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

Despite annotations already declaring read-only, the description adds valuable operational context beyond them: it calls an LLM (implying cost/latency), has no other side effects, and lists specific quota limits per plan (Free 10, Starter 50, Pro 200, Enterprise 2000). This exceeds the annotation baseline.

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 three concise sentences, each with a distinct purpose: what it does, when to use it (and when not), and operational constraints (read-only, quota). No redundant or filler content.

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, combined with a fully documented schema and annotations, covers the tool's return type, use cases, alternatives, quota, and safety profile. Since there is no output schema, the explicit mention of 'synthesized natural-language answer with citations' sufficiently communicates the expected response.

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 input schema provides 100% coverage for both parameters (question with length constraints, model with enum options). The description adds no additional parameter semantics, which is the expected baseline when schema coverage is high.

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'), identifies the resource ('AlgoVault knowledge bundle'), and specifies the output ('synthesized natural-language answer with citations'). It also explicitly distinguishes the tool from the sibling search_knowledge by contrasting LLM synthesis vs. raw ranked snippets.

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?

Clear usage guidance is provided: 'Use when you need an explanation, code pattern, or how-to', and an explicit alternative is named ('for raw ranked snippets without LLM synthesis use search_knowledge'), including the trade-off that it is faster and has no quota cost.

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. The description adds meaningful context: outputs are ranked, include a composite verdict, and are 'on-chain verified merkle anchor' – enriching the behavioral picture beyond the annotations without 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?

The description is only three sentences with the core purpose front-loaded. The second sentence ('AI trading signal...') is somewhat promotional, but the overall structure is efficient and no essential details are buried.

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 no output schema, the description gives a reasonable sense of what is returned (ranked pairs, spread, separate verdict, merkle anchor). It could be more explicit about output fields, but given the simple params and annotations, it is sufficiently 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 descriptions cover both parameters (limit, minSpreadBps) with defaults and ranges (100% coverage). The description does not add further parameter-level meaning, so 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 and returns ranked per-pair spreads and BUY/SELL/HOLD verdicts. The specific verb 'scan' and resource 'funding arbitrage' distinguish it from siblings like scan_trade_calls and 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 names related tools (get_trade_call, get_market_regime) providing useful alternatives for specific needs. However, it does not explicitly contrast with scan_trade_calls or mention when not to use this tool, falling just short of full 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 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 cover read-only and non-destructive hints. The description adds valuable context: fast BM25 lexical search, no LLM call, no quota cost, and explicitly 'Read-only, no side effects.' This goes beyond the annotation basics, though it doesn't detail result structure or pagination, which is acceptable given the tool's simplicity.

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: functionality, usage timing, and alternative. Front-loaded with the core purpose, then key behavioral notes. No fluff or repetition of schema fields.

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, two-parameter tool with no output schema, the description covers purpose, when to use, performance characteristics, safety, and alternative tool. It is sufficiently complete for an agent to select and invoke 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 coverage is 100%, so the baseline is 3. The description adds meaning by explaining the query is a natural-language question about specific topics (MCP tools, response shapes, etc.), and reinforces the limit as controlling 'ranked results'. This is more than the schema alone 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 uses a specific verb ('Returns') and resource ('AlgoVault knowledge bundle') with clear scope: ranked snippets answering questions about MCP tools, response shapes, integration patterns, or code examples. It also explicitly distinguishes itself from sibling chat_knowledge by noting that chat_knowledge provides synthesized natural-language 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?

Provides explicit when-to-use guidance: 'Call this BEFORE other tool calls to confirm parameter usage and avoid hallucinating tool shapes.' Also names the alternative: 'For a synthesized natural-language answer use chat_knowledge.' This gives clear context and exclusions.

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