Skip to main content
Glama

compute_rsi

Compute the Relative Strength Index (RSI) for a stock to identify overbought or oversold conditions, generating a buy or sell signal.

Instructions

Compute the Relative Strength Index (RSI) for a stock.

Returns the RSI value (0–100) and a signal:

  • overbought: RSI ≥ 70 (potential sell pressure)

  • oversold: RSI ≤ 30 (potential buy opportunity)

  • neutral: RSI between 30 and 70

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
periodNoRSI lookback period in days (default 14)
symbolYesStock ticker symbol

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rsiYesRSI value (0–100)
periodYes
signalYesoverbought ≥ 70 | oversold ≤ 30 | neutral otherwise
symbolYes
Behavior4/5

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

No annotations are provided, so the description carries full burden. It discloses the output structure (RSI value, signal with thresholds: overbought, oversold, neutral) and implies it is a read-only computation. It does not mention potential side effects or auth, but for a stateless computation this is acceptable.

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 concise (4 lines) and front-loaded with the key action. The formatting with blank lines is slightly wasteful but does not hinder readability. Every sentence adds value.

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 simple 2-parameter tool with an output schema, the description adequately explains the return value structure (RSI value and signal classification). It is complete enough for an AI agent to understand the tool's function and outputs.

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 coverage is 100% with descriptions for both parameters (symbol, period). The description does not add any new parameter semantics beyond the schema, but it is not required to. Baseline 3 is appropriate.

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 states 'Compute the Relative Strength Index (RSI) for a stock,' which is a specific verb+resource. It clearly distinguishes this tool from siblings like get_historical_prices or generate_research_report by focusing on a single technical indicator.

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?

No explicit guidance is provided on when to use this tool vs alternatives (e.g., when to use RSI over other indicators, or when to use this tool instead of generating a full research report). The description only explains the output signal thresholds, not the broader decision context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/mfarhan0304/MCP-MarketMind'

If you have feedback or need assistance with the MCP directory API, please join our Discord server