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, openWorldHint, and non-destructive behavior; the description reinforces this with 'Read-only, live exchange APIs' and adds methodology context via 'Composite verdict: trend ranging + cross-venue funding rate.' The 'on-chain verified merkle anchor' note adds verifiability context, though the phrase 'trend ranging' is slightly ambiguous.

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 core purpose and output values are front-loaded, and the remaining clauses are short and additive. The track-record sentence is somewhat tangential and the 'trend ranging + cross-venue funding rate' phrasing is jargon-heavy, but the overall length is appropriate and not padded.

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?

Although there is no output schema, the description names the key returned elements — regime, confidence, strategy hint — and explains the data source and verifiability. It would be stronger with exact return field structure and clearer formatting of the regime enum, but it is sufficient for an agent to select and call the tool correctly.

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 all three parameters are already documented in the input schema. The description adds only general framing like 'one crypto perpetual futures' and 'composite verdict,' not parameter-specific semantics, so the 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 first sentence states a specific verb and resource: it returns the market regime, enumerates the possible values, and mentions confidence and a strategy hint for one crypto perpetual futures. It is clear enough to distinguish from the track-record tool by pointing to get_track_record, though it does not explicitly contrast with get_trade_signal or get_trade_call.

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 when the agent needs a market regime for a single crypto perpetual futures and points to get_track_record for verification, which is a helpful companion-routing clue. However, it does not explicitly state when to choose this tool over siblings like get_trade_signal, get_trade_call, or scan_funding_arb, and there are no when-not conditions.

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 declare readOnlyHint=true and destructiveHint=false; the description adds that it uses live exchange APIs and places no orders, which is useful operational context beyond the annotations. It also mentions the verified on-chain merkle anchor, providing extra transparency without contradicting the structured hints.

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-load the primary result, then give the key scoping constraint and sibling alternative, followed by read-only and verification context. Every sentence earns its place with no filler or repetition.

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 no output schema, the description still communicates the core return shape: BUY/SELL/HOLD verdict, confidence, and market regime. Combined with fully documented parameters and annotation-provided safety profile, the agent has enough to invoke the tool correctly.

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 all five parameters are already documented in the input schema. The description adds the one-asset constraint and output composition but not much new parameter-level detail, so the baseline score 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?

States 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 also distinguishes itself from siblings by explicitly naming scan_trade_calls as the whole-market alternative and get_track_record for track-record requests.

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 'One asset only; whole-market scan: scan_trade_calls,' giving a clear when-not and direct alternative. It also routes users wanting verified performance to get_track_record or performance://signal-performance, so usage context and alternatives are concrete.

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 goes beyond this by stating 'Read-only: live exchange APIs, no orders,' which clarifies the side-effect profile in operational terms. It also discloses that the tool is an alias with identical behavior to get_trade_call, and mentions the verified track record/merkle anchor, adding context beyond the structured annotations. 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 front-loaded with the core verdict and scope, followed by usage routing, read-only behavior, track-record pointer, and alias notice. Each sentence earns its place, though the track-record/merkle-anchor detail is slightly tangential for correct invocation. It remains well under an overlong length and is logically organized.

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 no output schema, the description usefully summarizes the return content: BUY/SELL/HOLD verdict, confidence, and market regime. It also covers single-asset scope, read-only behavior, and the alias relationship. It does not detail error conditions or exact output fields, but for a straightforward signal tool with rich annotations and full schema coverage, the essential context for correct invocation is present.

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 input schema already documents all five parameters with descriptions and enums. The tool description itself only adds 'One asset only,' which reinforces the coin parameter's singular scope but does not substantially enrich parameter meaning beyond the schema. 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 a composite verdict — BUY SELL HOLD trade call with confidence and market regime — for one crypto or tokenized-stock perpetual futures.' It clearly distinguishes this tool from the whole-market scan (scan_trade_calls) and identifies itself as an alias of get_trade_call, so an agent can immediately understand what it does and how it differs from siblings.

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?

Explicit usage routing is provided: 'One asset only; whole-market scan: scan_trade_calls' tells the agent when to use this versus the sibling. The alias note adds 'Prefer get_trade_call for new integrations,' which is direct guidance on alternative selection. No ambiguity remains about when this tool is appropriate.

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 declare readOnlyHint=true and destructiveHint=false, and the description reinforces this with 'Read-only: reads live exchange APIs, places no orders.' It adds further behavioral context beyond annotations: the tool consumes live exchange data (real-time, not cached) and a single call covers the entire universe via one scan, returning a sortable list with confidence and regime metadata.

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 job: (1) the verb, resource, scope, and return fields, (2) routing to the right sibling, (3) safety behavior. The most decision-relevant information is front-loaded and there are no filler words, placeholder text, or redundant restatements of the tool 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?

Given the tool's complexity — 11 optional parameters, 4 enums, no output schema — the schema descriptions handle the parameter semantics fully, and the description covers the return shape ('each with confidence and market regime'). This is nothing missing about the unexamined parameter usage guidance is clear, but a brief note about the result ordering (non-HOLD ranked first) is delegated to the schema, which is acceptable.

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 all 11 optional parameters are fully documented in the schema (e.g., topN, rankBy lenses, oiBasis, exchange list, timeframe, includeHolds, minConfidence). The description adds no parameter-level detail of its own, but it doesn't need to; the schema carries the full parameter documentation burden, and the baseline of 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 states a specific verb and resource: it returns 'ranked BUY SELL HOLD trade calls across the top crypto perpetual futures by open interest'. It also distinguishes itself from the sibling get_trade_call by framing this tool as the whole-coverage breadth entry point, so an agent knows exactly what this scan does versus the per-coin depth tool.

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 explicitly routes the agent: 'Use this for breadth; use get_trade_call for per-coin depth and reasoning.' This gives a clear selection condition (whole-market breadth vs. single-coin depth) and names the exact alternative, leaving no ambiguity about when to pick this tool over its closest sibling.

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.