Noon Barbari Backtesting
Server Details
Crypto backtesting tools: real backtests with robustness verdicts, daily signals and market data.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Usage analytics
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Tool Definition Quality
Average 4.3/5 across 7 of 7 tools scored.
Each tool targets a distinct function: backtesting, comparison, signals, overfitting, buy-and-hold, strategy listing, and knowledge base. No functional overlap.
All tool names follow a consistent verb_noun pattern using snake_case (e.g., compare_strategies, get_coin_signals, run_backtest).
7 tools is well-scoped for a backtesting server, covering core workflows without unnecessary bloat or deficiency.
Core backtesting lifecycle is covered (list, run, compare, analyze signals/overfitting), but lacks custom parameter options and multi-coin backtesting beyond BTC/USDT.
Available Tools
7 toolscompare_strategiesAInspect
Head-to-head comparison of two strategy templates from real monthly engine runs across ~50 coins: per-coin win count, median out-of-sample Sharpe, survival counts, median return and drawdown. Use strategy names from list_strategies (e.g. 'super_trend', 'ema_crossover').
| Name | Required | Description | Default |
|---|---|---|---|
| strategy_a | Yes | First strategy name, e.g. super_trend | |
| strategy_b | Yes | Second strategy name, e.g. ema_crossover |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description explains that results come from real monthly engine runs across ~50 coins and lists the metrics. Without annotations, it adequately conveys behavior, though it could explicitly state it is a read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the core purpose, no wasted words. Every sentence is informative and earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with 2 parameters and no output schema, the description fully covers what the tool does, the required input source, and the output metrics. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions. The description adds value by linking parameters to list_strategies and providing examples (e.g., 'super_trend'), fully compensating for general parameter names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a head-to-head comparison of two strategy templates with specific metrics (win count, Sharpe, drawdown, etc.), which distinguishes it from sibling tools like list_strategies or run_backtest.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description instructs to use strategy names from list_strategies, providing examples. However, it does not explicitly mention when not to use this tool or alternative tools for single-strategy analysis.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_buy_holdAInspect
What a $1,000 buy of a coin on Jan 1 of each available year would be worth today — ROI, peak value and date, and the maximum drawdown endured along the way. Real Binance data, refreshed daily.
| Name | Required | Description | Default |
|---|---|---|---|
| coin | Yes | Lower-case ticker, e.g. btc, eth, sol. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses data source ('Real Binance data, refreshed daily') and output components (ROI, peak value/date, max drawdown). However, it doesn't mention assumptions (e.g., no fees, only Jan 1 buys) or output format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with no wasted words. Front-loaded with the core purpose, then additional details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no output schema), the description sufficiently covers what the tool does and returns. However, it lacks specifics on output structure (e.g., yearly values vs single summary) which could be inferred.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with parameter 'coin' already described as 'Lower-case ticker, e.g. btc, eth, sol.' The tool description adds no additional parameter meaning beyond the schema, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: calculate the value of a $1,000 buy of a coin on Jan 1 each available year, showing ROI, peak value/date, and max drawdown. It uses specific verb 'get' and resource 'buy_hold', and is distinct from sibling tools like run_backtest and get_coin_signals.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidelines on when to use this tool versus alternatives like run_backtest or list_strategies. The description implies it's for buy-and-hold analysis, but doesn't exclude other contexts or mention prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_coin_signalsAInspect
Today's daily-bar indicator readings for crypto coins, computed by a real backtesting engine from Binance closes: price, RSI-14, MACD state, SMA 50/200 posture, SuperTrend, Bollinger position, ATR volatility, 52-week range. Pass a coin ticker (e.g. 'btc') for one coin, or omit for the whole 50-coin board.
| Name | Required | Description | Default |
|---|---|---|---|
| coin | No | Lower-case ticker, e.g. btc, eth, sol. Omit for all coins. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description bears full responsibility. It states the data is computed by a real backtesting engine from Binance closes, but does not disclose additional behavioral traits such as data freshness, rate limits, or side effects, leaving some gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with key indicators and source, followed by parameter guidance. No unnecessary words; every sentence is informative and earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description lists many indicator names but does not specify the output format (e.g., JSON structure) or data timing beyond 'today's daily-bar'. However, it sufficiently covers the tool's essential functionality for an AI agent to understand its use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but description adds significant value: explains the effect of providing vs. omitting the 'coin' parameter, gives an example ticker ('btc'), and clarifies that omitting returns the entire 50-coin board, which is not in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description specifies exactly what the tool does: returns today's daily-bar indicator readings for crypto coins, listing specific indicators (RSI-14, MACD, etc.). It distinguishes from siblings by clearly identifying its unique function of providing coin signal data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description explains usage: pass a coin ticker for a single coin or omit for all 50 coins. It implies when to use by stating it's for today's readings, but does not explicitly mention when not to use or compare to alternatives like 'get_buy_hold'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_overfitting_indexAInspect
The Crypto Overfitting Index: the monthly share (%) of default-parameter strategy configurations (10 templates × 50 coins) whose out-of-sample Sharpe turned negative — how much of what backtests promise fails on unseen data. Returns the current reading and full history.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description reveals the tool computes a monthly share using 10 templates × 50 coins and tracks negative out-of-sample Sharpe. It also states returns both current reading and history, offering sufficient behavioral insight.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences that are efficient and front-loaded with the definition and purpose. Every word adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a no-parameter tool without output schema, the description explains the calculation basis and return type (current reading and history). It could be slightly more specific about the output format, but overall it's adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so no parameter documentation is needed. The description adds no param info but meets the baseline expectation for a parameterless tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns the monthly share of default-parameter configurations that failed on unseen data, specifically the out-of-sample Sharpe turning negative. It distinguishes itself from sibling tools like run_backtest or compare_strategies by focusing on overfitting measurement.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for assessing overfitting in crypto strategies but does not explicitly state when to use versus alternatives or provide prerequisites. Context signals show siblings are for other purposes, making the usage clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_strategiesAInspect
List the strategy templates available for backtesting and comparison (name, title, one-line description). Use the returned name value as the strategy identifier in other tools.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, and description does not explicitly state read-only nature or other behaviors. However, listing is inherently non-destructive, and the description accurately covers what the tool does without misleading. Lacks explicit behavioral disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose, no redundant information. Every sentence serves a clear purpose: stating what the tool does and how to use its output.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and no output schema, the description sufficiently explains the tool's purpose and return format. Provides enough context for an agent to understand the tool's role in a workflow.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters required, schema coverage 100% (empty). Description adds value by detailing the returned fields (name, title, description), which compensates for lack of output schema. Baseline for zero parameters is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool lists strategy templates available for backtesting and comparison, specifying exact fields (name, title, one-line description) and their use as identifiers. Distinguishes from sibling tools that perform actions like comparison or backtesting.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance to use the returned 'name' value as strategy identifier in other tools, contextualizing when to use this tool first. Does not mention when not to use or alternatives, but sibling tools imply different purposes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_backtestAInspect
Run a real backtest of a strategy template on BTC/USDT from a start date (public what-if engine; may take up to a minute on a cache miss; rate-limited). Returns net return, max drawdown, trade count, a robustness score with an overfitting verdict, and a shareable result URL.
| Name | Required | Description | Default |
|---|---|---|---|
| strategy | Yes | Strategy name from list_strategies, e.g. super_trend | |
| start_date | Yes | ISO date, e.g. 2022-01-01 (2020-01-01 or later) | |
| starting_cash | No | Starting balance in USD (default 10000, max 1000000) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavioral traits: it is a public, what-if engine, may take up to a minute on cache miss, and is rate-limited, which goes beyond simple 'run backtest'.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no unnecessary words. Front-loaded with the core purpose and followed by return details and behavioral notes. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description explains return values and key behavioral aspects (public engine, cache, rate limits). It is complete for a backtest tool of this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so parameters are well-documented. The description adds no extra meaning beyond the schema, though it reinforces the context (e.g., strategy from list_strategies). Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool runs a real backtest of a strategy template on BTC/USDT from a start date, listing specific return fields. It distinguishes from siblings like compare_strategies and list_strategies by focusing on a single backtest.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides context like public what-if engine, cache misses, and rate-limiting, but does not explicitly state when to use vs alternatives or provide exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_answersAInspect
Search Noon Barbari's Q&A knowledge base of direct, data-grounded answers about backtesting, overfitting, validation, indicators, risk management and crypto markets. Returns the top matching questions with their full answers.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Free-text query, e.g. 'why do backtests fail' |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It indicates a read-only search operation returning top matching results but does not disclose limits like result count, pagination, or rate limits. Adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences with no wasted words. The first sentence front-loads the purpose, and the second clarifies the output. Excellent economy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter search tool with no output schema, the description fully covers what the tool does and what it returns. No additional detail is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with one required string parameter and an example in its description. The tool description adds domain context ('backtesting, overfitting...') but does not add parameter detail beyond the schema, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Search' and the resource 'Noon Barbari's Q&A knowledge base', specifying the domain of backtesting, overfitting, etc. It returns top matching questions with answers, which distinguishes it from sibling tools that handle strategies or signals.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for questions about backtesting and related topics but does not explicitly state when to use it versus alternatives like compare_strategies or get_buy_hold. No when-not guidance is provided.
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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