newsgurus-intel

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

No annotations are provided, so the description must carry the full burden. While it doesn't explicitly say 'read-only,' the term 'liveness probe' strongly implies no side effects. It also discloses the data scope (24h, 40+ agents, counts and timestamps), which adds behavioral clarity.

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

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

The description is three sentences with no wasted words. The first sentence defines the tool, the second provides usage context, and the third adds a disclaimer. It is front-loaded and efficient.

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

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

For a tool with no parameters and no output schema, the description is fully complete. It explains the purpose, data returned (counts, timestamps, 40+ agents, 24h window), and usage context. No additional information is needed.

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 (schema coverage 100% with empty properties). The description adds meaning by explaining what the tool returns (counts and timestamps per agent), which goes beyond the schema's absence.

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 24 hours. It uniquely distinguishes this from sibling tools (which provide specific data, e.g., get_btc_snapshot, get_options_flow) by emphasizing system status.

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

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

The description explicitly says 'Verify the engine is live before buying data,' giving a clear usage context. It does not, however, mention when not to use or suggest alternative tools, but the sibling list is diverse enough that specificity is less critical.

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

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

With no annotations, the description carries the full burden. It does not disclose behavioral traits such as read-only nature, rate limits, or authentication needs. The disclaimer 'Educational data, not financial advice' adds no 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 description is front-loaded with the core purpose but includes flowery language and a disclaimer. It is acceptable but not maximally 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?

Given no output schema and no parameter documentation, the description should cover output structure and parameter usage. It does not, leaving significant gaps for an 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?

The only parameter (limit) is not mentioned in the description. Schema coverage is 0%, so the description adds no meaning beyond the schema's 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 clearly states it returns recent anomaly detections and lists specific types (pump/psyop spikes, rug precursors, etc.), effectively distinguishing it from sibling tools that focus on other 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?

No guidance is provided on when to use this tool versus alternatives like get_signals or get_recent_intel. The description does not mention context or prerequisites.

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

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

No annotations provided, so description carries full burden. Discloses ordering (created_at DESC), limit cap (50), and optional LIKE filter. However, omits idempotency, authentication needs, data freshness, or error behavior. Adequate but not comprehensive.

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

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

Three sentences: first defines tool and output fields, second covers parameters and ordering, third adds disclaimer. Front-loaded, no fluff, every sentence adds value.

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

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

For a simple tool with 2 optional params and no output schema, description covers essential aspects: what returns, how to filter, ordering. Minor gaps like response format details, but sufficient for agent usage.

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. Clearly explains that symbol is a LIKE filter and limit has default 25 and cap 50. Adds meaning beyond schema fields, though details on LIKE pattern format are missing.

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?

Describes tool as a multi-source confluence signals feed from live table, listing output fields (symbol, direction, score, etc.). Distinct from siblings like get_signals by specifying 'APEX v10.2' and source table, but could explicitly differentiate.

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 vs alternatives. Does not mention prerequisites or when to avoid. Only implicitly suggested for APEX signals queries.

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 must cover behavioral traits. It discloses that data is educational and not financial advice, but lacks details on caching, update frequency, or rate limits. For a read-only zero-parameter tool, this is adequate but not thorough.

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 pack all essential information: purpose and disclaimer. No wasted words; front-loaded with what the tool delivers. Highly efficient.

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, the description reasonably outlines return content. However, it could specify exact fields or format to fully compensate for missing schema. Given the tool's simplicity, it is minimally 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 schema coverage is 100%, so the baseline is 4. The description adds no parameter semantics since none are 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 states it is a one-call read for BTC, specifying the included data: dormant wallet moves, recent moves with values, APEX signals, and market regime. This differentiates it from siblings that focus on individual components like get_satoshi_moves or get_market_regime.

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

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

No explicit guidance on when to use this tool versus siblings. While 'one-call read' implies a comprehensive snapshot, the description does not state trade-offs or alternatives, such as using individual tools for deeper detail.

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 provided, the description carries full burden. It correctly indicates a read operation (no destructive effects) and describes the output structure, but does not explicitly state read-only nature, auth requirements, rate limits, or error behavior. The note 'Educational data, not financial advice' is a disclaimer, not a 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 concise and front-loaded, using a single informative sentence plus a brief note. Every word adds value, though the structure could be improved with a clearer separation of purpose and output details.

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

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

For a simple tool with one parameter and no output schema, the description adequately covers the output structure (bullish/bearish counts, average confidence) and scope (latest 50 signals across all generators). Missing details like error handling or more granular output fields do not significantly impair usability for this straightforward 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?

Description adds minimal meaning beyond the schema: it says 'one symbol' and 'per-symbol', but with 0% schema description coverage, it fails to elaborate on symbol format, restrictions, or examples. The parameter 'symbol' is left wholly undefined beyond its 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?

Description clearly identifies the tool as a composite signal reader for one symbol, merging latest 50 signals into bullish/bearish counts and average confidence. It positions itself as the flagship per-symbol conviction snapshot, which provides a unique identity among siblings, though it doesn't explicitly differentiate from similar tools like get_signals or get_symbol_sentiment.

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 this tool is the go-to for a quick overall conviction snapshot ('flagship per-symbol conviction snapshot'), but it offers no explicit guidance on when to choose this over alternatives like get_signals or get_symbol_sentiment, nor does it mention exclusions or prerequisites.

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 full responsibility for behavioral disclosure. It lacks details on data freshness, rate limits, authentication requirements, or whether results are paginated. The disclaimer 'Educational data, not financial advice' is 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 concise (3 sentences) and front-loaded with the core purpose. Every sentence adds value, though the mention of pricing alternatives could be considered tangential.

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 2 optional parameters, the description lists the returned fields reasonably. However, it omits ordering, pagination, and the role of the 'limit' parameter, leaving some gaps for a complete understanding.

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%, meaning the description must compensate. It adds meaning for 'symbol' as a filter, but does not explain 'limit' (default 25) or its effect. With 2 parameters, only 50% are described, and the explanation is minimal.

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 US congressional stock transactions' and lists the fields (member, party, state, buy/sell, ticker, amount range, dates). This is a specific verb+resource that distinguishes it from sibling tools (e.g., get_anomalies, get_btc_snapshot) which focus on other financial data types.

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 an optional '?symbol=' filter, which gives a hint for narrowing results, but does not explicitly state when to use this tool over others or provide exclusion criteria. No alternatives are 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?

Discloses that raw notional/price figures are not redistributed and that it's educational data. Lacks details on authentication, rate limits, or other behavioral aspects; no annotations provided.

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 concise sentences with no redundant information. Front-loaded with the most important 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?

Adequate for a tool with two simple parameters, but lacks explanation of return format, error handling, or how it differs from similar sibling tools like get_institutional_moves.

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?

Only describes the 'hours' parameter ('Lookback via ?hours= (default 48)'), but the 'limit' parameter is not mentioned. With 0% schema coverage, description partially compensates but leaves one parameter undocumented.

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 specifies it provides institutional dark-pool prints with key fields (symbol, direction, strength, confidence, size_bucket). Distinguishes from siblings by focusing on dark pool 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?

Mentions lookback via hours parameter but does not explicitly state when to use this tool over alternatives or provide exclusion criteria.

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

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

No annotations provided, and the description does not disclose behavioral traits beyond 'Educational data, not financial advice'. It does not state whether the operation is read-only, has rate limits, or requires authentication. For a data-fetching tool with no annotations, this is minimal 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?

Two sentences, no wasted words, but could be structured more clearly (e.g., list outputs). The front-loading is good: title-like header followed by content.

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

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

Given no output schema, the description explains what the tool returns, but lacks mention of response format (e.g., JSON array), size, or pagination. It is adequate for a simple fetch but leaves uncertainty about the structure of the returned data.

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 zero parameters and 100% schema coverage, the description adds value by explaining the output semantics: percentile-scored rankings and LEAPS candidates. It clarifies the scope (US equities + crypto protocols) and criteria, which is necessary since the schema provides no details.

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

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

Clear verb 'returns' plus specific content: quality-compounder rankings and growth-stock LEAPS candidates, with detailed criteria (high-margin, low-debt, etc.). Distinguishes from siblings by its focus on rankings and specific asset types.

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 like get_composite_signal or get_institutional_moves. The description implies use for quality-compounder rankings, but does not state when not to use it or name alternative 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?

No annotations are provided, so the description carries the full burden. It states it's a 'read' and 'educational data, not financial advice,' but lacks detail on rate limits, caching, or potential delays. This is minimally adequate.

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 efficiently convey purpose and output. The brand prefix 'News Gurus Intel API' is minor clutter but doesn't harm. Information is front-loaded; no redundant sentences.

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?

No output schema exists; the description partially describes return fields (direction, score, etc.) but omits structure, format, or typical ranges. Given the tool's simplicity and single parameter, it's adequate but not comprehensive.

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 adds meaning by listing example symbols (BTC, ETH, SOL, XRP) and implying it's a cryptocurrency ticker. It compensates for the schema gap but does not specify format (e.g., case sensitivity).

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 reads crypto futures/derivatives data for a single symbol, listing specific outputs (direction, score, momentum surge, timeframe, market regime) and source (APEX multi-source confluence). It distinguishes from siblings by naming the asset class and API source.

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?

Implied usage: when you need a futures snapshot for a crypto symbol. However, it provides no explicit when-to-use or when-not-to-use guidance, nor does it compare directly to siblings like get_apex_signals or get_btc_snapshot.

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 discloses that raw gamma dollar exposures are not redistributed and that data is educational. However, it does not mention authentication needs, rate limits, or any side effects. This provides moderate transparency beyond the structured fields.

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

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

The description is two sentences covering purpose, output, and limitations. It is front-loaded with the main action and contains no waste. Every sentence 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?

Given no output schema, the description hints at return values (regime label, gamma_bias) and states what is not returned. For a simple one-param tool, this is fairly complete, though more detail on the classification categories would improve 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 single parameter 'symbol' has 0% schema description coverage. The description compensates by specifying it expects one equity/ETF symbol, adding meaning beyond the type 'string' 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 clearly states the tool returns dealer-regime classification for one equity/ETF symbol, including regime label and gamma bias. It distinguishes from many sibling tools that cover options flow, institutional moves, etc., by focusing on gamma exposure analysis.

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 symbol and mentions it's educational, but does not explicitly state when to use this tool over alternatives like get_options_flow or get_volatility_scan. No when-not-to-use guidance is provided.

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 must fully disclose behavior. It describes the returned data but omits details on data recency, pagination, rate limits, or any potential side effects. The disclaimer is legal, not behavioral.

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

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

The description is two concise sentences that front-load the API source and core functionality. Every sentence adds value without redundancy.

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 list tool with two parameters and no output schema, the description adequately conveys the data returned but omits response structure, ordering, and pagination details. It is minimally complete for an agent to use but leaves 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?

Out of two parameters (limit, symbol), only 'symbol' is mentioned in the description with a brief note. The 'limit' parameter is not explained. With 0% schema description coverage, the description fails to add sufficient meaning beyond 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 clearly states the tool retrieves recent institutional 13F position moves, listing specific move types and data fields. This distinguishes it from siblings like get_institutional_stock or get_congress_trades by focusing on aggregated fund-level 13F filings.

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 context for use (tracked institutional moves with optional symbol filter) but does not explicitly state when to use versus alternatives or include exclusion criteria. The sibling names suggest other institutional tools, but no direct comparison is made.

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

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

No annotations provided; description carries burden. It discloses data sources (government disclosures, SEC filings) and exclusions (vendor licensing), but does not mention any behavioral traits like rate limits, auth needs, or side effects. Educational disclaimer adds 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?

Single sentence with clear front-loading of purpose. Source mention ('News Gurus Intel API') is slightly extraneous but not wasteful. Efficient overall.

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?

Description provides good overview but lacks output structure information, which is needed since no output schema exists. Does not clarify pagination or response shape. Adequate but not complete 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?

Schema has 0% description coverage, so description must compensate. It implicitly covers 'symbol' (one ticker) but provides no details on 'limit' parameter. Baseline for low coverage is compensation, but only partial compensation achieved.

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 states it's a per-stock institutional composite combining congressional trades and 13F moves, specific verb (get) and resource (institutional composite for one ticker). It distinguishes from siblings like get_congress_trades and get_institutional_moves by combining both data sources.

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?

Description implies usage for combined congressional and 13F data, but no explicit when-to-use or alternatives. It notes exclusions (analyst ratings, fundamentals) which helps, but lacks direct comparison to sibling 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 must fully disclose behavior. It mentions it returns live performance history but does not clarify read-only nature, pagination (despite a limit parameter), rate limits, or authentication needs. The fields listed are helpful but not sufficient for complete 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?

Single sentence with key information front-loaded. The 'News Gurus Intel API' prefix is somewhat extraneous but does not impede clarity. Overall efficient.

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 two parameters and no output schema, the description outlines return fields and a filter but lacks details on pagination, default limit, or any restrictions. It is adequate for basic understanding but not fully comprehensive.

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 description must add meaning. It explains signal_type as an optional filter, which adds value. However, the limit parameter is completely undocumented in the description. Thus, only one of two parameters is semantically covered.

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 it is a Kelly-criterion outcome ledger listing specific fields (signal_type, pnl, r_multiple, etc.) and labels it as live performance history. This distinguishes it from sibling tools like get_signals or get_trade_decisions.

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?

Only offers minimal guidance: 'Optional ?signal_type= filter for per-strategy slicing.' No explicit when-to-use, when-not-to-use, or comparison to alternatives. The disclaimer 'Educational data, not financial advice' is not a usage guideline.

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 full burden. It explicitly states that the tool returns 'NG-derived bias/score/factors verdict only' and that 'raw candles are not redistributed.' It also labels data as 'Educational data, not financial advice.' These disclosures effectively communicate behavioral traits and limitations.

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

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

The description is two sentences long, front-loaded with the tool's core purpose and immediately followed by parameter details and behavioral notes. Every sentence adds value; no redundancy.

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 explains that the tool returns a 'bias/score/factors verdict' and specifies data source (GeckoTerminal OHLCV) and access method (public/keyless). It could be more detailed about the verdict structure, but it provides sufficient context for a simple parameter set.

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 adds substantial meaning: it explains that 'token' can be a mint address or symbol, and that 'chain' is optional with default 'solana' and possible values 'solana/base/bsc/ethereum'. This fully compensates for the missing schema descriptions.

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 a 'VWAP/Bollinger/RSI/MACD confluence read on a fresh memecoin launch', with a specific verb ('get') and resource ('launch TA confluence'). It also distinguishes itself from siblings by mentioning 'fresh memecoin launch' and the specific indicators, unlike other tools like 'get_memecoin_movers' or 'get_trade_decisions'.

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 lacks any guidance on when to use this tool over alternatives. It does not mention prerequisites, when not to use it, or compare it to sibling tools. The description purely defines functionality without context for selection.

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

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

No annotations are present, so the description must disclose behavioral aspects. It only states the data is educational, but omits details like authentication requirements, rate limits, or whether the operation is safe (read-only). The disclaimer is minimal and insufficient for 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 a single sentence that covers the tool's core functionality without extraneous words. It is appropriately front-loaded with the key purpose. Slightly more structure could improve scannability, but it remains efficient.

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 should indicate the return format. It mentions 'entries from the macro agents' but does not describe the response structure (e.g., fields, data types). While the list of data sources provides context, 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?

The tool has zero parameters and schema coverage is 100%, so the description does not need to explain parameters. The default baseline of 4 applies as there is no missing parameter 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 indicates this tool provides a consolidated macro snapshot including market regime, Fed-speech, BIS, prediction markets, and geopolitical intelligence. It distinguishes from siblings like get_market_regime or get_composite_signal by specifying the exact data sources included. However, it does not explicitly contrast with similar 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?

No guidance is provided on when to use this tool versus alternatives such as get_market_regime or get_composite_signal. The only additional note is a disclaimer ('Educational data, not financial advice'), which does not serve as usage guidance.

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

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

With no annotations, the description carries the full burden. It discloses the source (40-source bot), the nature (live classification), and caveats (educational, not advice). It does not mention any destructive behavior or side effects, but since it's a read-only classification with no parameters, this is sufficient. No contradictions.

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 extremely concise: two sentences, no fluff. The first sentence delivers the core purpose and output, and the second adds important context. Every word 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?

Given the simplicity of the tool (no parameters, no output schema), the description is complete. It covers purpose, output format, source, and caveats. No gaps are evident.

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?

There are 0 parameters and 100% schema coverage. The description does not need to add parameter info. Baseline for 0 parameters is 4, and the description adds no misleading info.

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 'live crypto & equity market regime classification (TRENDING / MEAN_REVERTING / VOL_EXPANSION) with confidence, VIX and ADX context'. It uses a specific verb (classification) and resource (market regime), and distinguishes from siblings by naming specific regimes and context.

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 by mentioning 'Cheapest first call on this API' and 'Educational data, not financial advice', but it does not explicitly state when to use this tool versus alternatives or exclude scenarios. The 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?

No annotations are provided, so the description carries the full burden. It discloses that data is liquidity- and volume-gated, wash-trade noise is filtered out, and it provides educational data (not financial advice). This adds context beyond the schema, though details like rate limits or data freshness 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 two sentences, front-loading key information (what it does and supported chains). It is concise without irrelevant details, though could be slightly more 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?

Given the tool has only one parameter and no output schema, the description covers what the tool returns (top gainers, losers, volume leaders with price, liquidity, 24h stats) and its purpose. It lacks specifics like result count or format, but for a simple data retrieval tool it is reasonably 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, 'chain', has zero schema description coverage. However, the description enumerates the valid chain values (solana, base, bsc, ethereum) and implies the default via the example. This adds meaning beyond the schema's type and default fields.

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 liquidity- and volume-gated memecoin movers for a specific chain, listing output types (gainers, losers, volume leaders) and data points (price, liquidity, 24h stats). It distinguishes from siblings by focusing on memecoin movers with wash-trade noise filtering.

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 specifies the supported chains (solana, base, bsc, ethereum) and indicates it is for a single chain. However, it does not explicitly state when to use this tool versus siblings like 'verify_memecoin' or other market data tools, nor does it provide exclusion criteria.

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. It discloses internal methodology ('Park/weather/sim/Statcast used internally — not redistributed') and verification ('Roster/injury/lineup verified'), and states the entertainment-only purpose. However, it does not explicitly confirm read-only behavior or safety implications.

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 paragraph that efficiently packs key information upfront. It is not overly verbose, but could be structured into bullet points for improved 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, the description adequately describes the content of the sheet (props, matchups, predictions). It covers what the tool returns but does not specify the return format or any limits.

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 0% schema coverage. The description implies it is used to specify the day's sheet ('daily MLB props sheet'), adding semantics beyond the schema. However, it lacks details on accepted format or 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 explicitly states the tool returns 'the FULL daily MLB props sheet' and lists specific content like batter-vs-pitcher matchups, model predictions, etc. It clearly distinguishes itself from siblings like get_sports_props by offering a comprehensive daily sheet.

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 getting the complete daily props sheet with 'One call = the whole day's sheet'. It provides context but does not explicitly exclude alternatives or mention when not to use this 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?

Despite no annotations, the description discloses that the tool uses News Gurus Intel API, internal simulations, roster/injury/lineup verification, and that it's for entertainment/education only. It also mentions it does not redistribute proprietary data. However, it does not mention rate limits, caching, or behavior with empty date parameter.

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 paragraph that covers multiple aspects, but could be more structured with bullet points or separate sections for usage details and parameter info. It's not overly verbose but not optimally 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?

Given the simplicity (1 param, no output schema), the description provides fair context: data sources, content categories, and usage restrictions. However, it lacks information on output format, update frequency, error handling, and explicit parameter documentation.

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

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

The schema has only one parameter 'date' with a default empty string and no description. The description does not explicitly explain the date parameter, its expected format, or how omitting it affects results. It implies daily data but lacks clarity on parameter usage.

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 condensed daily MLB top picks including specific categories like HR picks, hits+runs+RBIs, SP strikeout plays, etc. It distinguishes the tool's purpose (headline plays vs detailed props?), but doesn't explicitly differentiate from the sibling tool get_mlb_props_sheet.

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?

Implicitly suggests usage for daily MLB picks, mentions 'entertainment/educational only, not betting advice' and '21+', but does not provide explicit when-to-use or when-not-to-use guidance, nor does it reference alternative 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 must fully disclose behavioral traits. It mentions 'Pure NG synthesis output — no raw third-party vendor data redistributed' and the limit behavior, but lacks information on read-only status, caching, freshness, authentication, or error cases.

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, filled with jargon ('News Gurus Intel API', 'SharedBrain', 'NARRATIVE_SYNTHESIS'), and mixes content description, parameter behavior, and a disclaimer without clear structure or front-loading key information.

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 few parameters, the description explains what is returned and the limit behavior, but it omits output structure, confidence score interpretation, prerequisites (e.g., symbol selection), and the concept of 'idea-types' essential for understanding the return data.

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 effectively explains the 'limit' parameter's per-idea-type capping and its effect on total returned items, adding substantial meaning beyond the schema's type and default.

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 'macro themes in motion, catalyst-convergence events and confidence scores' from NG-derived intelligence, distinguishing it from sibling tools focused on different data types. However, it is verbose and mixes content description with usage notes.

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 limit behavior per idea-type and includes a disclaimer ('Educational data, not financial advice'), providing some usage context. However, it does not explicitly state when to use this tool versus alternatives 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 provided, so description must carry burden. It does not disclose behavioral traits like read-only nature, authentication needs, rate limits, or data freshness. Only includes disclaimer 'Educational data, not financial advice' which doesn't address behavioral aspects.

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 plus a short disclaimer. Front-loaded with main purpose. Slight fluff in 'The per-call alternative...' but overall efficient. No unnecessary redundancy.

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?

Lacks output schema. Describes key data fields (direction, strength, confidence, size_bucket) and hours parameter, but omits explanation of limit and response structure. Does not cover how signals are generated or what 'sweep clusters' means.

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%. Description explains the 'hours' parameter ('Lookback via ?hours= (default 24)') but does not mention the 'limit' parameter (default 25). Thus adds partial value; not sufficient to compensate for low schema coverage.

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

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

Description clearly states it retrieves 'recent unusual options-flow signals' from a specific API, indicating verb ('get'), resource ('options flow'), and scope ('sweep clusters and large-premium prints'). Distinguishes from siblings by domain, but could be more explicit about 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?

No direct guidance on when to use this tool vs. alternatives. Mentions it's a 'per-call alternative to $50-150/mo flow subscriptions' which is a value proposition, not usage context. No exclusions or when-not-to-use advice.

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?

Without annotations, the description carries the full burden. It discloses data source ('bot's Polymarket intelligence agents'), educational nature, and limits (up to 2000 markets, default 100). It does not specify return format or pagination, but these are typical for such data tools.

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

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

Three concise sentences: one for purpose, one for parameters, one for disclaimers. No redundant information; every sentence adds value.

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 low parameter count and lack of output schema, the description is reasonably complete. It covers what the tool does, what data it returns, and parameter behavior. Minor missing aspects like error conditions are acceptable.

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

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

The schema has 0% coverage, but the description fully explains both parameters: category provides a list of allowed values with examples, and limit clarifies default (100) and maximum (2000). This adds substantial meaning beyond 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 clearly states it retrieves the 'full Polymarket market landscape' with specific attributes (category-grouped odds, liquidity, volume, direction). It distinguishes from sibling tools like get_polymarket_market_stats by focusing on the complete landscape up to 2000 markets.

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 the tool (to get market landscape) and provides usage details for optional parameters (category, limit). It lacks explicit exclusions or comparisons to sibling alternatives, but the context is clear enough for selection.

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

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

No annotations are provided, so description must disclose behavioral traits. It does not mention data freshness, rate limits, authentication requirements, or that it's a read-only operation. Only hints at educational nature.

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?

Single sentence with a concise list of outputs. The disclaimer is minor but acceptable. Could be slightly more structured (e.g., bullet points) but remains efficient.

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

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

With only one parameter and no output schema, the description reasonably covers what the tool returns (unique wallets, buy/sell split, etc.). Missing output format details, but the list is sufficient for an agent to infer the data 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 coverage is 0%, but description adds value by explicitly stating the parameter is 'condition_id' and explains it identifies the market. This bridges the gap between schema and meaning.

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 states the tool aggregates wallet intelligence for a single Polymarket market by condition_id, listing specific outputs (unique wallets, volume split, etc.). It distinguishes from sibling tools like get_polymarket_market_landscape which likely provide broader landscape 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?

No guidance on when to use this tool vs alternatives like get_polymarket_whales or get_polymarket_new_wallets. The disclaimer 'Educational data, not financial advice' does not provide 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?

No annotations are provided, so the description carries the full burden. It includes a disclaimer about educational data, but does not disclose behavioral traits such as rate limits, authentication requirements, or side effects. The description focuses on data content rather than tool 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 comprises two sentences with key information front-loaded. The first sentence is dense but informative, while the second adds a short filter note. It is concise but could be structured more clearly with bullet points or separate filter instructions.

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 3 optional parameters, no output schema, and no annotations, the description provides a good overview of the data content and filtering options. It lacks details on pagination, error handling, or response structure, but is mostly complete for a data retrieval 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 adds meaning by mentioning 'filter by ?condition_id= / ?side=' and implies limit usage. However, it does not explain default values, accepted formats, or the exact effect of each parameter, leaving gaps in semantic clarity.

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 first-time Polymarket bettor fills, fresh wallets taking positions, with details like fill size, side, outcome price, and wallet rolling totals. It distinguishes from siblings like 'get_polymarket_whale_leaders' by focusing on new wallets and insider patterns.

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

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

The description mentions filtering by condition_id and side, and it provides context about being educational data. However, it does not explicitly state when to use this tool over siblings like 'get_polymarket_market_landscape' or 'get_polymarket_whales', limiting clear guidance on alternative selection.

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

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

No annotations are provided, so the description carries full burden. It states 'Educational data, not financial advice' and 'Insider/algo detection per-call', but does not disclose read-only nature, data freshness, rate limits, or potential side effects. The behavioral profile is vague.

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 concise at three sentences, but the second sentence focuses on marketing value ('Elsewhere this data class sits behind $599/mo API plans') rather than functional details. It is front-loaded with the core function, but includes extraneous information.

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 simple parameters, no output schema, and no annotations, the description does not adequately cover parameter semantics, output format, or prerequisites. An agent would lack sufficient context to invoke the tool optimally, especially regarding sort_by options and limit 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%, yet the description does not explain the three parameters (limit, sort_by, min_bets). It hints at sort_by options ('informed score, win rate, realized PnL or volume') but does not specify default or allowed values. Limit and min_bets are unmentioned, leaving the agent to infer.

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 is a Polymarket smart-money leaderboard ranking whale wallets by informed score, win rate, realized PnL, volume, specialization, and average entry odds. This is a specific verb+resource combination that distinguishes it from sibling tools like get_polymarket_whales (likely a different view) and get_polymarket_new_wallets.

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 lacks explicit guidance on when to use this tool versus siblings. It mentions 'Insider/algo detection per-call' and contrasts with paid plans, but does not specify situations where get_polymarket_whales or other siblings are more appropriate. No exclusions or alternatives are 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?

Discloses data source ('Raw public-chain-derived data'), licensing ('no upstream-license restriction'), and optional parameter limit (default 25, cap 100). No annotations provided, so description carries the burden well with no contradictions.

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

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

Three sentences: function, data source, parameter+disclaimer. Concise and front-loaded with main purpose. No wasted words.

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

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

No output schema, but description lists expected fields (address, volume, counts, markets, cabal ideas). Sufficient for a simple list tool with one parameter. Could mention pagination but not essential.

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?

Only one parameter (limit). Schema shows default 25, but description adds cap of 100, providing additional meaning beyond schema. Schema coverage is 0%, so description compensates effectively.

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 it provides 'Polymarket flagged-wallet groupings + coordinated-betting cabal intel' with specific fields (address, volume, counts, markets, cabal clusters). Distinguishes from sibling tools by focusing on whales and cabals, contrasting with tools like get_polymarket_market_stats.

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 for obtaining whale data, but no explicit when-to-use or when-not-to-use vs alternatives like get_polymarket_whale_leaders. The 'Educational data' note is a disclaimer, not a usage guideline.

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 the tool is read-only, returns educational data (not financial advice), and limits results to the most recent ideas. It does not destroy data or require authentication beyond the API key, which is standard. The description adds value by specifying the source (SharedBrain) and cap of 25 ideas.

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 consists of only two sentences, front-loading the core purpose and including key details (data types, source, limits, disclaimer). No redundant information.

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 two parameters and no output schema, the description covers the necessary context: what data to expect, constraints, and purpose. It lacks details on the response structure, but output schema is absent and the tool is simple enough that the description is almost 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%, but the description adds context for the two parameters: 'symbol' is described as an optional filter, and 'limit' is implied by the 'up to 25 ideas per call' statement. It does not specify value formats or defaults (though defaults are in the schema). This is adequate but not detailed.

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 'most recent cross-agent market intelligence ideas' from a 40-source SharedBrain, specifying the types of data (institutional flow, options flow, on-chain, news catalysts). This distinguishes it from sibling tools like get_institutional_moves or get_options_flow, which focus on narrower categories.

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 optional symbol filtering and a limit of up to 25 ideas, providing context for use. However, it does not explicitly state when to use this tool versus alternative data tools (e.g., get_options_flow for options-specific data), nor does it include exclusions or prerequisites.

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 states the data is public and educational but does not disclose whether the feed returns recent movements, historical data, pagination behavior beyond the limit parameter, update frequency, or any rate limits. Key behavioral traits 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 consists of two sentences: the first conveys the core purpose and data fields, the second adds parameter details and a disclaimer. Every sentence is valuable, well-structured, and front-loaded without redundancy or unnecessary detail.

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 one parameter, no output schema, and many siblings, the description covers the essential data and constraints. However, it lacks details on the return format (array of objects?), historical range, or any prerequisites. It is minimally adequate but could be more complete with expected output structure or update behavior.

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 single parameter 'limit' has no description in the input schema (0% coverage). The description adds essential semantics: default 25 and cap 50. This provides the constraint and range information absent from the schema, adequately compensating for the coverage gap.

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

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

The description clearly states the tool provides a feed of on-chain movements from dormant/Satoshi-era BTC wallets, including amount, years dormant, and timestamps. This is a specific and unique resource among the many sibling tools, making the purpose highly clear and differentiated.

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 lacks guidance on when to use this tool versus alternatives like get_btc_snapshot or get_institutional_moves. It includes a disclaimer ('Educational data, not financial advice') but no explicit context for selection or exclusions, leaving the agent without needed usage direction.

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 helps by noting educational purpose and lookback window, but lacks details on data freshness, pagination, rate limits, or whether signals are real-time or historical.

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 wasted words. First sentence states purpose and capabilities; second adds parameter context and disclaimer. Efficient and front-loaded.

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?

No output schema exists, but description mentions returned fields (direction, strength, confidence, source agent), providing some context. However, lacks details on output structure, error handling, and when to prefer this over related tools.

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% (no description in schema). The description adds meaning for the hours parameter (lookback window, default 24) but does not elaborate on symbol format or constraints. Partial compensation but gaps remain.

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 specifies the tool retrieves recent trading signals for one symbol, listing multiple signal types (options flow, dark pool, etc.). It distinguishes well from sibling tools which focus on individual signal types.

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

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

The description mentions the lookback window parameter and defaults but does not provide guidance on when to use this aggregated tool versus the specific signal tools like get_options_flow. No exclusions or alternatives are mentioned.

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 full responsibility for behavioral disclosure. It explains the data source, fields included and excluded, sorting order (by absolute edge_pct descending), and result limit behavior (default 25, cap 50). It does not mention side effects, rate limits, or authentication, but given the read-only nature implied by 'data', this is acceptable. The description is transparent about what the tool does and does not provide, meeting the bar for a non-destructive data retrieval 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 remarkably concise at three sentences, yet packs essential information: data source, field listing, exclusions, sorting, parameter details, and disclaimer. It is front-loaded with the most important information ('News Gurus Intel API — player prop edges') and follows a logical structure. Every sentence adds value with no redundancy or 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?

Given the low complexity (single optional parameter, no output schema), the description is complete. It explains what the tool returns (specific fields), how results are sorted, and the parameter behavior. It also includes a disclaimer about the educational nature. The agent has sufficient information to determine if the tool meets its needs and how to invoke it. No critical information is omitted.

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 (no parameter descriptions in the schema), so the description must fully explain the parameter. It does: it explicitly describes the 'limit' parameter as optional, notes its default (25) and maximum (50), and implies it controls the number of results. This adds significant context beyond the bare schema, enabling the agent to understand and use the parameter 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 specifically states the tool provides 'player prop edges' from the 'player_prop_intelligence_agent' and lists the exact fields included (edge_pct, fair_prob, market_prob, direction, pick identifiers). It also distinguishes itself by noting that Raw PropsMadness fields are excluded due to licensing, avoiding confusion with similar data sources. The verb 'get' combined with the resource 'sports props' and the detailed output fields make the purpose unmistakable.

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 clearly indicates the tool is for retrieving player prop edges from the News Gurus Intel API, implying its use when such data is needed. It does not explicitly state when not to use or compare with sibling tools (e.g., get_mlb_props_sheet), but the unique data source and excluded fields provide implicit context. The phrase 'Educational data, not financial advice' sets expectations for appropriate use, but explicit guidance on alternatives is missing.

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 full burden. It discloses the output includes recent sentiment signals, counts, net bias, and merged ideas from various sources, but does not describe behavioral traits such as data freshness, rate limits, error handling, or what happens if the symbol is invalid. The disclaimer reduces expectations but does not substitute for behavioral details.

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

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

The description consists of two sentences: the first details the content, and the second adds a disclaimer. It is reasonably concise and front-loaded with the primary action. Removing the disclaimer would make it more concise, but it serves a purpose.

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 required parameter, no output schema, and no annotations, the description adequately covers what the tool does and what data it provides (sentiment counts, net bias, sources). However, it could improve by mentioning the output format or data freshness. Overall, it is sufficient for a simple 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 description coverage is 0%, yet the description compensates by explaining that the 'symbol' parameter is a ticker for which sentiment is retrieved. It mentions 'one ticker', clarifying the scope. This adds meaningful context beyond the schema's bare type 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 states it provides per-symbol social sentiment, including bullish vs bearish counts, net bias, and merged social-intelligence ideas from specific sources. It specifies 'for one ticker', making the action and resource unambiguous. Although not explicitly distinguishing from siblings, the unique focus on sentiment differentiates it from other data 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 includes the disclaimer 'Educational data, not financial advice' but offers no guidance on when to use this tool versus sibling tools like get_signals or get_composite_signal. There is no mention of prerequisites, limitations, or alternative tools. The usage context is implied to be for retail sentiment analysis, but explicit when-to-use or when-not-to-use guidance is missing.

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 must disclose behavioral traits. It states no raw vendor data is redistributed and it is educational, but does not explicitly confirm idempotency, read-only nature, or side effects. The description is adequate but lacks explicit safety or access information.

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 paragraph with key details, but could be more concise by removing redundant phrases like 'pure composition of existing derived reads'. It front-loads the core purpose effectively.

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 outlines the tear sheet sections (market regime, composite signal, etc.) but does not specify return structure, data types, or possible empty states. While adequate for a tool with one parameter and simple purpose, it leaves some ambiguity about the output format.

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 one parameter (symbol) with 0% schema description coverage. The description adds that the tool is for 'one ticker' and 'per-symbol', which provides context, but does not explain format requirements (e.g., case, exchange suffix), legal values, or examples, leaving the agent to infer.

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 the tool returns a 'per-symbol tearsheet' with specific components: market regime, composite signal conviction, social sentiment, and 13F moves. It clearly distinguishes from siblings by noting it composes existing reads and is educational, not financial advice.

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 ticker and mentions it's educational, but does not explicitly state when to use this tool versus siblings like get_composite_signal or get_symbol_sentiment, nor does it mention prerequisites or alternatives.

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 full burden. It explains the data content and its educational nature but lacks operational details like rate limits, authentication, or side effects.

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

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

The description is two sentences that are front-loaded with the core value proposition and include essential context. Every sentence adds value with no redundancy.

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 3 optional parameters, no output schema, and no annotations, the description provides a thorough account of the returned data (thesis, signal attribution, etc.). It is adequate for the tool's complexity, though pagination or error details are 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?

Schema description coverage is 0%; the description adds meaning for 'symbol' and 'stage' but omits 'limit'. It partially compensates for the lack of parameter descriptions 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 clearly states it provides 'full LLM trade-reasoning chain' including specific details like thesis, signal attribution, and kelly_fraction. While it distinguishes from siblings by describing a unique dataset, it does not explicitly compare to other 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 notes optional filters and labels data as educational, but it does not specify when to use this tool versus alternatives or mention any prerequisites or 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 must carry full behavioral burden. It mentions proprietary computation and educational disclaimer, but does not explicitly state read-only nature, rate limits, or side effects. More transparency would help.

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 sentence that efficiently conveys purpose, parameters, and context. While concise, breaking it into multiple sentences would improve readability without adding 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?

The description lists output fields but does not explain their meaning or data types, and lacks an output schema. For a data retrieval tool, it is adequate but could be more complete by detailing the return structure and field descriptions.

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?

Despite 0% schema description coverage, the description explains both parameters (asset_class filter and limit with defaults/cap), adding meaning beyond the schema. It could be slightly more detailed on allowed values 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 clearly states it retrieves per-symbol volatility scores from a live database table, listing the fields and filters. It differentiates from sibling tools by specifying its unique data source and purpose.

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 optional filters and default ordering, giving clear context for use. However, it does not explicitly state when to avoid using this tool or mention alternative tools for similar data.

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 fully discloses the tool's behavior: it provides an authenticity verdict, mentions the verification process (6-probe on-chain+social), includes a 'found:false' case, and labels the data as educational. Information about authorization, rate limits, or side effects is absent but not critical for a read-only verification 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, well-structured sentence that packs all essential information: purpose, parameters, output details, edge case, and disclaimer. No redundant words; every clause adds value.

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

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

Given the tool's simplicity (one input, no output schema), the description comprehensively covers input, output fields, edge cases (unscanned addresses), and the tool's nature (educational). The sibling tools are all get_* functions, so the purpose is clear. No gaps remain.

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 sole parameter 'mint' has no description in the schema (0% coverage), but the description adds valuable context by specifying it's a Solana token address. While format examples are missing, the context is sufficient for an AI agent to infer the required input.

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 the action (verify token authenticity), the resource (Solana mint), and the output (0-1 realness score, confidence, action, rug-risk flags, etc.). It also distinguishes that it returns found:false for unscanned addresses. This is highly specific and clearly distinguishes the tool from 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?

The description clearly indicates the tool is for verifying a single Solana token mint. While it does not explicitly state when not to use it or name alternatives, the context is clear and the sibling tools are all retrieval functions, so confusion is unlikely. A minor deduction for lack of explicit exclusions.

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