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math-mcp-learning-server

by clouatre-labs
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Python
Hybrid

Financial Line Chart

plot_financial_line
Read-onlyIdempotent

Generate synthetic financial price data and plot trend lines for educational use. Simulate bullish, bearish, or volatile markets with adjustable days, start price, and color.

Instructions

Generate and plot synthetic financial price data (requires matplotlib).

Creates realistic price movement patterns for educational purposes. Does not use real market data.

Note: Use for time-series price data with optional moving average overlay. For general XY data, use plot_line_chart instead.

Examples: plot_financial_line(days=60, trend='bullish') plot_financial_line(days=90, trend='volatile', start_price=150.0, color='orange')

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoNumber of days to generate, e.g., 30
colorNoLine color (name or hex code, e.g., 'blue', '#2E86AB')
trendNoMarket trend directionbullish
start_priceNoStarting price value, e.g., 100.0

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.12.1

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, so the description adds valuable context beyond them: it requires matplotlib, generates synthetic data for educational purposes, and explicitly notes it does not use real market data. It omits details about output/plot behavior, but this is not critical for a simple plotting tool.

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-structured and front-loaded, but there is slight redundancy between 'Generate and plot synthetic financial price data' and 'Creates realistic price movement patterns for educational purposes.' The note and examples are valuable.

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 four optional parameters and no output schema, the description covers purpose, use case, alternative, prerequisite, and examples. The only minor gap is the ambiguous 'optional moving average overlay' since no corresponding parameter exists in the schema.

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?

Input schema covers 100% of parameters with descriptions, so the baseline is 3. The description's examples demonstrate plausible parameter combinations but do not add semantic meaning beyond the 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 opens with a specific verb-resource pair ('Generate and plot synthetic financial price data') and explicitly differentiates from sibling plot_line_chart by stating 'For general XY data, use plot_line_chart instead.' This makes the tool's purpose unmistakable.

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

Usage Guidelines5/5

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

Provides clear when-to-use guidance: 'Use for time-series price data with optional moving average overlay' and names the alternative for general XY data. Examples with parameter combinations reinforce usage.

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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