AlgoVault — Crypto Quant Trade Calls

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

Annotations already declare readOnlyHint=true and destructiveHint=false; description adds that it 'calls an LLM, no other side effects' and provides quota details. This goes beyond structured annotations by clarifying runtime behavior and consumption costs. No contradiction.

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, each with a distinct purpose: output definition, usage guidance with alternative, and side-effect/quota disclosure. 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?

With two parameters, no output schema, and strong annotations, the description covers purpose, usage, side effects, and quota. It tells the agent exactly when to use it, what it returns, and that it's read-only – sufficient for invocation decisions.

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?

Input schema has 100% description coverage, with 'question' and 'model' fully described. The tool description adds no parameter-specific information beyond the schema, so 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?

Description states it 'Returns a synthesized natural-language answer with citations, grounded in the AlgoVault knowledge bundle' – a specific verb, resource, and output. It distinguishes from sibling search_knowledge by noting LLM synthesis, and from other siblings by focusing on knowledge Q&A.

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 'Use when you need an explanation, code pattern, or how-to' and contrasts with search_knowledge: 'for raw ranked snippets without LLM synthesis use search_knowledge (faster, no quota cost)'. This provides clear when-to-use and an explicit alternative.

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, and the description reinforces this with 'Read-only, live exchange APIs.' It adds useful behavioral context beyond the annotations: the verdict is composite of trend/ranging and cross-venue funding rate, and the output includes confidence and a strategy hint. It does not cover rate limits or failure modes, but with annotations present the safety profile is sufficiently disclosed.

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 two sentences and front-loads the key deliverable: the market regime values, confidence, and strategy hint. The verification sentence is dense but not bloated, and there is no redundant padding.

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 read-only retrieval tool with three well-documented parameters and no output schema, the description supplies the return values, confidence, strategy hint, and composite methodology. It lacks explicit sibling routing and exact response structure, but those gaps are minor given the schema enum coverage and annotations.

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 baseline is 3. The description adds little about the exact parameters beyond limiting the tool to a single crypto perpetual futures asset. It does not add syntax, default semantics, or parameter-specific guidance beyond what the schema already provides.

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 names a specific verb ('Returns'), the resource ('market regime'), the possible output values, and the asset scope ('crypto perpetual futures'). It does not explicitly differentiate itself from siblings like get_trade_signal or get_trade_call, but the regime-value list makes the core purpose clear.

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 context 'for one crypto perpetual futures' implies when the tool is relevant, and 'Read-only, live exchange APIs' clarifies the nature of the call. However, there is no explicit guidance about when to use this tool versus alternatives, and the get_track_record mention is about validating track record rather than choosing between 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 declare readOnlyHint=true and destructiveHint=false; the description reinforces this with 'Read-only, no side effects.' It adds useful behavior beyond annotations: compact-by-default output, optional breakdown sections, and the fact that the same aggregate is served by performance://signal-performance. No contradiction.

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, each earning its place: purpose/content, alternative-resource context, and parameter/default behavior. The key return content is front-loaded before the include guidance, and there is no filler.

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 read-only aggregate tool with no output schema, the description tells the agent what data is returned (PFE win rates, methodology, window), how to control detail with include, and that it is safe to call. The annotations cover the safety profile, and no essential call-time behavior is missing.

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 schema already documents the single include parameter with enum values and the 'Omit for compact' guidance, so description coverage is 100% and the bar is lower. The description adds mild semantic color by calling byExchange 'per-venue' and framing the values as breakdown sections, but it does not substantially extend 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 opens with a specific verb and resource ('Returns the AlgoVault track record') and specifies the exact contents: aggregated PFE win rates by call type, timeframe, and asset tier, plus methodology and window. This clearly distinguishes it from sibling tools like get_trade_call and get_trade_signal, which return individual calls/signals rather than an aggregate record.

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?

It states when the compact default is appropriate and when to use the include parameter for per-asset, per-venue, or recent-signal breakdowns. It also notes the performance://signal-performance equivalence and harness-only callability. It does not explicitly name exclusion cases or sibling alternatives, but the usage context is clear.

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 mark the tool read-only and non-destructive; the description reinforces this with 'live exchange APIs, no orders,' which adds operational context beyond the hints. It also discloses the verified-provenance angle with get_track_record and the on-chain merkle anchor. 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?

Three compact sentences front-load the core behavior and scope before routing to alternatives. The track-record sentence is marginally tangential but still informative, and there is no real padding.

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 lookup with rich schema coverage, the description covers purpose, scope, alternative, and safety in a compact way. Without an output schema, it gives the main elements of the return (verdict, confidence, market regime) but not a complete response shape; still sufficient to call correctly with just the coin parameter.

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 each parameter already has descriptive text with defaults and examples. The description adds only the one-asset constraint and the composite-verdict framing, not new parameter-level semantics, so the schema carries the weight.

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 first sentence names a specific verb and resource: returns a composite BUY/SELL/HOLD verdict with confidence and market regime for a single perpetual-futures asset. It also explicitly contrasts with the whole-market scan sibling scan_trade_calls, so an agent can distinguish the tool without opening the schema.

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 'One asset only; whole-market scan: scan_trade_calls' sentence gives an explicit alternative and the condition that selects it. However, it does not explain how to choose between get_trade_call and the similarly named get_trade_signal, leaving that distinction implied rather than stated.

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 that it queries 'live exchange APIs, no orders', discloses alias equivalence with get_trade_call, and mentions a verified track record. This adds useful behavioral context beyond annotations, though it could detail latency or failure modes.

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 main output is front-loaded in the first sentence. Subsequent sentences each serve a purpose: scope, whole-market alternative, read-only safety, verification, and alias. It is dense but not bloated; the 'performance://signal-performance' reference is slightly cryptic but still 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 tool with five parameters, no output schema, and informative annotations, the description covers the return shape (BUY/SELL/HOLD, confidence, market regime), single-asset input, read-only behavior, and alias relationship. It could add default exchange/timeframe behavior or response structure details, but the current context is sufficient for correct invocation.

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?

Input schema coverage is 100%, so the baseline is already satisfied. The description adds meaning by clarifying 'One asset only' and 'crypto or tokenized-stock perpetual futures', which usefully constrains the coin and assetClass parameters beyond the schema examples.

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 opens with a specific verb and resource: 'Returns a composite verdict — BUY SELL HOLD trade call with confidence and market regime — for one crypto or tokenized-stock perpetual futures.' It explicitly distinguishes itself from scan_trade_calls ('One asset only; whole-market scan') and identifies the alias get_trade_call, so sibling confusion is minimized.

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 single-asset scope and routes whole-market scans to scan_trade_calls. It also points to get_track_record/performance://signal-performance for verification and advises preferring get_trade_call for new integrations, giving clear when-to-use and alternative 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the tool is known to be safe. The description adds behavioral context: results are ranked, cross-venue, with a composite verdict per pair, and mentions an on-chain verified merkle anchor, which is extra useful context beyond the annotations. It does not describe pagination or output format, but with annotations covering the safety profile, this is reasonably transparent.

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 a single, information-dense sentence that front-loads the core purpose (ranked cross-venue funding arbitrage) and adds relevant context (composite verdict, sibling references, on-chain anchor). It is concise and structured, though the trailing marketing phrases ('Crypto quant AI trading signal') add slight noise.

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 tool is relatively simple (2 optional params, no output schema) and the description covers purpose, scope, and references to related tools. It orients the agent on what to expect (ranked pairs, verdict) and that it is a read-only signal, which is adequate for selection and invocation. It could mention the output format, but lack of output schema reduces the burden.

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% (both limit and minSpreadBps are described in the schema). The description adds no parameter-specific semantics beyond mentioning funding rate spreads, but since the schema already fully describes the parameters, the baseline of 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 scans cross-venue funding arbitrage across major crypto perpetual futures venues, computes funding rate spreads, and outputs a BUY SELL HOLD composite verdict per pair. It uses a specific verb ('scan') and resource ('funding arbitrage'), and is distinct from siblings like get_trade_call or get_market_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?

The description mentions this is an 'AI trading signal' and points to alternatives via 'Trade call via get_trade_call, market regime via get_market_regime', providing directional guidance. It does not explicitly say when not to use this tool vs. alternatives, but the cross-references and clear scope imply usage context.

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 mark readOnlyHint=true and destructiveHint=false. The description adds context beyond that by stating it 'reads live exchange APIs' and 'places no orders,' and it discloses output characteristics: ranked calls, confidence, and market regime. There is 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?

Three sentences carry all essential information: what it returns, when to use it, and its safety profile. The main purpose is front-loaded, usage guidance comes second, and safety is concise. No filler or redundant restating of the name.

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 an 11-parameter, read-only market scanner with no output schema, the description covers purpose, breadth-versus-depth routing, safety, and the shape of the result. The remaining operational details (parameter meanings, defaults, constraints) are fully covered by the rich input schema.

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%, and the schema already thoroughly documents every parameter with defaults, ranges, enums, and condition-specific meanings. The description adds high-level context about whole-market scanning but does not need to repeat parameter details. 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 opens with a specific verb and resource: 'Returns ranked BUY SELL HOLD trade calls across the top crypto perpetual futures by open interest.' It clearly distinguishes itself from the sibling get_trade_call by framing this as whole-market breadth versus per-coin depth, so an agent can select it without inspecting schemas.

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 explicitly says 'Use this for breadth; use get_trade_call for per-coin depth and reasoning.' This provides a direct when-to-use rule and names the alternative, making the selection decision unambiguous.

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, so the safety profile is known. The description adds valuable behavioral context beyond annotations: 'Fast: BM25 lexical search, no LLM call, no quota cost.' This discloses performance characteristics and resource implications. It also reiterates 'Read-only, no side effects', reinforcing but not contradicting 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 serving a distinct purpose: purpose, usage timing, performance, and alternative tool. It is front-loaded with what the tool does and avoids fluff. No wasted 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?

With a simple 2-parameter schema, comprehensive annotations (readOnly, openWorld, non-destructive), and a clear description covering purpose, usage, performance, and alternatives, the tool definition leaves no significant gaps. Although there is no output schema, the description's 'ranked snippets' conveys the return format sufficiently.

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 both parameters (query, limit) are adequately described in the schema itself. The description does not add extra meaning beyond what the schema already conveys; it only mentions the query type indirectly ('answering a question'), which does not materially enhance parameter understanding. Baseline 3 is appropriate given high schema 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 uses a specific verb ('Returns') and resource ('ranked snippets from the AlgoVault knowledge bundle'), and clearly scopes the domain (MCP tools, response shapes, integration patterns, code examples). It also distinguishes itself from chat_knowledge by emphasizing 'ranked snippets' vs. synthesized answers, and from other siblings by focusing on knowledge lookup.

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 explicitly tells the agent when to use it: 'Call this BEFORE other tool calls to confirm parameter usage and avoid hallucinating tool shapes.' It also names the alternative for a different need: 'For a synthesized natural-language answer use chat_knowledge.' These are clear usage directives and exclusions.

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