newsgurus-intel

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

Describes the tool as a liveness probe returning counts and timestamps, implying read-only behavior. However, no annotations exist, and the description lacks explicit mention of side effects or safety, though none are expected.

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?

Contains marketing fluff and a disclaimer that do not aid tool selection. Could be more concise by removing extraneous phrases like 'same price tier as /x402/regime' and 'Educational data, not financial advice'.

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?

Describes the output shape (counts, timestamps, across agents) but lacks explicit return structure or fields. Given no output schema, more details on format would improve completeness.

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

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

No parameters exist, and schema coverage is 100%. The description adds no parameter info, which is acceptable 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?

Clearly states the tool retrieves per-agent intelligence output counts and last-seen timestamps over the last 24h, acting as a liveness probe. Distinguished from siblings by its focus on agent status rather than signals or trades.

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

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

Explicitly recommends using this tool to verify the engine is live before buying data, providing a clear usage context. Does not explicitly state when not to use or list alternatives, but the purpose alone differentiates 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 bears full burden for behavioral disclosure. It fails to mention read-only status, authentication needs, rate limits, or data freshness. The disclaimer 'Educational data, not financial advice' does not address behavioral traits.

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

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

The description is relatively brief but includes fluff ('News Gurus Intel API —', 'The bot's own risk filters'). It front-loads the core purpose but could be trimmed for efficiency.

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 presence of sibling tools, one parameter, no output schema, and no annotations, the description is incomplete. It lacks details on output format, usage context, and how to interpret the anomaly data for effective decision-making.

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%, yet the description provides no information about the single parameter 'limit'—its purpose, range, or effect. The agent receives no guidance 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 explicitly states the tool returns 'recent ANOMALY detections' with specific types (pump/psyop spikes, rug precursors, etc.), clearly identifying the resource and action. It distinguishes from siblings like 'get_signals' by focusing on anomalies.

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 like 'get_signals' or 'get_catalog'. The description implies it is for anomaly detection but does not specify scenarios or exclusions, leaving the agent to infer usage.

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 labels the call as a 'one-call read' implying a single read operation, but does not detail side effects, authentication needs, rate limits, or response format. The absence of such information leaves significant gaps for the agent.

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 with no waste. The key purpose is front-loaded, and the disclaimer is appended succinctly. 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 no parameters and no output schema, the description covers the main data points returned. It lists dormant wallet state, recent moves, APEX signals, and market regime, which seems sufficient for a snapshot tool. However, without an output schema, the agent cannot verify completeness, and some users might expect more detail like data formats or sources.

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 zero parameters, so the baseline is 4. The description explains what the tool returns (dormant wallet state, recent moves, APEX signals, market regime), adding value beyond the empty schema. It could be scored higher if it elaborated on the output structure, but 4 is appropriate given the baseline.

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

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

The description clearly states the tool's purpose: a one-call read for BTC dormant wallet movement state, recent moves, APEX signals, and market regime. It uses specific verbs and resources, and the sibling tools cover different assets or metrics, so it is well-distinguished.

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_macro_snapshot or get_market_regime. The disclaimer 'Educational data, not financial advice' is present but does not help with selection 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?

Despite no annotations, the description declares the tool is FREE and requires no payment, disclosing its non-destructive nature. It lacks details on potential rate limits or response size, but given zero parameters and presence of output schema, the description is adequately transparent.

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

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

Two sentences, no redundancy. Front-loads 'FREE' and actionable 'list every News Gurus Intel tool.' Every sentence contributes purpose or usage guidance.

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

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

With zero parameters and an output schema describing return values, the description covers all necessary context. It tells the agent this is the entry point for tool selection, which is complete for the tool's role.

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

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

Tool has 0 parameters and 100% schema description coverage. The description adds no parameter info, but baseline 4 applies as no compensation needed for zero-parameter tools.

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 'list every News Gurus Intel tool with its price (USDC on Base via x402), input schema and example output.' This specifies the action (list), resource (every tool), and details (price, schema, output), distinguishing it from siblings that retrieve specific 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?

Explicitly says 'Call this first to decide what to buy,' providing clear when-to-use guidance. Also states 'No payment required,' which clarifies no prerequisites or costs.

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

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

No annotations provided, so description carries full burden. It discloses key behavioral traits: returns aggregated counts and confidence, is educational data. However, it could be more explicit about output structure or any side effects (likely none). 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?

Two clear sentences plus a tagline, no wasted words. Key information is front-loaded: action, scope, output summary, and disclaimer.

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 and lack of output schema, description covers purpose, data returned, and disclaimer. Missing some detail on result structure (e.g., bullish/bearish counts format) is acceptable but could be improved.

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, symbol, with 0% schema description coverage. Description adds meaning by stating it is for 'one symbol' and the tool is per-symbol. It does not detail format (e.g., ticker case), but this is sufficient for a simple parameter.

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

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

Description clearly specifies action (read composite signal), resource (per symbol), and output (latest 50 signals merged into bullish/bearish counts and average confidence). Distinguishes itself from sibling tools like get_signals by emphasizing it's a composite summary.

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 indicates it's the 'flagship per-symbol conviction snapshot' but does not explicitly state when to use vs. alternatives or provide exclusions. No guidance on prerequisites or when not to use.

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 only states data fields and an optional filter, but lacks details on rate limits, authentication, data freshness, or any side effects. The disclaimer 'Educational data, not financial advice' adds little 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 a single sentence that covers purpose, data fields, and an optional filter. It is concise but includes marketing language ('$25-75/mo congressional-data plans'). It is front-loaded with the main function and avoids verbosity.

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 absence of output schema and annotations, the description provides a reasonable overview of the tool's data and filtering. However, it misses details on result format, pagination, error handling, and the meaning of 'recent'. The 'limit' parameter suggests results may be limited but is not explained.

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

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

Schema coverage is 0%, so the description should compensate. It adds meaning for the 'symbol' parameter (optional filter), but doesn't describe 'limit' beyond its default. The parameter semantics are minimally enhanced for one of two 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 it provides 'recent US congressional stock transactions' and lists fields (member, party, state, buy/sell, ticker, etc.). It names the specific resource 'congress_trades' and distinguishes itself from sibling tools like 'get_anomalies' or 'get_options_flow' by the explicit domain of congressional trading.

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 an optional '?symbol=' filter and positions the tool as an affordable alternative to paid services, but it does not provide explicit guidance on when to use this tool versus sibling tools. No exclusions or context for tool selection are given.

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

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

With no annotations, the description partially discloses behavior: recent data, filtered to significant blocks, lookback via hours parameter. However, it omits details like data delay, authentication needs, rate limits, or read-only nature, which are important for safe invocation.

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 with two sentences, front-loading purpose and key details. It could be slightly more structured (e.g., listing parameters explicitly), but overall it is efficient and readable.

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

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

Given no output schema and two optional parameters, the description lacks necessary details: no explanation of return format, pagination, or behavior when no data is found. The mention of fields (symbol, notional, etc.) is helpful but insufficient for 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%, so description must compensate. It explains the 'hours' parameter as lookback with default 48, but does not mention 'limit' at all. This leaves one parameter completely undocumented, reducing the value added.

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 institutional dark-pool prints with specific details (symbol, notional, price, share size, exchange context). It uses the verb 'get' and resource 'dark-pool prints', distinguishing it from sibling data tools like get_options_flow or get_gamma_exposure.

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. The description includes a disclaimer but no context about appropriate use cases or exclusions, leaving the agent without decision-making support.

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 the data source and educational nature but lacks details on update frequency, pagination, rate limits, or any behavioral traits. Provides minimal context beyond what is evident from the name and schema.

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

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

The description is one sentence with parenthetical notes, conveying necessary information but somewhat verbose. Could be streamlined to improve readability without losing 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 and many sibling tools, the description covers the purpose and criteria but does not detail the output format or fields, leaving ambiguity about the returned data structure. Adequate but incomplete.

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 value by explaining what the single no-parameter call returns, including the ranking criteria and source.

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 that the tool returns full quality-compounder rankings and growth-stock LEAPS candidates in one call, detailing the criteria (high-margin, low-debt, high-cash, growing-revenue US equities + crypto protocols) and noting it's percentile-scored. It distinguishes from siblings by describing a unique data product.

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

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

The description implies use for obtaining ranked lists but does not provide explicit guidance on when to use this tool versus siblings like get_anomalies or get_signals. No exclusion criteria or alternatives 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?

No annotations are provided, so the description carries full burden. It states the tool is a read operation and provides a disclaimer, but lacks details on authentication, rate limits, error behavior, or data freshness. The behavioral traits are minimally disclosed.

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

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

The description is two sentences with no wasted words. It front-loads the core purpose and ends with a disclaimer. It could be more structured but is concise and to the point.

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 one-parameter tool with no output schema or annotations, the description covers inputs and output types. However, it lacks details on output format (e.g., numerical ranges) and error handling, leaving gaps 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 description coverage is 0%, and the description adds examples of valid symbols (BTC, ETH, SOL, XRP) and mentions the symbol is crypto-related. However, it does not specify case sensitivity, format, or exhaustive list, so value added is moderate.

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 as a read for crypto futures/derivatives data for one symbol, listing specific outputs (direction, score, momentum surge, timeframe, regime). It distinguishes from siblings by naming the API and data types, making it clear what it does.

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 educational context, but provides no explicit guidance on when to use this tool over siblings like get_signals or get_market_regime. No alternatives or exclusions are stated.

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

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

No annotations are provided, so the description carries full burden. It mentions the data is 'live' and 'educational, not financial advice' but does not disclose whether it's read-only, auth requirements, rate limits, or symbol limitations. Adequate but could be more detailed.

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 efficiently conveys purpose, outputs, and nature without extraneous words. It is front-loaded with 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 the simple input (one symbol) and lack of output schema, the description adequately covers what the tool does and what it returns. It could mention the output format or potential errors, but for a straightforward 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?

Schema description coverage is 0%, so the description must compensate. It clearly indicates the 'symbol' parameter is an equity/ETF symbol and that the tool returns gamma exposure data for that symbol, adding 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 provides a live dealer gamma exposure snapshot for one equity/ETF symbol, listing specific outputs (net gamma, split, regime) and use case (squeeze/volatility analysis). It distinguishes itself from sibling tools which cover other financial data domains.

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

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

The description implies the tool is for equity/ETF gamma analysis but does not explicitly state when to use it vs alternatives or provide exclusions. No guidance on prerequisites or conditions.

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

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

No annotations provided. Description does not disclose behavioral traits such as whether it is read-only, any authentication requirements, rate limits, or side effects. It only states the default behavior and disclaimer, lacking transparency on safety or constraints.

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 effectively summarizes tool purpose and key features, with a brief disclaimer. Could be slightly more concise by omitting 'Educational data, not financial advice' as it's not behavioral, but 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 no output schema and few parameters, the description covers the main return data (shares, value, weight, date) and the optional filter. Missing explanation for limit parameter, but otherwise adequate 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 description coverage is 0%, so the description must compensate. It explains the symbol parameter as an optional filter but does not mention the limit parameter (integer, default 25). This partial coverage leaves the limit parameter's meaning unclear.

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 institutional 13F position moves across tracked funds, specifying types (new/increased/decreased/sold) and data fields (shares, value, portfolio weight, filing date). This distinguishes it from sibling tools like get_congress_trades or get_options_flow.

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 querying institutional moves with an optional symbol filter. No explicit when-to-use or when-not-to-use compared to siblings, but the name and description make the domain clear.

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

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

No annotations are provided, so the description carries the burden. It only includes a disclaimer about educational data, but does not disclose any behavioral traits such as authentication requirements, rate limits, 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 a single sentence that conveys the essential purpose efficiently. Slight room for improvement by front-loading the core action.

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 zero parameters and no output schema, the description adequately lists the types of data included. However, it could benefit from mentioning response format or structure for better completeness.

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

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

With zero parameters, the schema is trivial. The description adds value by explaining the data content beyond the tool name. Baseline for 0 params is 4.

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 'macro one-call read' and enumerates specific content types (market regime, Fed-speech, BIS, prediction-market, geopolitical intelligence). It distinguishes from sibling tools which target specific domains.

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 broad macro overview, but does not provide explicit when-to-use or when-not-to-use guidance, nor does it reference alternatives among siblings.

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

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

No annotations exist, so the description carries full burden. It discloses the data source (40-source bot), educational purpose, and non-advice disclaimer. However, it does not explicitly state read-only nature or any potential side effects, leaving some ambiguity.

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 efficient sentences convey purpose, key outputs, source, cost hint, and disclaimer. No filler or 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 zero-parameter tool with no output schema, the description covers the output types, context, source, and warnings. A brief note on the response format would increase completeness.

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

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

No parameters in input schema (baseline 4). Description adds value by explaining what the tool returns (regime types, confidence, VIX, ADX), beyond the empty 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?

Clearly states the tool returns market regime classification (TRENDING/MEAN_REVERTING/VOL_EXPANSION) with confidence, VIX, ADX context. However, it does not explicitly differentiate from sibling tools like get_anomalies or get_signals, missing an opportunity to distinguish.

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

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

Provides no explicit guidance on when to use this tool versus alternatives. The mention 'Cheapest first call' hints at cost-efficiency but does not establish clear usage criteria 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 provided, the description carries the full burden. It discloses that data is educational only, not financial advice, and that wash-trade noise is filtered out. However, it does not detail return format, pagination, or rate limits, though these are less critical for a read tool.

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

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

The description is extremely concise, using two sentences to cover purpose, parameter, data content, and disclaimer. No superfluous words, and key information is 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?

Given the single parameter and lack of output schema, the description provides a reasonable overview of returned data (gainers, losers, volume leaders, stats). However, it does not specify exact field names or data structure, leaving some ambiguity for the 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?

Despite schema coverage of 0% in the structured schema, the description lists the allowed values for the chain parameter (solana, base, bsc, ethereum) and explains its role in selecting the network. This adds meaning beyond the schema's simple string type.

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 liquidity- and volume-gated memecoin movers for a specific chain, listing top gainers, losers, and volume leaders with price, liquidity, and 24h stats. It specifies the API source and educational purpose, distinguishing it from sibling tools like verify_memecoin or get_anomalies.

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

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

The description implies usage when memecoin movers are needed and specifies the chain parameter options, but does not explicitly state when to use this tool versus alternatives like verify_memecoin. No exclusions or prerequisites 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, the description carries the full transparency burden. It mentions data sources, verification, and legal disclaimers (entertainment only, 21+), but lacks details on rate limits, costs beyond a single mention, or whether the call is destructive. The safety profile is implied as read-only, but not explicitly stated.

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

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

The description is verbose (4 sentences) with marketing language, pricing, and disclaimers that dilute the core functional description. Could be more succinct while retaining essential 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?

Despite lacking an output schema, the description enumerates the key data categories (prop picks, matchups, model predictions, risk flags) and data sources (Statcast, win-prob sim). However, it does not specify output structure or pagination, which would be helpful for a complex daily sheet.

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 does not clarify the 'date' parameter's purpose, format, or default behavior. The agent must infer from the parameter name and default empty string. The description adds no semantic value 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 provides 'the FULL daily MLB props sheet' and enumerates specific contents like HR picks, matchups, and model predictions. It distinguishes from the sibling 'get_mlb_top_picks' by emphasizing comprehensiveness ('full 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?

No explicit guidance on when to use this tool vs alternatives like 'get_mlb_top_picks' or other siblings. The description focuses on what the tool returns but does not indicate prerequisites, typical use cases, 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, description carries full burden. It states data is 'condensed', 'verified', and is for entertainment/educational purposes, but does not disclose any potential side effects, rate limits, or data freshness.

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?

Description is a single paragraph with multiple sentences, efficiently conveying the tool's purpose and limitations. However, it could benefit from better structuring (e.g., bullet points) for clarity.

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

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

Missing output schema; description does not specify the response format or structure. The tool is simple but key details about the date parameter and output are absent.

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 for the single 'date' parameter. The description does not explain the expected format, default behavior, or permissible values, failing to compensate for schema deficiency.

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 returns condensed daily MLB picks including specific categories like top-10 HR picks, hits+runs+RBIs picks, etc. Distinguishes from sibling 'get_mlb_props_sheet' by mentioning it's the 'headline plays only' and referencing the full paid version.

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?

Hints at scope by saying 'headline plays only' and mentions 'buy /x402/mlb/props-sheet for complete analysis', but does not explicitly state when to use this tool vs 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?

With no annotations, the description carries the full burden. It discloses the tool provides educational data (not financial advice) and describes the nature of the signals (sweep clusters, premium prints, IV-context reads). No destructive behavior is implied, and it appears read-only. Additional details like rate limits or auth would improve it.

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

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

The description is relatively concise, front-loading the main purpose. The sentence about educational data adds necessary context. However, it could be slightly more structured, e.g., separating functionality from disclaimers.

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

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

Given no output schema and no annotations, the description provides adequate context for the tool's purpose and the hours parameter, but lacks details on the limit parameter and the structure of the returned data. More information on the output format would enhance completeness.

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

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

Schema coverage is 0%, so the description must add meaning. It clarifies the 'hours' parameter as 'lookback via ?hours= (default 24)', but does not mention 'limit'. With two parameters, only one is explained, leaving the agent to infer the purpose of limit.

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 'unusual options-flow signals: sweep clusters, large premium prints and IV-context reads' across equities, with direction, strength, and confidence. This distinguishes it from sibling tools like get_anomalies or get_congress_trades.

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

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

The description notes 'The per-call alternative to $50-150/mo flow subscriptions,' indicating a cost-effective use case. It also mentions the default lookback via hours parameter. However, explicit guidance on when not to use or alternatives among siblings is lacking.

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

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

No annotations provided, so description carries full burden. It discloses the educational nature and lists returned metrics, suggesting a read-only query. However, it lacks details on side effects, rate limits, or error behavior. Does not contradict annotations (none present).

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

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

Two sentences, front-loaded with the core functionality. No filler words, though it could be slightly more structured. Each sentence earns its place by describing purpose and output.

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?

Lists key output fields but omits return format, pagination, or error handling. With no output schema or annotations, the description covers the basics but is not fully complete for a single-parameter tool.

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

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

Schema coverage is 0% for condition_id with no description in schema. The description mentions 'by condition_id' but does not explain what it is or how to obtain it. For a critical required parameter, this leaves ambiguity.

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

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

Clearly states the tool aggregates wallet intelligence for one Polymarket market by condition_id, listing specific outputs (unique wallets, volume split, new-wallet stats, top-10 buyers). Distinguishes from siblings like get_polymarket_new_wallets and get_polymarket_whale_leaders by focusing on a single market's comprehensive 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 use for retrieving aggregate wallet data for a specific market, and includes an educational disclaimer, but does not explicitly state when to use this tool versus related tools (e.g., get_polymarket_new_wallets) or any prerequisites for the condition_id.

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

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

With no annotations, the description carries the burden. It explains that the tool identifies 'suspiciously confident new wallet' patterns and returns educational data. However, it does not disclose idempotency, rate limits, or authentication requirements.

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 (two sentences plus a tagline) with no redundant information. It front-loads the key result and filters. Minor jargon ('insider-pattern feed') could be clearer but does not waste space.

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 3 optional parameters, the description provides reasonable context: it lists the output fields (fill size, side, outcome price, rolling totals) and mentions filters. However, it could elaborate on output format or additional use cases.

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

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

Schema coverage is 0%, so the description must compensate. It adds meaning to condition_id and side by mentioning filtering, but does not explain the side parameter's valid values or the limit parameter. This partial coverage merits a 3.

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

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

The description clearly states the tool's purpose: retrieving first-time Polymarket bettor fills (new wallets taking positions) with details like fill size, side, outcome price, and rolling totals. It distinguishes from sibling tools like get_polymarket_market_stats and get_polymarket_whale_leaders by focusing on new wallet activity.

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

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

The description mentions filtering by condition_id and side, providing some usage context. However, it lacks explicit guidance on when to use this tool versus alternatives, and does not specify 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 provided, so description carries burden. Mentions 'Insider/algo detection per-call' but doesn't disclose data freshness, rate limits, or error handling. 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?

Two sentences with clear purpose front-loaded. The promotional and disclaimer text could be shortened, but 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?

With no output schema and sparse parameter info, description provides a high-level overview but lacks details on return format, pagination, or error cases. Adequate for a simple list but incomplete.

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. Description only hints at possible sort_by values (informed score, win rate, etc.) but doesn't explain limit or min_bets 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?

Clearly states the tool returns a leaderboard of Polymarket whale wallets ranked by various metrics. Differentiates from siblings like get_polymarket_market_stats by focusing on smart-money leaderboard.

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

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

Provides context (API source, pricing comparison, educational disclaimer) but lacks explicit guidance on when to use this tool versus alternatives like get_polymarket_new_wallets.

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

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

With no annotations, the description carries the behavioral disclosure burden. It indicates read-only behavior by stating it retrieves 'educational data' and imposes a limit of 'up to 25 ideas per call', but omits details on rate limits, authentication needs, or whether data is destroyed upon retrieval.

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, consisting of only two sentences. The first sentence front-loads the core purpose and data sources, while the second adds a necessary disclaimer. Every word serves a purpose 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?

Given the tool's simplicity (2 parameters, no output schema), the description is mostly adequate but lacks detail on the returned data structure. It does not mention pagination, response fields, or how results are ordered, which are important for an agent to process the output correctly.

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

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

The schema has 0% coverage, so the description must compensate. It explains the 'symbol' parameter as an 'optional symbol filter' and clarifies that 'limit' defaults to 10 but can go up to 25. This adds meaningful context beyond the raw schema, though the symbol format is not specified.

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 'most recent cross-agent market intelligence ideas' from a 40-source SharedBrain, specifying the types of data (institutional flow, options flow, etc.). However, it does not explicitly differentiate from sibling tools like 'get_signals' or 'get_anomalies', which could lead to ambiguity.

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. The description lacks information about prerequisites, such as required authentication or context, and does not address exclusions 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?

With no annotations, the description carries full burden. It discloses that the tool returns educational data (not actions), includes a lookback window, and provides multi-faceted signals. However, it does not mention error handling, rate limits, 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 concise, front-loading the API context and purpose in a single sentence, then adding the lookback window detail and disclaimer. No redundant information, but slightly fragmented.

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 limited annotations, the description provides the essential return structure and input parameters. It is adequate for a simple retrieval tool but lacks completeness on error conditions or usage 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 provides 0% description coverage, so the description must compensate. It explains the 'hours' parameter as a lookback window with default 24, but the 'symbol' parameter is only implied as 'one symbol' with no format or constraints. Additional details 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?

The description clearly states it returns 'recent trading signals for one symbol' and enumerates specific signal types (options flow, dark pool, etc.) and attributes (direction, strength, confidence, source agent). This distinguishes it from sibling tools like get_composite_signal and get_anomalies.

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 (get_anomalies, get_catalog, etc.). The description only states what it does without indicating 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 the full burden. It describes the data content (counts, sources) but does not disclose read-only nature, update frequency, or any side effects. The disclaimer 'Educational data, not financial advice' adds context but does not cover behavioral traits.

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

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

The description is efficient: two sentences, front-loaded with core functionality. The first sentence packs purpose and output details. The second is a brief disclaimer. Minor improvement could remove the API name prefix.

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 complexity (multiple data sources, no output schema), the description lists returned fields but lacks detail on format, time range, or numerical ranges. It provides a reasonable overview for an educational tool but leaves some ambiguity.

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 significant meaning by explaining that the 'symbol' parameter is a ticker. It clarifies the parameter's role (per-symbol, one ticker) beyond the schema's bare type 'string'.

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 social sentiment for a specific ticker, listing key data points (bullish/bearish counts, net bias) and sources (LunarCrush, StockTwits, Telegram, X). This distinguishes it from sibling tools like get_composite_signal or get_signals.

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

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

The description implies usage is for obtaining recent sentiment for one ticker, but does not explicitly state when to use this versus alternatives like get_composite_signal. No exclusion criteria or prerequisites are given.

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

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

With no annotations, the description provides substantial behavioral details: it returns a 0-1 realness score, confidence, recommended action, rug-risk flags, psyop/pump detection, narrative tags, and reports 'found:false' for unscanned addresses. It also mentions the 6-probe verifier and creator rug-history. Missing are rate limits or authentication requirements.

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 followed by a short note, efficiently packing information. While it could be broken into bullet points for readability, it is concise 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?

Given the simple input (one string) and no output schema, the description adequately explains the output structure and behavior for unscanned addresses. It could benefit from specifying the exact output format or examples.

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

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

The only parameter 'mint' has 0% schema description coverage, but the description explains it is a Solana mint address, adding crucial context beyond the raw 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 identifies the tool as a token authenticity verifier for Solana mints, specifying the return fields (realness score, confidence, action, flags, tags). It distinguishes itself from sibling tools like get_memecoin_movers and get_anomalies by its specific function.

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

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

The description implies usage when needing to verify a single Solana token, but lacks explicit when-to-use/when-not-to-use guidance or comparisons with alternatives. The disclaimer 'Educational data, not financial advice' adds context but does not direct the agent.

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