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tanmaymish

MarketPulse MCP

by tanmaymish

support_resistance

Identify key support and resistance levels for any stock. Computes pivot points (R1, R2, R3, S1, S2, S3) from historical price action to inform trading decisions.

Instructions

Compute support and resistance levels for a stock. [PRO]

Calculates pivot points (R1, R2, R3, S1, S2, S3) and identifies key price levels from historical price action.

Args: symbol: Stock ticker (e.g., RELIANCE, AAPL, TCS) period: Data period: 3mo, 6mo, 1y, 2y (default: 6mo)

Examples: support_resistance("RELIANCE") → Key levels for Reliance support_resistance("AAPL", "1y") → Apple support/resistance with 1yr data

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
periodNo6mo
symbolYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that the tool uses historical price action and identifies specific levels, but it does not mention data sources, latency, limitations, or the exact structure of the returned output. The '[PRO]' marker is unexplained, leaving an ambiguity about access requirements.

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 concise and well-structured: a clear purpose sentence, a detail sentence, a compact Args section, and two relevant examples. No redundant information or filler words are present; every line contributes to understanding how to call the tool.

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?

The description covers the core aspects: purpose, parameters, and usage examples. An output schema exists, so the lack of explicit return-value documentation is acceptable. However, it omits any note on data limitations, update frequency, or how the 'key price levels' are derived beyond stating historical price action, which keeps it slightly short of a perfect score.

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?

Schema description coverage is 0%, meaning the schema fields lack descriptions. The description compensates by explaining both parameters: 'symbol' with example tickers and 'period' with allowed values and a default. This adds practical meaning beyond the bare schema, though it could include more details like the interpretation of 'period' values.

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 'Compute support and resistance levels for a stock,' which clearly states the action and resource. It further specifies that it calculates pivot points (R1, R2, R3, S1, S2, S3) and identifies key price levels, distinguishing it from generic technical analysis or other sibling tools.

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 usage through its examples and the nature of the tool, but it does not explicitly state when to choose this tool over alternatives like technical_indicators or stock_historical. No exclusionary or comparative guidance is provided, relying instead on the agent's inference from the tool's purpose.

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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