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pyalgobot

angelone-mcp

by pyalgobot

get_gainers_losers

Fetch top F&O gainers and losers by price or open interest percentage for near, next, or far expiry contracts. Identify market movers to inform trading decisions.

Instructions

Get top F&O gainers/losers.

datatype: PercPriceGainers | PercPriceLosers | PercOIGainers | PercOILosers expirytype: NEAR | NEXT | FAR

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datatypeYes
expirytypeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Install Server

TDQS

B3.3/5.0
Behavior2/5

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 reveals the domain and allowed parameter values, but does not state whether the call is read-only, how results are ranked, what time frame applies, or any other behavioral constraints.

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 extremely compact and front-loaded, with the core purpose in the first sentence and parameter values listed in two terse lines. Every part earns its place and there is no filler.

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

Completeness3/5

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

The presence of an output schema covers return-value expectations, and the description covers purpose and parameter values. However, with no annotations and no usage guidance, the definition is only minimally sufficient for an agent to call the tool correctly in context.

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 0%, so the enumerated values in the description are the only semantic guidance for the parameters. The value lists are helpful and self-explanatory, but the description does not explain what each datatype means or how NEAR, NEXT, and FAR differ beyond their names.

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 clear verb-resource statement: 'Get top F&O gainers/losers.' The parameter value lists further specify the exact categories of gainers/losers, which distinguishes this from close siblings like get_oi_buildup or get_market_quote.

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

Usage Guidelines2/5

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

The description gives no guidance on when to use this tool versus sibling market-data tools, and names no alternatives or exclusions. It only states what the tool does and lists parameter values, so an agent gets no decision support for selecting it.

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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