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 carries the full burden and handles it well. It discloses the $0.05 per-call cost, payment/settlement mechanisms, subscriber API key alternatives, proprietary computation, educational-data disclaimer, and newest-first ordering. This goes well beyond the schema and gives an agent important behavioral and commercial 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 description is dense but purposeful; cost, result fields, example, filtering, ordering, and payment instructions all earn their place. It is somewhat sprawling as a single block, and the 'HOW TO PAY' section could be tightened, but nothing is pure filler.

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

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

For a paid, authenticated read tool with no output schema, the description covers almost everything needed to call it: result fields, example, filters, limit, cost, and auth. It does not describe the exact JSON envelope or error behavior, but those are minor gaps given the example and field list.

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 that symbol acts as a filter, that limit has a maximum of 50, and that results are newest first. It also provides a concrete symbol example. It does not state the default limit of 25, but the schema already carries that default, so the added semantic value is solid.

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 indicates this tool returns APEX v10.2 confluence signals, listing the key output fields (symbol, long/short, score, entry, timeframe, momentum_surge) and giving a concrete BTC-USD example. It is distinguishable by its APEX-specific naming, but it does not explicitly contrast itself with sibling tools like get_signals or get_composite_signal, so it stops short of full sibling differentiation.

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 practical invocation guidance: ?symbol= filter, ?limit= up to 50, newest-first ordering, and mentions the free get_catalog tool for exploring alternatives. It does not explicitly state when to prefer this tool over get_signals or get_launch_ta_confluence, but the operational context is clear enough for an agent to decide.

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 the full burden and does so well: it discloses the $0.05 per-call cost, x402 payment settlement vs subscriber API key alternatives, an example output shape, and the educational caveat. It lacks failure-mode details but covers the main behavioral traits.

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 organized: pricing/function first, then example and positioning, then explicit HOW TO PAY, then pointer to get_catalog. Some branding and marketing ('News Gurus', 'only x402 API') is arguably nonessential, but the core content is front-loaded and scannable.

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?

Despite having no output schema, the example enumerates the key response fields (counts, confidence, signal types), and the payment/auth instructions are fully specified. For a single-parameter tool this is a complete calling contract; the get_catalog pointer covers broader discovery.

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 explain the lone 'symbol' parameter. It does so by calling it a TICKER, using NVDA as an example, and scoping it to US equities, which adds real meaning beyond the schema's bare 'Symbol'. It stops short of specifying case/exchange 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?

States a precise resource: per-symbol composite conviction built from 50 latest signals across 42 generators, with a concrete NVDA example showing bullish/bearish counts, avg confidence, and signal types. Positions itself as the flagship evidence read and distinguishes from raw-quote APIs, though it doesn't name a sibling.

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?

Gives an explicit use case — 'what does the evidence say about TICKER right now' — and notes it's the flagship read, which tells an agent when it applies. It does not spell out when to prefer sibling sentiment/ranking tools or provide exclusions.

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

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

With no annotations, the description carries the full burden and covers cost ($0.05 per call), payment mechanisms (x402 or subscriber API key), data provenance (public disclosure, no vendor licensing), and a disclaimer (educational, not financial advice). It does not disclose rate limits or response format, but the essential behavioral traits are present.

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 front-loads the core purpose and cost before diving into payment details and examples. Every sentence adds distinct information, but the overall length is substantial. It is efficient yet dense.

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 2-parameter, no-output-schema tool with no annotations, the description covers the data fields returned, gives a full example, explains the optional filter, and details payment/auth. Missing limit semantics and response structure are small gaps; otherwise the tool is well specified.

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 symbol parameter ('Optional ?symbol= filter') with a concrete example, but says nothing about the limit parameter's behavior beyond the schema's default. Partial compensation for a low-coverage schema.

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

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

Description states the exact resource: 'latest US congressional stock trades (STOCK Act): member, party, buy/sell, ticker, amount, dates.' The concrete example (R-TX member, Purchase NVDA, $15,001-$50,000) makes the tool's function unmistakable. It distinguishes itself from sibling market-data tools by focusing on congressional trades.

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 ('Per-call alternative to $25-75/mo congressional-data subscriptions') and points users to get_catalog to browse all tools. However, it does not explicitly state when to choose this over sibling tools or when not to use it. The usage guidance is 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 at all, the description carries the full behavioral burden. It discloses the per-call cost, that only derived signals are returned rather than raw notional/price data, and the payment/authorization mechanisms. It does not cover failure modes or response details, but the most consequential behaviors—cost, auth, and data scope—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 core semantics are front-loaded in the first sentence, with the example and bucket explanation following in a logical order. The payment/auth block is lengthy but necessary because there are no annotations providing this critical invocation information. The description is somewhat dense but each section earns its place.

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

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

For a tool with two optional parameters and no output schema, the description provides the output field names, an example, and billing/payment context, which is reasonably complete. The main gaps are that the limit parameter is undocumented and the value ranges for direction, strength, and confidence are not specified, leaving some ambiguity in interpreting the response.

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

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

The schema has 0% description coverage, so the description must compensate. It does explain the hours parameter by noting 'Lookback ?hours= (default 48)', which adds real meaning. However, the limit parameter is never mentioned, leaving its semantics entirely to the schema, so coverage is incomplete.

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 'recent dark-pool prints as derived signals' and lists the specific fields: symbol, direction, strength, confidence, size_bucket, with a concrete example. It is specific about verb and resource, but it does not explicitly distinguish itself from sibling tools such as get_institutional_moves.

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 when one needs dark-pool-derived signals and explains that the buckets 'flag institutional-scale accumulation or distribution,' providing useful context. However, it never explicitly states when to prefer this tool over alternatives or what situations make it inappropriate.

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?

With no annotations provided, the description carries the full burden, and it does so thoroughly: it discloses the $0.1 per-call cost, the x402 payment flow, subscriber API key alternatives, excluded fields due to vendor licensing, the fact that public filings are merged by the bot, and an educational-data disclaimer. It also reveals that output includes example trade details and fund movement descriptions, which is valuable beyond any structured metadata.

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

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

The description is long but each sentence earns its place: pricing, data scope, example, exclusions, payment/auth instructions, and catalog pointer. It is front-loaded with the most decision-relevant information and the 'HOW TO PAY' section is clearly organized. Slightly dense, but not padded.

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

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

The description covers purpose, pricing, payment, auth, data sources, and exclusions, which is strong for tool selection. However, there is no output schema and the description leaves out the meaning of `limit`, return structure, and potential error/empty-result behavior. An agent could invoke it with just the required symbol, but would be guessing about parameter semantics and response shape.

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, but it only indirectly clarifies the `symbol` parameter through 'TICKER' and the NVDA example. The `limit` parameter is not mentioned at all, leaving its meaning, behavior, and default undocumented. This is only partial compensation for a fully undocumented 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 one ticker's institutional picture by merging congressional trades and 13F fund moves. It is specific about the resource and scope, and its 'Answers who is buying TICKER' framing makes the purpose actionable. However, it does not explicitly differentiate itself from the similar sibling get_institutional_moves, so it falls 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 description implies the tool should be used when a user needs institutional buying activity for a single ticker, and it provides an illustrative NVDA example. It does not state when to prefer this over siblings like get_congress_trades or get_institutional_moves, nor does it give exclusion criteria. The usage context is clear but largely implicit.

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?

No annotations are provided, so the description carries the full burden. It discloses the per-call price, authentication and payment methods, the underlying data source (40-source trading bot), intraday refresh cadence, educational disclaimer, and shows an example response with all key fields. This is comprehensive behavioral disclosure.

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 front-loads the core output definition, then adds pricing, use case, payment mechanics, and a pointer to get_catalog. Each section adds operational value, though the HOW TO PAY block could be somewhat condensed without losing meaning.

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?

Even without an output schema, the description provides a concrete example response, covers cost and auth, states the data source and cadence, and gives a usage context. For a zero-parameter read endpoint, nothing essential for invoking it correctly is missing.

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

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

The tool has zero parameters and the schema coverage is 100%, so there is no parameter meaning for the description to add. The baseline for zero-parameter tools is appropriate here; the example output incidentally confirms that the only required input is authentication/payment.

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 resource: current US equity + crypto market regime, categorized as TRENDING, MEAN_REVERTING, or VOL_EXPANSION with confidence. It does not explicitly differentiate from sibling tools, but the precise output definition is specific enough to distinguish it from other 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 gives concrete usage context: the tool is described as the 'standard trend day or chop day check before sizing any trade' and as the 'cheapest first call on this API.' It does not mention exclusions or alternative tools, but the conditions for use are reasonably 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?

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 and does well: it discloses the per-call price, payment/authentication methods, that it is educational data and not financial advice, and provides a concrete output example. It does not cover error behavior or rate limits, but the key operational behavior is 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 purpose is front-loaded and the tool composition is described in the first sentence. The payment and disclaimer details add length but are genuinely necessary for a paid external API. It is slightly dense, with multiple clauses stacked in the first sentence, but no content is wasted.

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 single-parameter tool with no output schema and no annotations, the description is remarkably complete: it explains what the tool returns, gives a representative output example, specifies pricing and authentication, and sets expectations about educational use. Nothing essential for an agent to decide whether to call it is missing.

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

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

The schema provides only a required 'symbol' with no description and schema coverage is 0%. The description compensates by calling it a 'ticker' and giving the example symbol 'NVDA', which clarifies the expected input. It could be stronger on formatting or accepted symbol styles, but for a single obvious parameter this is adequate.

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 what the tool does: it is a 'one-call tearsheet for a ticker' that composes regime, signal conviction, social sentiment, and 13F moves. It distinguishes itself from sibling tools by emphasizing it is a 'pure composition of this API's derived reads (no raw vendor data)', so an agent can tell it apart from lower-level sentiment or regime 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 says this is 'the single call an agent needs before a trade decision', giving clear intended context, and it directs users to browse the FREE get_catalog tool. However, it does not explicitly say when not to use this tool versus specific alternatives like get_symbol_sentiment or get_market_regime.

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?

With no annotations provided, the description carries the burden and does well: it discloses the OWNER-only permission requirement, authentication methods, a notable non-effect (never triggers trades), and format hints for symbols/sources. It doesn't describe other side effects, but the key behaviors 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?

Four sentences, each carrying distinct information: purpose, kind values, format rules, and behavioral/authentication context. No filler, though it could have been slightly more organized with explicit parameter bullet points. Still concise and readable.

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

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

For an 8-parameter tool with required fields, the description gives authentication, permission, and data-usage context, but leaves several parameters (e.g., mission, confidence, event_date) undefined, relying on schema naming. Output schema exists, so return values are likely self-explanatory, but overall completeness is moderate.

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 provides enums for 'kind' and comma-separation rules for 'symbols' and 'sources', which helps, but it leaves title, body, mission, confidence, and event_date without any semantic hints beyond their names. Partial compensation only.

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

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

The description states a clear action ('submit') and a specific resource ('structured research finding into News Guru's SharedBrain'), and it enumerates the valid kinds. It is easily distinguished from sibling tools, which are all read-only 'get' operations.

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 conveys when to use the tool: when submitting a research finding to SharedBrain, and it warns that findings do not trigger trades. It does not explicitly state when not to use it or name an alternative, but given the tool's uniqueness among siblings, the context is sufficient.

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.