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FlawlessByte

quant-research-mcp

by FlawlessByte

quant_compute_indicators

Read-onlyIdempotent

Compute daily ATR and Hurst exponent, plus intraday EMA9/20, RSI, and VWAP for a US equity ticker, using recent data.

Instructions

Compute EMA9/EMA20, RSI, ATR, VWAP and the Hurst exponent for a ticker.

Daily series drive Hurst and ATR; the latest intraday session drives VWAP, EMAs and 5m RSI.

Args: params (TickerInput): ticker, response_format.

Returns: str: a dict with price, hurst, atr_daily, and intraday ema9/ema20/ rsi_5m/vwap. Error string if no data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, so the tool is clearly safe and non-destructive. The description adds valuable context about data sources (daily series for Hurst/ATR, intraday for VWAP/EMAs/RSI) and error handling ('Error string if no data'), which goes beyond what annotations provide.

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 very concise, with a clear structure: one-sentence summary, then details on data sources, followed by docstring-style args and returns. Every sentence is informative with no redundancy.

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?

Given the complexity (multiple indicators, different timeframes) and the presence of annotations and schema, the description covers the essential behavioral aspects (data sources, return keys) and error behavior. However, it could mention prerequisites like market data availability or whether the ticker must be actively traded.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% because the description only lists parameter names ('ticker, response_format') without adding any meaning beyond the schema's own descriptions. The schema already documents ticker as a required US equity symbol and response_format with enum, so the description adds negligible value.

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?

Description explicitly states the tool computes specific technical indicators (EMA9/EMA20, RSI, ATR, VWAP, Hurst exponent) for a ticker, with distinct action and resource. Sibling tools have clearly different purposes (e.g., analyzing setups, screening universes), so no confusion.

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?

Usage is implied through the description of the tool's function (computing indicators), but there is no explicit guidance on when to use this tool versus alternatives like quant_analyze_setup or quant_detect_regime. No exclusions or prerequisites 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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