equity-curve detail describes (default: the largest)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

No annotations are present, so the description carries the full burden. It discloses key limitations up front—no costs/slippage, universe measured today, and explicitly that results 'describe past matches, never a prediction.' It also lists the output dimensions, which is high-value behavioral context for a financial analysis tool.

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?

Three sentences with no filler: the first states the action and outputs, the second defines the tool's limitations, and the third clarifies the epistemic status of the results. The most important qualifier ('A sketch, not a backtest') appears early and earns its place.

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?

For a tool with 10 parameters and no output schema, the description still conveys purpose, limitations, and main result categories. It does not discuss parameter interactions or interpretation, but the schema covers the inputs and the description's output list is sufficient to call the tool with reasonable expectations.

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

Parameters3/5

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

Schema description coverage is 100%, so the baseline is 3. The description adds no extra meaning about individual parameters beyond a general reference to 'user-chosen forward horizons,' and the schema already documents each field adequately.

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 verb ('Replay') and resource ('a Sift query'), then enumerates concrete outputs: win rate, payoff, excess return, monthly breakdown, and equity-curve approximation. This makes it clearly distinct from sibling tools like run_scan or get_bars, which concern live matching or raw bar data rather than historical performance analysis.

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?

It states that the tool is 'a sketch, not a backtest' and lists the missing components (costs, slippage) and the measurement caveat (universe as of today). This provides clear context for when results are appropriate, but it does not name alternative tools or explicitly contrast with run_scan, so it stops short of full when-to-use/when-not-to-use routing.

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

A3.7/5.0
Disambiguation5/5

Each tool targets a clearly distinct action: symbol discovery, symbol metadata, price bars, query reference, scan execution, saved scan management, market metadata, and historical replay. run_scan and hitrate are differentiated as current matches versus past performance, so there is no meaningful overlap.

Naming Consistency4/5

Most tool names follow a lowercase snake_case verb_noun pattern such as delete_scan, get_bars, run_scan, save_scan, and search_symbols. A few names like hitrate, market_meta, and sift_reference break that pattern but remain consistent in style and readable.

Tool Count5/5

Ten tools is well-scoped for an NSE stock screener. Each tool earns its place and together they cover symbol lookup, market data, querying, saved scan lifecycle, and historical evaluation without redundancy.

Completeness4/5

The core screener workflow is covered: find symbols, fetch bars and metadata, learn the query language, run scans, manage saved scans, and evaluate hit rates. Minor gaps exist, such as no dedicated view for a single saved scan's full query and get_bars only returning recent bars, but these are workable limitations.

Resources