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tynorn1

Indian Stock Analysis MCP Server

by tynorn1

forecast_prophet_model

Forecast Indian stock prices with Prophet by inputting a ticker and forecast horizon. Get confidence intervals, seasonality detection, and visual charts for data-driven decisions.

Instructions

Train Prophet model and generate forecasts with confidence intervals.

Provides:

  • Automatic seasonality detection (yearly, weekly patterns)

  • Trend changepoint identification

  • Holiday effects support (Indian market holidays)

  • Component decomposition (trend + seasonality)

  • Multi-period forecasting with confidence bands

  • Model validation and quality checks

  • Visual forecast charts with historical data

Args: ticker: Stock ticker symbol (e.g., 'RELIANCE', 'TCS', 'INFY') periods: Number of periods to forecast (default: 20 trading days) confidence: Confidence interval level (0.8-0.99, default: 0.95) period: Time period for training data ('1mo', '3mo', '6mo', '1y', '2y', '5y') yearly_seasonality: Enable yearly seasonality (default True) weekly_seasonality: Enable weekly seasonality (default True) seasonality_mode: 'additive' or 'multiplicative' (default 'additive') changepoint_prior_scale: Flexibility of trend changes (default 0.05) seasonality_prior_scale: Flexibility of seasonality (default 10.0) holidays_prior_scale: Flexibility of holiday effects (default 10.0) validation_split: Train-validation split ratio (default 0.2) include_holidays: Include Indian market holidays (default False)

Returns: List containing text analysis and ImageContent with forecast plot

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
periodNo1y
tickerYes
periodsNo
confidenceNo
include_holidaysNo
seasonality_modeNoadditive
validation_splitNo
weekly_seasonalityNo
yearly_seasonalityNo
holidays_prior_scaleNo
changepoint_prior_scaleNo
seasonality_prior_scaleNo

Schema Changelog

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

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses automatic seasonality detection, changepoint identification, holiday support, validation checks, and the return format of text analysis plus an ImageContent forecast plot. It does not cover potential errors or data requirements, but the core behavior is clearly described.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-organized with an intro, feature bullets, Args, and Returns. It is longer than necessary due to the 12 parameters, but each section serves a purpose and the main purpose is front-loaded.

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 12-parameter tool with no annotations and no output schema, the description is quite complete: it covers all parameters, defaults, and return content. It could add example usage or explicit guidance on when Prophet is preferred over ARIMA, but an agent can invoke the tool correctly with the provided information.

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?

Schema description coverage is 0%, but the description lists all 12 parameters with meaningful explanations and defaults, such as 'Number of periods to forecast', 'Time period for training data', and 'Flexibility of trend changes'. This fully compensates for the schema's lack of 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 opening sentence states a specific verb and resource: 'Train Prophet model and generate forecasts with confidence intervals.' This clearly distinguishes it from sibling ARIMA tools by naming Prophet explicitly and describing the forecasting output.

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

Usage Guidelines3/5

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

The description implies use for Prophet-based forecasting with features like seasonality, holidays, and confidence bands, but it never explicitly states when to choose this over train_arima_model or forecast_arima_model. No alternatives or exclusions are mentioned.

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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