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PatternsRadar — NSE stock screener

sift_reference

The Sift query language: every field, indicator, window, operator and pattern that can appear in a scan. Read this before writing a query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It communicates that the tool returns comprehensive reference content about the query language, which is the essential behavior for a documentation tool. However, it does not describe the output format, length, or whether the tool is purely informational, though those are largely inferable from its purpose.

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?

A single, information-dense sentence that front-loads the tool's scope and ends with a clear usage directive. There is no filler or redundancy, and every phrase earns its place.

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?

For a zero-parameter reference tool with no output schema, the description fully covers what the tool is, what content it provides, and when it should be consumed. There are no missing operational details that an agent would need to invoke it correctly.

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 tool has zero parameters, so the schema already fully describes the input surface. The baseline for zero-parameter tools is 4, and the description adds appropriate context by explaining what the reference contains instead of needing to document parameters.

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 this is a reference for the Sift query language, enumerating the exact content types it covers (fields, indicators, windows, operators, patterns). It also includes a directive ('Read this before writing a query') that tells the agent what the tool is for, distinguishing it from action-oriented siblings like run_scan and delete_scan.

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 gives explicit usage context: read this before writing a query. This is a clear instruction on when to invoke the tool. It does not explicitly exclude other tools or list alternatives, but given the tool's nature as a reference, the timing guidance is sufficient.

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