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Noon Barbari Backtesting

get_dca

Read-only

Dollar-cost-averaging outcome for a coin: what buying a fixed dollar amount on a schedule (weekly or monthly) since a start date would be worth today — total invested, units, average cost, current value and ROI — plus the lump-sum comparison and the worst drawdown endured. Real Binance closes, refreshed daily.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
coinYesLower-case ticker, e.g. btc, eth, sol.
amountNoUSD invested per purchase (default 100).
frequencyNoPurchase cadence (default weekly).
start_dateNoISO date to start buying from, e.g. 2021-01-01 (optional; default = full history).

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true (safe read). Description adds value by specifying data source ('Real Binance closes, refreshed daily') and confirming the tool only reports historical outcomes. No destructive behavior.

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?

Two sentences efficiently cover purpose, inputs, outputs, and data freshness. Front-loaded with key action and result set. No unnecessary words.

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?

No output schema exists, so description must fully document return values. It explicitly lists all returned metrics: total invested, units, average cost, current value, ROI, lump-sum comparison, worst drawdown. This is thorough for the tool's complexity.

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 description coverage is 100%, so baseline is 3. Description adds no extra semantics beyond what schema already provides (e.g., 'lower-case ticker' matches schema). Adequate but not enhanced.

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 clearly specifies verb ('get'), resource ('DCA outcome for a coin'), and key parameters (fixed dollar amount, schedule, start date). It lists all output metrics including lump-sum comparison and drawdown, making the tool's purpose unambiguous.

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?

No explicit guidance on when to use this tool vs siblings like 'get_buy_hold' or 'compare_strategies'. Usage is implied by the DCA focus, but the agent receives no help with tool selection decisions.

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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TDQS

A4.2/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: statistical checks, strategy comparison, historical returns, live signals, DCA, overfitting index, strategy listing, dataset query, backtesting, Q&A search, and glossary. No overlap in functionality.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., check_overfitting, run_backtest, search_glossary). No mixing of conventions.

Tool Count5/5

11 tools is well-scoped for a crypto backtesting server, covering the full workflow from strategy selection, backtesting, overfitting analysis, to educational queries without excess or deficiency.

Completeness4/5

The tool set covers core backtesting, overfitting diagnostics, data retrieval, and knowledge base searches. Minor gaps like strategy modification or saving results are present, but the main lifecycle is complete.

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