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remybanks77

market-pulse-mcp

by remybanks77

market_pulse_indicators

Read-onlyIdempotent

Calculate RSI-14, EMA-20/50, ATR-14, and annualized realized volatility for any crypto symbol using recent Coinbase candles. Adjust granularity and limit to match your trading timeframe.

Instructions

Compute technical indicators (RSI-14, EMA-20/50, ATR-14, annualized realized volatility) for a crypto asset from recent Coinbase candles. All math is implemented from scratch on the standard library; see market_pulse_mcp/indicators.py.

Args: symbol: Base ticker, e.g. "BTC", "ETH", "SOL". granularity: Candle granularity: one of "1m", "5m", "15m", "1h", "6h", "1d". Determines both the indicator timeframe and the annualization factor used for realized volatility. limit: Number of candles to pull (more history gives a more stable EMA-50; at least 15 candles are required for RSI-14/ATR-14).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
symbolYes
granularityNo1h

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds meaningful behavioral context beyond annotations, such as granularity determining the annualization factor, limit affecting EMA-50 stability, and the minimum 15 candles for RSI-14/ATR-14. This goes beyond a simple 'compute' statement.

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 a clear summary followed by an Args section. It is slightly padded by the implementation note ('All math is implemented from scratch... see file'), which is not directly useful for tool selection or invocation, but the overall length is appropriate for three parameters.

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?

Given that an output schema exists, the description correctly focuses on input semantics and behavior. It covers the data source (Coinbase candles), indicator timeframes, annualization, and parameter effects, providing everything an agent needs to call the tool correctly.

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 coverage is 0%, but the description fully documents all three parameters: symbol with examples, granularity with allowed values and its effect on annualization, and limit with stability and minimum requirements. This completely compensates for the schema's missing 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 description opens with a specific verb ('Compute') and names the exact technical indicators (RSI-14, EMA-20/50, ATR-14, annualized realized volatility) and the asset class (crypto asset). This clearly distinguishes it from siblings like market_pulse_price or market_pulse_candles, which serve different purposes.

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 description establishes clear context: this tool computes technical indicators from recent Coinbase candles, so an agent can infer when to use it (needing derived indicators). It does not explicitly name alternatives or exclusions, but the sibling names plus this context make the selection straightforward.

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