newsgurus-intel

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

With no annotations, the description carries the full burden. It discloses the per-call cost, confirms it is a read-only liveness probe, includes a disclaimer that it's educational data not financial advice, and explains payment methods. This is substantial transparency, though it doesn't cover potential error behaviors or rate limits.

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 lengthy but every part serves a purpose: pricing, functionality, use case, payment instructions, and a link to a free catalog. It is front-loaded with price and purpose, and while payment details could be condensed, the structure is logically organized and does not contain 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 no-parameter liveness probe, the description covers the key aspects: what it returns, how to pay, caution about educational data, and a pointer to explore other tools. It lacks details on response format or potential failure modes, but given the lack of an output schema and the simplicity of the tool, it is adequately 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?

The tool has zero parameters and the schema coverage is 100%, so the baseline is 4. The description adds no parameter details because there are none, which is appropriate. It does not need to explain anything beyond what the schema shows.

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 is a liveness probe that returns per-agent intelligence output counts and last-seen timestamps over the last 24h across 40+ data agents. This is specific, uses a distinct verb (get) and resource (agent status), and distinguishes it from sibling data-retrieval tools by framing it as a health check.

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 tells the agent when to use this tool: 'Verify the engine is live before buying data' – a clear use case. It also mentions a price comparison and points to get_catalog for browsing tools, but does not explicitly enumerate alternative tools to use instead of this one, so it's slightly below a 5.

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?

With no annotations, the description must disclose behavioral traits. It does include pricing ($0.01 per call), a disclaimer (educational data, not financial advice), and payment methods (x402, API key). However, it does not describe whether the tool is read-only, how errors or rate limits are handled, or what the actual response format is (e.g., list vs. feed). It also mentions a 'feed' but doesn't clarify if this is streaming or a one-time response.

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 overly verbose, mixing essential purpose with payment instructions, disclaimers, and a pointer to get_catalog. The core purpose is stated early, but the bulk of the text is unrelated to the tool's functionality. This dilutes the message and adds unnecessary length for a tool with a single optional parameter.

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 has only one optional parameter and no output schema, the description provides some context about the content (types of anomalies) but fails to explain the 'limit' parameter or the response structure. It also doesn't mention any pagination or data freshness. The disclaimer and payment info are useful but do not compensate for missing functional details.

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 only parameter, 'limit', is completely undocumented in the description. The schema provides a default of 10 but no explanation of its meaning or effect. With schema description coverage at 0%, the description fails to compensate, leaving the agent to guess what this parameter controls (likely the number of anomalies to return).

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 provides recent anomaly detections and lists specific types (pump/psyop spikes, rug precursors, coordinated wallet activity, data-integrity divergences). The name 'get_anomalies' aligns with this purpose. However, it doesn't explicitly differentiate from sibling tools like 'get_signals' or 'get_recent_intel' beyond the name, so it's not a full 5.

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 does not provide guidance on when to use this tool versus alternatives. It mentions browsing all tools with get_catalog, but does not explain under what circumstances one would prefer anomaly detections over other signal types. No exclusions or alternative recommendations are given.

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?

With no annotations provided, the description fully carries the burden. It discloses cost per call, the exact fields returned, optional filters, ordering by created_at DESC, proprietary computation, the educational/non-advice disclaimer, and two payment/auth methods (x402 or subscriber API key). This is exceptionally transparent for a read-only feed.

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 long but every sentence provides necessary value: data content, filters, ordering, pricing, authentication, and a pointer to get_catalog. It is front-loaded with the core purpose and structured logically.

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 (paid API, auth, filters), the description covers pricing, payment methods, parameter semantics, output fields, ordering, and disclaimers. No output schema exists, so listing the expected fields is sufficient. It even directs users to a catalog for full context.

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 has no descriptions (0% coverage), but the description explains both parameters precisely: symbol as an optional LIKE filter and limit with a default of 25 and cap of 50. This adds critical meaning beyond the raw schema types and defaults.

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 multi-source confluence signals feed from the live apex_signals table, listing specific fields (symbol, direction, score, entry price, timeframe, momentum_surge flag). It distinguishes itself from sibling tools by naming the APEX v10.2 feed and proprietary News Guru computation.

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 tells the agent to browse all tools and prices with the free get_catalog tool and describes the specific data source/table, which implies when this tool is relevant. However, it does not explicitly name alternative signal tools or state when not to use this one.

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?

Discloses cost ($0.05 per call) and payment methods (x402 or API key), which is critical behavioral context not covered by annotations. Also adds disclaimer about educational data. Does not describe rate limits or failure modes, but is transparent about auth and cost.

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 long and includes extensive payment instructions and catalog referral, which is general boilerplate rather than tool-specific. While front-loaded with cost and purpose, it could be more concise; the payment details could be separated into overall API docs.

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 zero-parameter, no-output-schema tool, the description lists the main data components returned (dormant moves, APEX signals, regime) and notes educational status. It lacks explicit return format or recency details, but adequately covers the tool's scope and requirements for an AI agent.

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?

Tool has zero parameters, so the description does not need to explain parameter behavior. Per guidelines, baseline is 4 given no parameters. The description adds nothing about parameters, but that is appropriate since there are none.

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 the tool provides a consolidated BTC snapshot including dormant wallet movement state, recent dormant-wallet moves with USD value and years dormant, APEX confluence signals, and market regime. Distinguishes from siblings like get_satoshi_moves or get_market_regime by combining multiple data points in one 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?

Implies usage: use this for a comprehensive one-call BTC read. It does not explicitly say when not to use it or compare with alternatives. Mentions checking the catalog first, but no direct exclusion or 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?

With no annotations, the description bears full burden. It discloses that the tool is free, returns listing of tools with prices (USDC on Base via x402), schemas, and example output. It does not mention any destructive or rate-limiting behaviors, but as a read-only catalog, this is sufficient.

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 with no extraneous words. The key term 'FREE' is front-loaded, and each sentence adds unique value: what the tool lists, and why to call it first.

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 fully covers what the tool does, its output contents (tools, prices, schemas, example output), and its purpose (decision tool). Given the tool has no parameters and an output schema exists, 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?

The input schema has zero parameters, so baseline is 4. The description correctly does not need to explain parameters.

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 lists every News Gurus Intel tool with price, input schema, and example output. It distinguishes itself from sibling tools which are specific data tools, making it clear this is a catalog 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 says 'Call this first to decide what to buy,' providing a clear when-to-use directive. It also mentions no payment required, but does not explicitly state 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?

With no annotations, the description carries full responsibility for behavioral disclosure. It discloses the cost ($0.05 per call), explains payment mechanisms (x402 or API key), and adds the disclaimer 'Educational data, not financial advice.' It does not explicitly state read-only, but that is inherent. It also mentions output nature (counts and average confidence), which is beyond annotations. This is solid behavioral context, though it could mention rate limits or response format.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is fairly long, including cost, function, payment instructions, and disclaimer in a single paragraph. It is front-loaded with cost and function, which is good, but the payment details add length. While every sentence serves a purpose, it could be more concise by separating payment info into a clearer structure. It is not terse, but it is organized. A 3 reflects the slight over-length.

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 one parameter and no output schema, this description is quite complete. It explains the output shape (bullish/bearish counts and average confidence), covers payment and authentication, and references get_catalog for broader context. It lacks example symbol format but overall provides sufficient context for an agent to use the tool correctly. A 4 is appropriate.

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 has a single required 'symbol' parameter with 0% description coverage. The description says 'for one symbol' and implies the symbol is needed, but does not specify format (e.g., ticker, case-sensitivity). For a single obvious parameter, this is adequate, but it does not add much beyond the schema's parameter name. Given the simplicity, a 3 is fair.

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: 'composite signal read for one symbol: latest 50 signals across all generators merged into bullish/bearish counts and average confidence.' This is specific (verb: get/read, resource: composite signal, scope: one symbol) and differentiates it from sibling tools like get_signals or get_symbol_sentiment by emphasizing it merges all generators. The phrase 'flagship per-symbol conviction snapshot' further cements its unique role.

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 when to use it—as the flagship per-symbol snapshot—but does not explicitly contrast with alternative tools. It advises browsing all tools via the free get_catalog tool, giving users a path to discover alternatives. However, it lacks explicit 'when not to use' guidance, though the positioning is clear enough. It earns a 4 for providing context and pointing to catalog.

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?

With no annotations provided, the description carries the behavioral disclosure burden. It covers pricing, payment settlement via x402, subscriber API key options, and the educational/non-financial-advice nature of the data. It lacks explicit statements about data freshness or error behavior, but the key operational traits are 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 core function is front-loaded in the first sentence, but the description becomes lengthy with pricing, payment instructions, marketing comparisons, and a pointer to get_catalog. Most content earns its place for a paid API, but it could be tightened without losing essential guidance.

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 two-parameter tool with no output schema, the description is reasonably complete: it lists the return data fields, optional symbol filtering, payment/auth requirements, and the educational caveat. It does not explain the limit parameter's behavior or pagination, but the core use case and access details are well covered.

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 0%, so the description must compensate. It explains the symbol parameter as an optional filter, but the limit parameter is entirely unmentioned. Since limit has a default of 25 and is otherwise self-explanatory, the description still provides partial value, but fails to fully document all parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: retrieving recent US congressional stock transactions with specific data fields (member, party, state, buy/sell, ticker, amount range, dates). It also differentiates itself from the many sibling get_* tools by focusing on congressional trades and offering an optional symbol filter.

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 clear context for when to use the tool (getting congressional trades, as a per-call alternative to subscription data plans) and how to access it (x402 payment or API key). It does not explicitly list exclusions or name sibling alternatives, so it stops short of full when-not-to-use 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?

With no annotations, the description carries the burden. It discloses that raw notional/price figures are not redistributed and mentions payment mechanisms, but it does not state whether the operation is read-only or describe rate limits, pagination, or error conditions. It adds some value but omits potentially critical behavioral details.

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 its core purpose, then adds payment and catalog references. It is slightly verbose but each section adds necessary operational information (payment, availability). The structure is logical, though it could be trimmed without losing critical details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with two optional parameters and no output schema, the description covers the output fields and behavioral constraints. However, it lacks explicit usage context versus siblings and omits any mention of response format or pagination. It is adequate but not fully complete for informed selection without additional cues.

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 0%, so the description must explain all parameters. It does explain 'hours' as a lookback window with a default of 48, but it does not mention 'limit' at all. The description also fails to clarify the meaning or possible values for 'limit', leaving a gap for agents.

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 it retrieves recent institutional dark-pool prints with specific output fields (symbol, direction, strength, confidence, size_bucket). The verb 'get' and resource 'dark-pool prints' are explicit and distinct from many siblings, making the purpose unambiguous.

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 explains the lookback parameter (hours) but provides no guidance on when to choose this tool over siblings like get_institutional_moves or get_options_flow. It lacks explicit alternatives or exclusion criteria, leaving the agent without clear context for selection.

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?

With no annotations provided, the description carries the full behavioral burden and discloses the $0.25 per-call cost, the x402 automatic payment flow, subscriber API key alternatives, and the 'educational data, not financial advice' disclaimer. It does not detail response shape or data freshness, but the payment and access model is a significant transparency add.

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 purpose is front-loaded and the payment/authentication section is clearly separated with a 'HOW TO PAY' header. There is some redundancy ('per call' appears twice, and 'a whole ranked universe per request' restates 'in one call'), but overall the length is justified by the essential billing and auth details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Without an output schema or annotations, the description provides a good high-level picture of the returned universe: percentile-scored quality-compounder rankings plus LEAPS candidates, with payment and auth context. It leaves minor ambiguity about exact response fields and ordering, but is complete enough for a parameterless ranked-list endpoint.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so the empty input schema leaves nothing to explain and there is no parameter-meaning gap. Baseline 4 is appropriate for a parameterless tool.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as returning quality-compounder equity rankings and growth-stock LEAPS candidates, with explicit detail on the included equities and crypto protocols. The 'PLUS' construction and specific data categories (high-margin/low-debt/high-cash/growing-revenue) distinguish it from the many sibling get_* tools.

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 conveys that this is a one-call full-universe rankings endpoint and explains how to pay, but it does not explicitly state when to prefer this tool over alternatives like get_signals or get_apex_signals. The pointer to get_catalog is only general discovery guidance, not a direct use-vs-alternative comparison.

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?

No annotations were provided, so the description carries the full burden. The description discloses that this is a paid API call, explains the pricing model, mentions required auth mechanism, and warns it's educational data. It also mentions what it returns (signal, direction, score, momentum surge, timeframe, market regime). However, it doesn't disclose what happens if the symbol isn't found, or what the rate limits are, or what 'latest' means in terms of data staleness. No annotations exist, so it gets a baseline 3 for having some transparency but with gaps like not describing response behavior or failure modes. The '[this is an API]' is described as educational data — that's a transparency plus.

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 information-dense but slightly run-on in structure. The first sentence packs in price, data source, and what it returns. The second sentence crams the 'HOW TO PAY' instructions with authentication methods and a pointer to get_catalog. Every sentence has a purpose, and the '[$0.05 per call]' prefix is a good front-loaded detail. However, it's a bit of a wall of text; the payment/authentication detail could be more concisely organized. It could be restructured: put the 'what it does' first (which is already mostly first), then pricing, then payment, then disclaimers. The 'not financial advice' disclaimer is a valuable addition, though.

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 only 1 parameter and no output schema, the description has to carry the load for usage, and it largely does. The description mentions 'latest APEX multi-source confluence signals' and 'current market regime' — enough to understand what it returns. The biggest gaps: sample usage example (though CSS might cover it) and explicit listing of accepted symbol formats. For a one-param tool with no output schema, it's fairly complete: it covers what, cost, auth/payment, and points to free tools for discovery. Slight deduction because it doesn't clarify what happens if the symbol is invalid or the atoms of the 'fingerprint' — but for the tool's stated purpose, it's mostly 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 description coverage is 0%, meaning the parameter 'symbol' is NOT described in the schema beyond its type. The description partially compensates by giving examples like '(BTC, ETH, SOL, XRP...)' and explaining it's for one symbol. However, it doesn't specify the exact accepted format (e.g., is it 'BTC' or 'BTCUSDT' or 'BTC-USD'?), case sensitivity, or whether full asset names are accepted. The description adds some value by listing example symbols and emphasizing the singular nature ('one symbol'), but doesn't fully compensate for the missing schema documentation with format requirements. A 3 is fair because we know it's a required string and can guess format from examples, but ambiguity remains about symbol format.

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 this is a crypto futures/derivatives read tool for one symbol, with specific examples (BTC, ETH, SOL, XRP) and details about what it returns (APEX multi-source confluence signals, market regime). It has a specific verb+resource combination ('get futures snapshot' for crypto), distinguishing it from siblings like get_btc_snapshot (which may be BTC-specific) and get_market_regime (which may be more general). The 'for one symbol' clarifies its single-asset scope, contrasting with broader market tools. However, it doesn't explicitly distinguish itself from get_btc_snapshot or get_composite_signal, which could be the closest 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?

The description provides explicit context for when to use this tool, particularly that it's for cryptocurrency futures/derivatives snapshots for a single symbol. The sibling list shows alternatives like get_btc_snapshot (probably BTC-only) and get_market_regime (probably broader), and the description clarifies that this is crypto-futures-focused. It also explains the payment mechanism (how to pay and use: 'x402-capable client' or 'subscriber API key'). The caveat 'Educational data, not financial advice' is a usage limitation. However, it doesn't explicitly state when NOT to use this tool and which alternative to use instead. The pricing info '[$0.05 per call]' and reference to get_catalog for free browsing are useful usage guidance. It could have been a 5 with an explicit when-not-to-use or sibling comparison.

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?

With no annotations, the description carries the full burden and does well: it discloses the payment mechanism, authentication options, the educational/non-advice nature, and the limitation that raw exposures are not provided. It does not cover rate limits or error behavior, but the core behavioral traits are clearly stated.

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 dense block of text with pricing, payment, auth, and catalog guidance mixed together. It is front-loaded with the core purpose, but the 'HOW TO PAY' section and catalog pointer add length. It is informative but not 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?

For a one-parameter tool with no output schema, the description covers the main output fields, the tool's limitations, and the access/payment model. It does not detail the exact JSON response structure, but the named outputs and constraints make it reasonably complete for an agent to 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 0%, so the description must compensate. It adds meaning by specifying the parameter is an equity/ETF symbol, but it does not clarify format, case sensitivity, or examples. For a single simple parameter this is adequate but not rich.

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 provides dealer-regime classification for one equity/ETF symbol, including specific output labels (positive_gamma_pinning, negative_gamma_amplifying) and gamma_bias. This is a specific verb+resource+scope that distinguishes it from sibling tools like get_market_regime or get_volatility_scan.

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 gives context for use ('for squeeze and volatility analysis') and notes that raw gamma dollar exposures are not redistributed, implying this tool is for regime classification rather than raw data. However, it does not explicitly name alternatives or state when not to use this tool versus 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?

With no annotations, the description carries the full burden and it covers key behavioral context: $0.05 per call, x402 payment/authentication flow, API-key alternatives, and an educational-data disclaimer. It does not mention rate limits or pagination, so it is strong but not exhaustive.

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 longer than average but well-structured and front-loaded: first the tool's purpose and data, then payment/authentication, then a pointer to get_catalog. Each clause adds necessary commercial/operational context.

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 two-parameter tool with no output schema, the description provides the essential return fields, optional filtering, cost, and auth. The main gap is the undocumented limit parameter, but the otherwise complete context makes the tool safely invokable.

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 0%, so the description must compensate; it explains the optional symbol filter but is silent on the limit parameter, its default behavior, or value range. This leaves the agent to infer limit from its name and default value.

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 action and resource: 'recent institutional 13F position moves' with enumeration of position types (new/increased/decreased/sold) and output fields. This differentiates it from sibling tools like get_institutional_stock by focusing on moves rather than static stock data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The intended use is implied by the description ('recent institutional 13F position moves... Optional ?symbol= filter'), and the suggestion to browse the catalog is useful. However, it never explicitly states when to prefer this tool over sibling alternatives like get_institutional_stock or get_congress_trades.

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?

No annotations provided, so description carries full burden. It discloses data source, exclusions (analyst ratings, fundamentals), educational nature, and pricing, but could add more about response format or rate limits.

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 long and includes payment details that may not be necessary for tool selection; it could be split into a pricing note and a focused functionality description. Front-loaded with purpose, but extra details add bulk.

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 data retrieval tool with no output schema, the description sufficiently explains what data is returned, exclusions, and how to access. However, it doesn't specify the structure of the response, which could be a concern, but overall adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so description must compensate, but it only describes 'symbol' implicitly (single ticker). 'limit' is not explained, so the agent may not know it caps the number of records. This is a gap.

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 it returns per-stock institutional composite data (congressional trades and 13F moves) for a single ticker, distinguishing it from siblings like get_institutional_moves (likely broader) and get_congress_trades (congress-only).

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 implies a single-ticker scope and tells users to browse the catalog for pricing, but doesn't explicitly state when to choose this over get_institutional_moves or other siblings. It also explains payment methods, which is useful 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?

No annotations are provided, so the description carries the burden. It discloses the cost ($0.05 per call), payment methods (x402, API key), and educational nature. However, it doesn't describe return format, pagination, or error behavior, which is a gap for a paid API tool.

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 dense but front-loaded with the core purpose and fields. The payment and catalog information is useful but adds length; still, it's structured and each sentence adds value. Slightly long but acceptable for a paid API.

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 has 2 optional params, no output schema, and no annotations, the description covers the main purpose, filter, and payment. However, it lacks details on response structure, pagination, and error handling, which are important for a paid API. It's adequate but not 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 description coverage is 0%, so the description must compensate. It explains the signal_type filter's purpose (per-strategy slicing) but doesn't detail the limit parameter's behavior or valid values. The description adds some meaning beyond the schema but not comprehensive.

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 retrieves Kelly-criterion outcome ledger data with specific fields (signal_type, pnl, r_multiple, kelly_fraction, narrative_score, llm_provider) and mentions an optional filter. It distinguishes from siblings by focusing on Kelly outcomes and live performance history, though 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?

It provides clear context for when to use (to access strategy's live performance history) and mentions the optional signal_type filter for per-strategy slicing. It doesn't explicitly state when not to use or name alternatives, but the context is sufficient for a data retrieval tool.

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?

With no annotations, the description carries the full burden and does so thoroughly. It discloses the $0.05 per call cost, authentication requirements (x402 or subscriber API key), the data source, the output limitation ('bias/score/factors verdict only — raw candles are not redistributed'), and an educational-data disclaimer.

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 is front-loaded in the first sentence, but the rest of the description packs pricing, payment mechanics, header options, and catalog referral into a dense single block. This information is useful, but it could be better structured for readability.

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 and no annotations, the description provides enough context for selection and invocation: what the tool computes, input semantics, chain defaults, cost, authentication, and return scope. It also clarifies that raw candles are not provided, which sets accurate expectations.

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 0%, and the schema only lists token and chain with no explanations. The description significantly enriches both: token is defined as 'mint address or symbol', and chain is described as optional with a default of solana and allowed values enumerated (solana/base/bsc/ethereum).

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 identifies the tool as a 'VWAP/Bollinger/RSI/MACD confluence read on a fresh memecoin launch' using GeckoTerminal OHLCV data. It specifies the exact indicators and asset type, distinguishing it from sibling tools like get_memecoin_movers or get_signals.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives useful context—such as token input, chain options, and a pointer to browse with get_catalog—but does not explicitly state when to prefer this tool over alternatives or when not to use it. Usage is implied rather than directly contrasted with 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?

No annotations are provided, but the description discloses key behavioral aspects: the cost per call ($0.02), the payment challenge mechanism for x402-capable clients, and the alternative API key authentication. It also labels the data as educational and not financial advice, which is transparent about limitations. This exceeds typical transparency for a simple read tool.

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, moderately lengthy string that packs a lot of information: purpose, pricing, payment instructions, and a reference to get_catalog. While it is not overly verbose, it could be more concise by separating the payment details into a distinct section. The structure is logical, moving from function to access to cost.

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 provides sufficient context for a user to understand what the tool does, how to access it, and what to expect. It mentions the data content (market regime, Fed-speech, BIS, prediction-market, geopolitical intelligence) and clarifies it is educational. It does not specify the exact output format, but since there is no output schema, this is not a major gap. The reference to get_catalog for further details adds 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?

The tool has no parameters, and the schema reflects that with an empty properties object. The description does not add parameter-specific semantics because there are none to explain. Given the schema coverage is 100% (no missing parameters), 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?

The description clearly states the tool's function: a one-call read for macro data, including current market regime and latest entries from Fed-speech, BIS, prediction-market, and geopolitical intelligence. It distinguishes itself from sibling tools by combining multiple data sources into a single snapshot.

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 explains when to use this tool (as a macro one-call read) and provides essential usage instructions, including payment methods and authentication options. It also directs users to the free get_catalog tool for browsing all tools and prices, which serves as an alternative for discovery. It does not explicitly compare against other get_* tools, but the purpose is clear enough.

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?

With no annotations, the description bears full responsibility for disclosing behavior. It mentions the payment requirement (via x402 or subscriber key) and includes an educational disclaimer ('not financial advice'), but does not explicitly state whether the tool is read-only, rate-limited, or has other side effects.

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, repeating payment instructions and usage hints. It could be more concise (e.g., removing redundant phrases like 'Cheapest first call' and 'Browse every tool + price first'). However, it is structured in clear sentences and not excessively long.

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 covers the tool's purpose, cost, payment method, data source (40-source bot), and output components (regime, confidence, VIX, ADX). It lacks explicit output format details (e.g., JSON structure) but provides sufficient context for a zero-parameter tool. The suggestion to use get_catalog for browsing adds useful context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so the schema coverage is 100%. The description adds no parameter-specific information, but there are none to explain. This aligns with the baseline of 3 for high schema coverage with no parameter details.

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 provides live crypto & equity market regime classification with specific categories (TRENDING, MEAN_REVERTING, VOL_EXPANSION) and includes confidence, VIX, and ADX context. It distinguishes itself from other get_* sibling tools by focusing on 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 'Cheapest first call on this API' and suggests using the FREE get_catalog tool to browse all tools and prices, providing some guidance on when to use this tool relative to alternatives. However, it does not explicitly state when to use this tool over others (e.g., for a market-regime overview).

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?

With no annotations provided, the description carries the full transparency burden and does disclose important traits: per-call cost, x402/API-key auth requirements, liquidity/volume gating, wash-trade filtering, and an educational-data disclaimer. It omits rate limits, error behavior, and data freshness, but still adds substantial behavioral context.

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 purpose is front-loaded, and the payment/auth details are relevant for actual usage. The description is somewhat dense and long, but every clause contributes useful operational information without 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?

For a one-parameter data tool with no output schema or annotations, this description is largely complete: it covers purpose, chain options, returned metrics, data-quality filtering, cost, authentication, and a catalog pointer. Minor gaps remain around response format or pagination, but the simple tool scope makes this adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema gives chain a default but no description or enum, so the description compensates by explicitly enumerating the allowed values (solana|base|bsc|ethereum) and clarifying single-chain usage. This adds real meaning beyond the schema for the only parameter.

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 identifies the tool as retrieving memecoin movers for specified chains, listing output categories (gainers, losers, volume leaders) and metrics (price, liquidity, 24h stats). It distinguishes itself from the large sibling set by its memecoin-specific and chain-gated focus.

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 practical context such as chain choices, payment/auth methods, and a pointer to get_catalog for browsing tools. However, it does not explicitly state when to prefer this tool over alternatives or when not to use it, so usage guidance remains implied rather than explicit.

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?

With no annotations, the description carries the full burden and does an excellent job: it discloses the $5 per call cost, one-call-for-whole-day behavior, payment authentication methods (x402 or API key), legal/age warnings, that proprietary park/weather/sim/Statcast data is used internally and not redistributed, and that roster/injury/lineup info is verified. This is exemplary transparency.

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 dense but well-structured, starting with the cost and core purpose, then drilling into content, disclaimers, and payment. Every sentence provides meaningful information, though it is longer than necessary and could be slightly more scannable with line breaks or a shorter payment explanation.

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 and no annotations, the description covers nearly all critical context: content, cost, payment, legal, and data sourcing. Missing elements are explicit guidance on the 'date' parameter format/defaults and a description of the response structure. Otherwise it is very complete for a single-sheet daily 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?

The schema has one parameter 'date' (type string, default '') with 0% description coverage. The tool description never mentions the date parameter at all—no format (e.g., YYYY-MM-DD), no default behavior, no example. While the parameter name is self-explanatory, the description adds no value beyond the schema and fails to clarify how to request a specific day.

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 identifies the tool as retrieving the 'FULL daily MLB props sheet' and enumerates specific content (HR & hits+runs+RBIs picks, batter-vs-pitcher matchups, SP strikeout plays, etc.). The use of 'FULL' and 'whole day's sheet' distinguishes it from sibling tools like get_mlb_top_picks and get_sports_props.

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 clearly states when to use the tool (for the full day's entire sheet) and directs users to browse the catalog first, but does not explicitly name alternative sibling tools or state when NOT to use this one. The distinction from a top-picks variant is implied but not explicit.

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?

The description mentions that it is a paid service (with payment details) and includes disclaimers about being for entertainment only, but it does not disclose the underlying behavior such as read-only nature, output structure, or any side effects. Since annotations are absent, the description carries the burden but falls short of revealing the tool's operational behavior.

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 overly long and includes extraneous information such as pricing, payment methods, and disclaimers that could be separated. The core purpose is buried among promotional and legal text, reducing clarity and conciseness.

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?

While the description gives an overview of the content (top picks), it does not specify the response format, schema, or any pagination or error handling. Combined with the lack of parameter details, the tool is incomplete from a developer's perspective.

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 only parameter, 'date', has no description in the schema, and the tool description does not clarify its format, allowed values, or whether it is required. This is a critical omission for a daily picks tool, as users need to know how to specify the date.

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 fetch condensed daily MLB top picks, including specific categories like HR, hits+runs+RBIs, SP strikeouts, matchups, F5 plays, and slate risk. This distinguishes it from other sports data tools by focusing on MLB-specific picks.

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 is provided on when to use this tool versus alternatives. It does not mention the expected date format, whether the date parameter is required, or how to handle errors. The description focuses on content and pricing rather than 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?

With no annotations, the description carries full burden and does well: it discloses per-call cost, data provenance (pure NG synthesis, no raw third-party data), per-idea-type limit capping, educational disclaimer, and authentication methods. It does not describe response structure or error behavior, but the disclosed traits are substantial.

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 dense and front-loaded with the core purpose, then covers pricing, limit behavior, payment/auth, and disclaimer. It is longer than ideal, but each sentence carries operational value for a paid API with auth requirements.

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 annotations and no output schema, the description covers the essential invocation context: cost, payment/auth, limit semantics, data source, and non-advice disclaimer. It lacks explicit return-format details and when-to-use guidance, but is otherwise complete for a one-parameter 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 0%, but the description fully compensates for the single 'limit' parameter by explaining that it applies per idea-type and that limit=25 can return up to ~50 total. This adds meaning far beyond the bare schema definition.

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 identifies the tool as returning NG-derived NARRATIVE_SYNTHESIS and CATALYST_CONVERGENCE intelligence, including macro themes, catalyst-convergence events, and confidence scores. This distinguishes it from sibling tools by naming specific idea types, though it lacks an explicit verb like 'retrieves' or 'lists'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for narrative/catalyst intelligence and instructs users to browse tools with get_catalog first, but it does not explicitly state when to prefer this tool over alternatives or when not to use it. The guidance is mostly operational (pricing, payment) rather than comparative.

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?

With no annotations, the description carries the full burden and does well by disclosing cost, payment challenge flow, authentication methods, educational disclaimer, data fields, and hours lookback default. It omits output shape and pagination but covers the key operational behaviors.

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 cost and core capability, and each sentence earns its place: data scope, subscription comparison, payment instructions, authentication, and catalog pointer. It is longer than strictly minimal, but the monetization and access details justify the length.

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 paid API tool with no output schema and no annotations, the description provides enough to invoke it: data fields, payment mechanism, auth options, and hours lookback. However, it lacks limit semantics, response format details, and error/pagination behavior, leaving meaningful 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?

The description adds meaning for hours by calling it a lookback window with a default of 24, but it says nothing about limit's effect, units, or accepted ranges. With schema_description_coverage at 0%, the description should compensate more for both parameters.

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 identifies the tool as returning recent unusual options-flow signals with specific content: sweep clusters, large-premium prints, direction, strength, confidence, and size_bucket across the active equity universe. This scope distinguishes it from generic sibling signal tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context by framing this as a per-call alternative to options-flow subscriptions and directs users to get_catalog for browsing. However, it does not explicitly state when to prefer this over sibling signal tools such as get_signals, get_apex_signals, or get_anomalies.

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?

With no annotations provided, the description carries the behavioral burden and does substantial work: it discloses the per-call price, x402/USDC payment flow, bearer/key/query auth fallback, limit cap, and educational purpose. It does not discuss response shape or rate limits, but the core operational behaviors are well covered.

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 more verbose than minimal, but the extra sentences about payment, auth, and catalog browsing are actionable for invocation. Core purpose and filters are front-loaded, and pricing appears first.

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 data retrieval tool with no output schema, the description explains return content, available filters, limits, pricing, and authentication well. It lacks explicit sibling differentiation but is otherwise complete enough for an agent to select and invoke it.

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 has no parameter descriptions and 0% coverage, so the description fully compensates by enumerating the category filter values and documenting limit default/cap (100/2000). This gives both parameters meaningful semantics beyond their raw types.

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 identifies the tool as returning the full Polymarket market landscape with category-grouped odds, liquidity, volume, and direction, plus optional category and limit filters. It does not explicitly differentiate from sibling Polymarket tools like get_polymarket_market_stats, so it misses the top sibling-differentiation credit.

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 the use case: use this when you need a broad Polymarket landscape or a category-filtered subset. It does not state when to prefer this over sibling Polymarket intelligence tools or 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?

No annotations are provided, so the description carries the burden. It discloses the cost ($0.05 per call), payment methods, and that it's educational data not financial advice. However, it doesn't describe response format, error behavior, or any rate limits. The payment mechanism is well explained, but other behavioral aspects are missing.

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 dense paragraph that front-loads the core purpose but then includes a long payment explanation. It's not overly long, but the payment details could be more concise. The structure is acceptable but could be improved with clearer separation of purpose and usage.

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 has one parameter and no output schema, the description covers the main purpose and payment, but lacks details on how to get condition_id, what the response looks like, and how this tool fits with siblings. For a paid API tool, more operational context would be helpful, but it's minimally adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description only mentions 'by condition_id' without explaining what a condition_id is or how to obtain it. The single parameter is required but its format, source, or examples are not provided. The description adds minimal value beyond the schema's field name.

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 aggregates wallet intelligence for one Polymarket market by condition_id, listing specific metrics (unique wallets, buy/sell volume split, new-wallet count, top-10 buyers). It distinguishes from siblings by specifying 'one market' and the condition_id parameter, though it doesn't explicitly name alternative tools for broader market views.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for a single market's stats but doesn't explicitly state when to use this vs. sibling tools like get_polymarket_market_landscape or get_polymarket_whales. It does provide payment instructions and mentions browsing all tools with get_catalog, which gives some context, but lacks clear when-to-use/when-not-to-use 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?

With no annotations provided, the description carries the full burden. It discloses pricing ($0.05 per call), authentication requirements (x402 or API key), and a disclaimer that data is educational, not financial advice. It also mentions plan limitations and points to get_catalog for further pricing. This is substantial coverage of access and operational constraints, though it does not mention rate limits, pagination, or error behavior.

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 relatively long, but each part adds value: the purpose, the data content, the pricing, and the payment mechanism. It is front-loaded with the tool's core function and structured clearly with a 'HOW TO PAY' section. There is no filler, though it could be slightly more concise without losing critical info.

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 straightforward data retrieval tool with three optional parameters and no output schema, the description covers the key aspects: what data is returned, how to filter, and how to access it. It also directs users to get_catalog for broader context. The only gap is a lack of detail on 'limit' behavior and default values, which are partially in the 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 0%, so the description must compensate. It explicitly explains that condition_id and side are filters, but it does not specify allowed values for side (e.g., YES/NO) or the format of condition_id. The 'limit' parameter is not explained beyond its default in the schema. Overall, it adds some meaning but not complete semantics.

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 specific resource (first-time Polymarket bettors/fresh wallets) and action (getting fills with details like fill size, side, outcome price, and rolling totals). It also distinguishes from siblings like 'get_polymarket_whales' by focusing on new wallet insider patterns, making its purpose unambiguous.

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 a specific use case—detecting suspiciously confident new wallets in Polymarket markets—and mentions filtering by condition_id and side. However, it does not explicitly contrast with alternative sibling tools or state when not to use it, leaving usage context to the agent's inference.

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?

With no annotations, the description carries the transparency burden and does well: it discloses per-call cost, payment flow, authentication methods, the data source, insider/algo detection, and the educational/non-financial-advice caveat. It does not describe rate limits or exact return structure, but the core behavioral and access traits are covered.

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?

Purpose is front-loaded and the payment/auth instructions are actionable, but the description is heavier than necessary due to marketing language ('elsewhere this data class sits behind $599/mo API plans') and repeated auth alternatives. It earns its place mostly but could be tightened.

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 three optional parameters, no output schema, and no annotations, the description provides enough context for an agent to understand what data is returned, how to pay, and how to authenticate. The main gaps are the unexplained min_bets parameter and the absence of explicit sort_by value constraints.

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 0%, so the description must compensate. It partially does by naming likely sort_by values (informed score, win rate, realized PnL, volume) and implying limit through 'leaderboard'. However, min_bets is not explained at all, and no explicit accepted values are given for sort_by.

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 identifies the tool as a Polymarket smart-money leaderboard and specifies the ranking dimensions (informed score, win rate, realized PnL, volume) plus extra data like specialization and average entry odds. It does not explicitly differentiate itself from the similarly named sibling get_polymarket_whales, so it falls just short of a 5.

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 intended use is implied by the description (retrieve a whale leaderboard with ranking metrics), and it provides practical caller guidance about x402 payment and subscriber API keys. However, it does not state when to choose this tool over alternatives like get_polymarket_whales or get_polymarket_new_wallets.

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?

No annotations are present, so the description carries the full burden. It discloses pricing, data source (on-chain tracker), and that it is educational data, not financial advice. It does not explicitly state read-only behavior or potential side effects, but the 'get' verb and raw data context make it inferable.

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 long but each sentence adds value: data scope, source, pricing, payment methods, and a pointer to get_catalog. It is front-loaded with the core purpose and then details, though it could trim the payment explanation. Still, it is dense and 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?

The description covers what data is returned (address, volumes, counts, markets, cabal cluster ideas), the payment/auth mechanism, and the optional limit. With a single parameter and no output schema, this is sufficient for an agent to invoke and interpret results. It lacks explicit error handling notes but that is not critical.

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 0% schema description coverage, the description compensates by explaining the limit parameter: optional, default 25, cap 100. This adds critical meaning beyond the schema (which only shows default), making the tool callable correctly.

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 it retrieves Polymarket flagged-wallet groupings and coordinated-betting cabal intel, which is specific and distinguishes it from similar sibling tools like get_polymarket_new_wallets or get_polymarket_whale_leaders. It doesn't explicitly name alternatives, but the purpose is unambiguous.

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 payment and invocation details, but it does not explain when to use this tool versus the many sibling tools (e.g., when to prefer whale leaders vs groupings). There is no context on use cases or exclusions, leaving agents to infer applicability from the data description.

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?

With no annotations, the description carries the burden. It mentions 'Educational data, not financial advice' but does not disclose side effects, read-only nature, or any other behavioral traits. Payment method is explained, but that's not a behavioral trait.

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 lengthy due to cost, payment instructions, and a reference to get_catalog, but this information is necessary for usage. It is structured in clear sentences and not overly verbose given the need to convey payment details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the data type, source, optional filters, output limit, and payment. It does not specify the output format or error handling, but for a straightforward data retrieval tool, this 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?

The schema has two optional parameters: limit and symbol. The description explains 'Optional symbol filter' for symbol and 'up to 25 ideas per call' which implies limit's purpose. This adds meaningful context beyond the bare 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's function: retrieving the most recent cross-market intelligence ideas (institutional, options flow, on-chain, news catalysts) from a 40-source SharedBrain. It distinguishes itself from siblings by emphasizing 'cross-market' and the specific source count.

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 versus alternatives. It only suggests browsing the free get_catalog tool first, which is a general recommendation, not a specific usage condition. It does not say when to prefer this over other intel 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?

The description transparently states that the data is educational and not financial advice, and includes payment settlement details. It does not mention any side effects or data freshness, but as a read-only feed, this is sufficient.

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 verbose, including extensive payment instructions that may be redundant across similar tools. While it packs necessary information, it could be more succinct without losing clarity.

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 covers the data fields returned (BTC amount, years dormant, timestamps) and includes payment context, making it complete enough for an agent to understand the tool's output and usage without additional schema details.

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 only parameter, limit, is clearly explained with its default (25) and cap (50). The description fully covers the parameter's meaning and constraints, exceeding the schema's minimal 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?

The description clearly states the tool's function: a feed of dormant/Satoshi-era BTC wallet movements, specifying the data fields (BTC amount, years dormant, timestamps). It is distinct from other tools by focusing on Bitcoin wallet activity.

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 usage details such as the optional limit parameter with default and cap, and mentions the educational nature. While it does not explicitly contrast with sibling tools, the specificity of the data makes the use case 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?

The description discloses pricing ($0.01 per call), states it is 'educational data, not financial advice', and explains payment methods. It also mentions the lookback window. However, it does not explicitly state side effects (e.g., read-only nature) or any rate limits, and it lacks detail on output format or error behavior. Since no annotations are provided, the description carries the burden but only partially fulfills it.

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 purpose, then provides relevant usage details (lookback, payment, disclaimer). It is somewhat lengthy due to payment instructions, but each sentence adds necessary context. The structure is logical, starting with what it does and then how to pay, without excessive tangents.

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 provides partial information about return values (mentions direction, strength, confidence, source agent) but lacks clarity on response format, error handling, or edge cases. It is sufficient for a high-level understanding but not fully complete for an agent to anticipate all behaviors.

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 description explicitly explains the 'hours' parameter ('Lookback window via ?hours= (default 24)') and implies the symbol parameter by saying 'for one symbol'. Schema coverage is 0%, so the description must compensate, and it does provide some clarity but does not detail accepted formats or edge cases (e.g., valid symbols, range limits).

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 it provides 'recent trading signals for one symbol' and lists specific types (options flow, dark pool, technical, sentiment, congressional), making the tool's purpose explicit. However, it does not differentiate from sibling tools like get_apex_signals or get_composite_signal, which might have overlapping functionality.

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 mentions the lookback window parameter and points users to browse other tools via get_catalog, but it does not provide explicit guidance on when to use this tool versus alternatives. There are no exclusions or comparative selection criteria beyond the general description.

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?

With no annotations, the description carries the full disclosure burden. It discloses the per-call price, x402 payment settlement vs API-key fallback, data sources, the 'Model-owned fields only' constraint, and the educational disclaimer. It does not describe response shape or rate limits, but the operational transparency is well above average.

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 dense but front-loaded with the core purpose and output contract before moving into pricing and payment details. The HOW TO PAY section is long, but each detail is operationally necessary for correctly invoking an x402-priced API.

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 2-parameter read tool with no output schema or annotations, this description is nearly complete: it covers output fields, target sports, optional filters, pricing, authentication, and usage caveats. The main gaps are limit semantics and a more explicit statement of response structure.

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 0%, so the description must compensate. It adds meaning to the sport parameter by listing allowed values (mlb/nba/nfl) and marking it optional. However, the limit parameter is never explained, leaving only its default value 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 names a specific resource ('multi-sport prop-bet edge scan') and the exact output fields (edge_pct, confidence, reasoning, recommended_action) with an explicit enum of action values. It also scopes the tool to MLB, NBA, and NFL props, making it clearly distinguishable from siblings like get_sports_props and get_mlb_top_picks.

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 when to use the tool: when you want multi-sport prop edge scans with model-derived recommendations. It documents the optional sport filter and payment methods, but it never explicitly contrasts this tool with sibling alternatives or states conditions for choosing it over related 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?

With no annotations, the description carries the full burden and does disclose cost per call, authentication options, excluded raw fields, and the educational nature of the data. It does not explicitly state whether the tool is read-only or whether repeated calls incur multiple charges, but the payment and auth details are 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 longer than necessary and mixes payment instructions, data source explanations, and disclaimers into one block. It contains useful information, but the repeated payment flow and catalog promotion could be trimmed for clarity.

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 lists returned fields, sorting order, limit behavior, and payment requirements, which covers a lot without an output schema. However, it does not describe the actual response structure, possible absence of data, or error conditions such as payment failure.

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 only parameter, limit, is described in the schema with a default, and the description adds the cap of 50 and clarifies it is optional. This is sufficient for the single parameter, though no other parameters exist.

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 that the tool returns player prop edges with specific fields like edge_pct, fair_prob, and direction, which makes the primary purpose identifiable. However, the odd phrase 'News Gurus Intel' and the lack of an explicit 'gets sports props' action slightly obscure clarity.

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 explains how to pay and that results are sorted by edge_pct, but it gives no guidance on when to prefer this tool over the many sibling tools. There are no usage alternatives or contextual cues such as 'use this for X, not for Y.'

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?

With no annotations provided, the description carries the behavioral transparency burden. It adds valuable context: the $0.02 per-call cost, x402 payment/settlement requirement, optional API-key auth paths, and the 'educational data, not financial advice' caveat. It does not explicitly state that this is a read-only/no-side-effect operation or describe rate limits, but the payment/auth behavior is 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 front-loaded with the core purpose, then pricing, caveats, payment/auth details, and a pointer to the free catalog. Every sentence adds useful information, though the payment section is somewhat long. Overall it is appropriately sized for an API with nontrivial access requirements.

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 one-parameter tool with no output schema and no annotations, the description covers pricing, authentication methods, data sources, and the educational nature of the output. It does not specify the exact time window of 'recent' sentiment or output shape, but these are partially captured by the described content. The description is sufficiently complete for the tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% and there is only one required parameter, 'symbol.' The description adds the meaning that a symbol is a 'ticker' and that the call is for a single symbol, but it does not provide format guidance, examples, or constraints beyond that. Given the low coverage, more parameter semantics were needed.

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 identifies a per-symbol social sentiment tool: 'recent sentiment/retail signals (bullish vs bearish counts + net bias) merged with social-intelligence ideas' for 'one ticker.' This verb-like purpose distinguishes it from sibling tools like get_composite_signal or get_symbol_tearsheet.

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?

Usage context is implied through 'per-symbol' and 'for one ticker,' indicating this is for single-symbol sentiment queries. However, there is no explicit guidance on when to choose this over alternatives, no exclusions, and no named comparable 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?

With no annotations, the description carries the full burden. It discloses the cost ($0.5), payment methods (x402 or API key), data composition (pure derived reads, no raw vendor data), educational disclaimer, and authentication options. While it does not explicitly state that it's a read-only operation, the nature of a tearsheet implies it, and the payment details add useful context beyond the schema.

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 moderately long but front-loaded with the core purpose, followed by data source, disclaimer, payment methods, and a catalog pointer. Each sentence adds value: cost, contents, composition, disclaimer, payment, and catalog recommendation. There is no redundant filler, though the payment details make it slightly verbose.

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 one parameter, no output schema, and no annotations, the description provides enough context for a user to understand its purpose, inputs, cost, and authentication. It lists the data components (market regime, composite signal, social sentiment, institutional moves), giving a clear idea of what the response includes. It could be more explicit about the response format, but the listed components suffice for retrieval.

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 has only one parameter 'symbol' with no additional description (0% coverage). The tool description adds meaning by clarifying that the symbol is a ticker: 'for one ticker', 'per-symbol', and 'one-call per-symbol tearsheet'. This compensates for the schema's lack of detail, though it doesn't specify format constraints (e.g., uppercase, exchange suffixes).

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: 'one-call per-symbol tearsheet' that combines market regime, composite signal conviction, social sentiment, and 13F moves for a single ticker. This specific verb+resource structure distinguishes it from sibling tools like get_symbol_sentiment or get_market_regime, which target individual data points.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for a consolidated view ('one-call') but does not explicitly state when to use this tool versus individual tools like get_symbol_sentiment or get_market_regime. It mentions browsing the catalog but gives no clear guidance on selection criteria or when not to use it. The guidance is implicit rather than explicit.

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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the data is educational, not financial advice, and explains payment/auth methods, but it does not disclose output shape/pagination, whether results are historical or real-time, or any rate limits. The cost note is transparent but leaves gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is front-loaded with the core purpose and unique value, then lists filters and payment instructions. The final sentence about browsing tools is extra but useful. It is concise for the information it conveys; could be slightly tighter by moving the payment how-to into an annotation-like section, but acceptable.

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 three optional parameters, no output schema, and no annotations, the description explains the data contents, filters, and access method (including cost and auth). It could be complete with a note on response format and guidance on which stage values are valid, but it covers the essentials for a data-access 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?

Three optional parameters (limit, stage, symbol) have zero schema coverage, so the description must compensate. It mentions optional ?symbol= and ?stage= filters, adding meaning beyond the bare schema (e.g., symbol filters by ticker, stage filters by decision stage). The 'limit' parameter is not described in the text, but the description covers the two most semantically important ones; further detail would improve to a 5.

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 'full LLM trade-reasoning chain: every routing decision with thesis, signal attribution, kelly_fraction, gate result and exit detail.' It identifies the unique dataset (the AI's own trade logic) and optional filters, which distinguishes it from sibling tools that likely return other data snapshots.

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 implicitly advises use for trade-decision reasoning and includes optional filters. It does not explicitly mention when not to use it or alternative tools, but the guidance to 'Browse every tool + price first with the FREE get_catalog tool' implies exploration. Could be improved by naming a sibling for when users need executed trades or outcomes.

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?

There are no annotations, so the description carries the transparency burden. It discloses pricing, payment methods (x402 or API key), proprietary computation, educational disclaimer, and connection to a live table. This is substantial behavioral context beyond the schema, though it doesn't state potential side effects (unlikely for a read-only listing tool).

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 dense paragraph but front-loaded with purpose and key fields. Payment details and disclaimers are essential but could be shorter. Still, all content serves a purpose for an API tool with authentication nuances.

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). The description covers purpose, fields, ordering, filters, limits, payment, and disclaimers. Missing explicit return format (envelope vs bare array) but output schema is absent, so the description could have clarified that, a minor gap.

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 0%, so description must compensate. It does: explains limit default and cap (50) and asset_class filter purpose. It adds an ordering note (composite_score DESC) and mention of optional query parameters, helping the agent construct calls. Slight gap: no explicit value format for asset_class.

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 explicitly states it returns per-symbol ATR%/volume squeeze scoring with fields (symbol, atr_pct, volume_usd, galaxy_score, composite_score, asset_class, rank) and ordering (composite_score DESC). It names the underlying table and clearly distinguishes itself from siblings by product and fields.

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 mentions optional filters (asset_class, limit) and default/cap, but does not explicitly say when to use this tool over others. However, given sibling names like get_anomalies, get_signals, get_market_regime, the description's focus on volatility squeeze makes use-case inferable, though direct alternatives are not named.

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?

The description discloses that each call costs $0.01 and requires payment via x402 or subscriber API key. It also states the behavior for unscanned addresses (returns found:false) and includes a disclaimer that the data is educational, not financial advice. While it does not explicitly state whether the tool modifies anything, the nature of a verification tool and the absence of annotations imply it is read-only.

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 lengthy but begins with the core purpose and then details output fields and payment instructions. It is structured with periods and lists, but includes redundant pricing and payment details that could be condensed. However, the main functionality is front-loaded and the additional information is relevant.

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 the output fields (score, confidence, action, flags, narrative tags) and the 'found:false' case. It also covers payment methods and a disclaimer. It does not mention error handling for invalid mints or rate limits, but for a simple verification tool with one input, it is fairly 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?

The only parameter, 'mint', is described as a Solana mint address in the tool description, clarifying its meaning despite lacking a schema description. The description provides sufficient context to understand what value to pass.

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 that this tool provides a token authenticity verdict for a Solana mint, including a realness score, confidence, recommended action, rug-risk flags, psyop/pump detection, and narrative tags. It distinguishes itself from the sibling 'get_' tools by focusing on verification rather than data retrieval.

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 that this tool should be used when you need to verify the authenticity of a Solana token, as it returns a verdict and associated flags. It also notes that if the address has never been scanned, it returns found:false, which suggests it might not initiate new scans. However, it does not explicitly compare with alternative tools or state when not to use it, but given the distinct purpose, it is clear enough.

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