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finstack-mcp

finstack-mcp

by finstacklabs
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_nifty_outlook

Calculate the probability of Nifty 50 closing up in the next trading session using six market signals including RSI, FII flow, Put/Call Ratio, VIX, G-Sec yield, and GIFT Nifty.

Instructions

Compute the probability that Nifty 50 closes UP in the next trading session.

Aggregates 6 market signals into a single % score: • RSI(14) — overbought/oversold momentum • FII net flow (5d) — institutional buy/sell pressure • Put/Call Ratio — options market positioning (contrarian signal) • India VIX — fear index (low=risk-on, high=fear) • G-Sec 10Y yield — interest rate pressure on equities • GIFT Nifty — overnight global pre-market signal

Returns JSON with: - probability_up: e.g. 67 (% chance Nifty goes up) - signal: "Bullish" / "Cautiously bullish" / "Neutral" / "Bearish" - bull_factors: list of signals supporting upside - bear_factors: list of signals supporting downside - inputs: raw values for all 6 signals

Example: "67% probability Nifty up tomorrow — FII buying + low VIX but overbought RSI"

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_nifty_outlook"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It transparently lists the 6 input signals, the output fields, and gives an example. It does not disclose the exact weighting methodology or caveats about accuracy/uncertainty, but it does communicate that the output is a probability and provides signal labels, which is sufficient for an agent to know what to expect.

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 well-structured with a front-loaded purpose sentence, bullet-point lists for signals and output fields, and a concise example. Every sentence adds value; there is no redundant or filler content. The length is appropriate for the complexity of the tool.

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 0-parameter tool with an output schema, the description is exceptionally complete. It covers the purpose, the methodology (aggregating 6 signals), the output structure with example values, and provides a natural-language example. An agent can invoke this tool and interpret its results without any additional context.

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

Parameters5/5

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

The tool has 0 parameters, so the schema provides no parameter information. The description compensates richly by explaining the underlying data inputs (RSI, FII flow, Put/Call ratio, India VIX, G-Sec yield, GIFT Nifty), giving the agent meaningful context about what drives the computation beyond the empty 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 uses a specific verb ('Compute') and clearly states the resource ('probability that Nifty 50 closes UP in the next trading session'). It distinguishes itself from sibling tools by explicitly aggregating 6 named market signals into a single percentage score, which is a unique capability among the listed tools.

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 context is clear: this tool is for getting a next-session direction probability for Nifty 50. However, it does not explicitly mention when not to use it or name alternatives (e.g., technical_indicators for standalone indicators). It implicitly differentiates itself by focusing on an aggregated outlook, so it earns a 4 rather than a 5.

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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