AlgoVault — Crypto Quant Trade Calls

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

Annotations already declare readOnlyHint=true, so safe read. The description adds that output is synthesized with citations, uses LLM, and includes quota limits. No contradictions. Adequate beyond annotations.

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 well-structured sentences: purpose, usage guidance, alternative, and quotas. Front-loaded with key information. No unnecessary words.

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 simple input schema (2 params) and no output schema, the description explains output type and content, quotas, and when to use alternative. Complete for agent decision-making.

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 covers both parameters with descriptions (100% coverage). The description does not add additional meaning beyond what's in schema. Baseline 3 applies.

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 synthesizes natural-language questions into answers with citations, grounded in the knowledge bundle. It distinguishes from search_knowledge by specifying synthesized vs raw output.

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?

Explicitly says when to use (explanations, code patterns, 'how do I' answers) and when not to (use search_knowledge for raw snippets, which is faster and quota-free). Provides clear alternatives.

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

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

Annotations already indicate readOnlyHint=true, openWorldHint=true, destructiveHint=false. Description adds significant behavioral details: it returns trade call with confidence/regime/factors, routes to perp call if exchange/timeframe provided, and returns structured errors with suggestions. No contradictions.

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?

Description is comprehensive but slightly verbose (three sentences with moderate length). Key information is front-loaded. Minor room for tightening, but overall well-structured.

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?

No output schema, but description adequately explains the return value (trade call with confidence, regime, factors) and error handling. Covers routing logic, universe limitations, and parameter interactions. Complete for a read-only data tool.

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 further meaning: specifies that symbol accepts BRK-B or BRK.B, and that exchange/timeframe route to perp call with recommendation to use get_trade_call instead. This enriches the schema information.

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 it returns a daily-bar trade call (BUY/SELL/HOLD) for US stocks/ETFs, with confidence, market regime, and technical factors. It distinguishes from sibling get_trade_call by noting that crypto exchange or timeframe routes to perpetual-futures call, and that for crypto perps get_trade_call is preferred.

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?

Explicitly states when to use this tool vs alternatives: 'For crypto or tokenized-stock perps, prefer get_trade_call.' It also describes the universe (top US equities plus ETFs), handling of out-of-universe tickers, and routing behavior when exchange or timeframe is provided.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description adds valuable context: derivation from ADX/DI, Hurst exponent, and volatility compression, plus the four possible regime outputs.

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?

Single sentence with all key details (output types, derivation, default), no fluff.

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 read-only tool with one optional parameter and no output schema, the description fully explains what it produces and how.

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 single optional parameter 'symbol' has full schema coverage (description in input schema notes defaults). The tool description does not add further parameter semantics beyond that.

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 a market regime type (trending_up, trending_down, compression, or ranging) with a confidence score for a US equity or ETF, defaulting to SPY. This distinguishes it from siblings like get_market_regime (likely broader) and get_trade_signal.

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 mentions default to SPY, implying use for any US equity/ETF, but lacks explicit comparison to sibling tools or guidance on when not to use it.

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

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

Annotations indicate read-only; description adds composite signal blending trend, volatility, funding rate sentiment, and mentions verified track record and merkle anchor.

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 core purpose and outputs, no redundant information.

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?

Explains inputs and outputs (label, confidence, strategy hint) adequately, though could elaborate on confidence interpretation.

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%; description provides context (crypto perpetual futures) but does not add syntax or format details beyond 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?

Clearly states it classifies market regimes (TRENDING_UP, TRENDING_DOWN, RANGING, VOLATILE) for crypto perpetual futures on specific exchanges. Distinguishes from sibling get_equity_regime.

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?

Implied usage for crypto trading, but no explicit when-to-use or when-not-to-use compared to siblings like get_equity_regime or get_trade_signal.

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

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

Annotations already indicate read-only and non-destructive. The description adds that it returns market regime and mentions verified track record and on-chain anchor, providing extra behavioral context beyond annotations.

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 plus a tagline; front-loaded with the core purpose. No filler, every sentence serves a purpose. Highly concise.

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?

The description explains inputs and general output (market regime, verified track record), but lacks details on the exact return structure (e.g., object fields, error handling). Without an output schema, more specificity would improve completeness.

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% and descriptions for each parameter are present. The overall description adds value by explaining default values for exchange (Binance) and timeframe (15m), and advises against using those for US stock tickers—information 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?

The description clearly states the tool returns a BUY/SELL/HOLD trade call for crypto or US stocks. It distinguishes from siblings like get_equity_call by mentioning stock tickers, and implies it's composite compared to get_trade_signal. However, it doesn't explicitly name alternatives.

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 on how to use parameters based on asset type (crypto vs equity), but does not explicitly state when to use this tool over siblings like get_equity_call or get_trade_signal. No exclusionary guidance.

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

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

Beyond annotations (readOnlyHint, destructiveHint, openWorldHint), the description adds behavioral context: returns market regime, verdict, reasoning, and mentions 'Verified track record, on-chain verified merkle anchor,' which informs trustworthiness.

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?

Description is clear and front-loaded with purpose, but slightly wordy ('Composite verdict BUY SELL HOLD trade call' could be trimmed). Alias note adds value. Overall effective but not maximally concise.

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, description sufficiently explains return value (market regime, verdict) and parameter behavior. Includes alias for backward compatibility. High coverage of schema descriptions and 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?

Adds significant meaning beyond schema: explains coin accepts crypto or stock ticker, exchange defaults to Binance, timeframe defaults to 15m, assetClass forces engine, includeReasoning includes reasoning. Schema coverage is 100%, but description enriches semantic understanding.

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?

Clearly states it provides a 'Composite verdict BUY SELL HOLD trade call' for crypto or US stocks/ETFs, differentiating from siblings like get_equity_call and get_trade_call. Also notes it's an alias of get_trade_call, establishing equivalency.

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 specific usage guidance for crypto (exchange, timeframe defaults) and stocks (daily-bar read). While it doesn't explicitly state when not to use it, the alias note and context help differentiate from siblings.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds context about composite verdict and verification, but does not disclose potential behavioral details like data freshness or rate limits. Adequate with annotations.

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?

The description is somewhat verbose with marketing language ('AI trading signal', 'on-chain verified merkle anchor') that could be trimmed. Front-loaded with purpose, but some sentences are extraneous.

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 scanner with two parameters and no output schema, the description provides sufficient context about what the tool outputs (composite verdict, spreads). Could specify the format of results more clearly.

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%, so the schema already documents both parameters. The description does not add additional meaning beyond the schema, meeting baseline.

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 scans funding rate spreads across multiple exchanges (Binance, Bybit, OKX, etc.) for arbitrage, and distinguishes from sibling tools like scan_trade_calls by specifying its focus on funding arbitrage.

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 guidance on when to use this tool vs alternatives. It mentions targeting crypto quant and Claude trading agents but does not differentiate scenarios or exclusions from sibling tools.

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

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

Annotations already provide readOnlyHint=true and destructiveHint=false. The description adds useful behavioral context: mentions that HOLDs never cost quota (a detail not in annotations), that the scan is based on top perps by open interest, and that it returns ranked non-HOLD calls with confidence and market regime. No contradiction with annotations.

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?

The description is four sentences, each adding distinct value: tool definition, output summary, coverage statement, and sibling guidance. No redundant or unnecessary text; front-loaded with the core purpose.

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 6 parameters (none required), good annotations, and no output schema, the description covers the main use case, alternative tool, and a behavioral detail (quota cost for HOLDs). It does not explain the output structure (e.g., format of confidence/regime) but is adequate for an overview.

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?

With 100% schema coverage, the baseline is 3. The description does not repeat parameter details and adds little extra semantics beyond mentioning 'non-HOLD ranked first' and 'whole-market scan' which contextualize parameters but do not explain individual parameters beyond 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?

The description clearly defines the tool as a cross-asset market scanner that provides composite BUY/SELL/HOLD trade calls across top perpetual futures. It uses specific verbs ('scan') and resource ('trade calls'), and explicitly distinguishes itself from the sibling tool get_trade_call by noting that tool is for per-coin depth and reasoning.

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 states that this tool provides whole-market coverage for AI trading agents and explicitly directs users to use get_trade_call for per-coin depth, giving clear context on when to use this tool versus alternatives. However, it does not explicitly state when not to use it or list exclusions.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context: 'Fast (BM25 lexical search, no LLM call, no quota cost)', which explains performance and cost implications 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, with four sentences covering purpose, usage guidelines, behavioral traits, and a contrast with a sibling tool. Every sentence adds value, and the most critical information is front-loaded.

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 explains the return type ('ranked snippets'). It covers usage context, performance characteristics, and distinguishes from the only similar sibling. For a simple search tool with two parameters, this is complete.

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 the schema already documents both parameters. The description does not add new information about parameter semantics; it only reaffirms the roles ('natural-language search query', 'max ranked results'). Credit is baseline for full coverage.

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 purpose: to search a knowledge bundle for information about MCP tools, integration patterns, and code examples. It uses specific verbs ('ask', 'returns ranked snippets') and explicitly distinguishes itself from the sibling tool 'chat_knowledge', which provides synthesized answers.

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 provides explicit guidance on when to use the tool: 'before attempting any tool call to confirm correct parameter usage and avoid hallucinating tool shapes.' It also tells when not to use it and points to the alternative: 'For natural-language synthesized answers, use chat_knowledge instead.'

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