Cabrini Market Data

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

Describes output fields (OHLC, fractional change, volume, transactions) and cost ($0.015 per day). No annotations exist, so description carries full burden. Missing error conditions or auth requirements, but covers key 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?

Two sentences, front-loaded with core purpose and data details. No redundant information, efficient and clear.

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 fully explains return values (OHLC, fractional change, volume, transactions) and includes pricing. With no annotations, description provides sufficient context for agent to understand 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?

Schema coverage is 100%, baseline 3. Description mentions intervals and confirms resampling, but the schema already details the interval parameter. Adds minimal extra 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?

Clearly states it fetches resampled intraday bars for one ticker at custom timeframes, listing allowed intervals. Distinguishes from siblings like query_minute_bars and query_daily by specifying the resampling nature and output fields.

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

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

Implied usage for intraday bar data analysis, but no explicit when-to-use or when-not-to-use compared to alternative tools. Lacks guidance on scenarios like when raw minute bars or daily bars are more appropriate.

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 bears full burden. It mentions the cost ($0.25 USDC), which is useful, but does not disclose authentication needs, error behavior, or data freshness. The cost disclosure adds some value, but other behavioral aspects are missing.

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

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

The description is a single, efficient sentence that includes the core function and cost, with no wasted words. It is front-loaded with the key 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 no output schema, the description explains what categories are included in the brief. However, it is somewhat vague ('full research brief') and could specify the format or completeness. For a tool with 3 parameters and no nested objects, 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 coverage is 100%, so baseline is 3. The description does not add meaning beyond the schema; it lists output categories but not parameter details like date format or lookback_days interpretation.

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 a 'full research brief' including price, fundamentals, insiders, and splits for one ticker, which distinguishes it from sibling tools like get_bars, get_fundamentals, and get_insiders.

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 a comprehensive overview of a single ticker, but lacks explicit when-not or alternative tool guidance. The sibling tool list is provided in context outside the description.

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

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

With no annotations, the description must disclose behavior fully. It mentions cost but not that the tool is read-only, requires authentication, or has any rate limits or side effects. The return format and error handling are not described.

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 short sentences) and front-loaded with the main purpose. It efficiently conveys the tool's essence without 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?

For a simple tool with one parameter and no output schema, the description is adequate but lacks detail on the return format (e.g., JSON structure, field types) or examples. It covers the core fields but is not fully self-contained.

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

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

The schema already describes the ticker parameter clearly (e.g., AAPL). The description adds no additional semantic information about the parameter beyond listing the fields returned. Schema coverage is 100%, so baseline is 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 returns a company profile with specific fields (name, CIK, industry, exchange, fiscal year) and cost. It distinguishes itself from siblings like get_bars and get_pricing by focusing on profile data, but does not explicitly differentiate from get_brief, which may return similar info.

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 versus alternatives. It does not mention prerequisites, such as requiring a valid ticker, or cases where other tools (e.g., get_brief) might be more appropriate.

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 pricing and section extraction behavior but does not mention data freshness, permissions, rate limits, or whether the operation is destructive. Adequate but incomplete.

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. The second sentence is dense but still efficient. Could be slightly clearer by splitting pricing and extraction 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?

No output schema, so description should explain return structure. It says 'filing index' but does not specify fields. Section extraction is explained well. Lacks details on pagination, response format, or error handling, which are needed for full 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 100%. Description adds meaning beyond schema: explains that sections extract text from latest 10-K/10-Q unless accession specified, and provides pricing context for sections parameter. This enhances understanding.

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 SEC filing index for a ticker, listing specific filing types (10-K, 10-Q, etc.) and optional section extraction. This distinguishes it from sibling tools like get_bars or get_pricing.

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 pricing but does not provide explicit when-to-use guidance or alternatives among siblings. Usage is implied for SEC filing retrieval, but no exclusions or comparisons.

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 full burden. Only mentions cost ($0.02 USDC) but does not disclose side effects, permissions, rate limits, or response format for this fundamental data 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?

Efficient single sentence including cost information. No wasted words, but could be more structured with separate lines for purpose and pricing.

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 4 parameters, no output schema, and no annotations, the description is insufficient. It does not explain return structure, date format details, or how metrics filtering works, leaving gaps for proper tool invocation.

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

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

Schema covers 100% of parameters with descriptions. Description adds examples of metrics (revenue, EPS) but does not clarify array format for metrics parameter or date format constraints beyond schema. Baseline 3 with minimal added 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?

Clearly identifies the tool provides quarterly fundamentals from SEC EDGAR, listing specific data types (revenue, EPS, margins). Distinguishes from sibling tools like get_bars (price bars) and get_stats (statistics).

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 or when not to use it. The description lacks context about appropriate use cases or alternatives among 15 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 description carries full burden. It discloses cost but omits behavioral traits such as read-only nature, authentication requirements, rate limits, pagination, or what happens when no data exists.

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 very short and front-loaded with the key purpose. It wastes no words. However, the extreme brevity sacrifices important behavioral and usage 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 3 parameters, no output schema, and no annotations, the description is incomplete. It lacks information on output format, data range implications, and how this tool integrates with sibling 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 100%, so baseline is 3. Description adds no additional meaning beyond the schema's param descriptions (ticker, to_date, from_date). No further context is provided.

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 retrieves insider transactions (Form 4) from SEC EDGAR, specifying the data source and regulatory form. The $0.02 USDC cost is also noted. This distinguishes it from sibling tools like get_filings or get_company.

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 versus alternatives like get_filings. Does not mention any prerequisites, context, or exclusions. The description is purely declarative with no usage 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?

No annotations provided; description only says 'Free', which is not behavioral. No disclosure of whether this is a read operation, mutation, rate limits, auth needs, or expected output format.

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

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

The description is very short but lacks substance. It is concise but under-specified, not earning its place by providing 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?

Given zero parameters, no output schema, and many sibling tools, the description fails to explain the tool's role or what 'pricing and data coverage' means, leaving the agent without sufficient context.

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

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

Tool has no parameters, so schema coverage is 100%. Baseline is 4; the description adds 'Free' which is extra but not about parameters. Score is appropriate.

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

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

The description says 'Pricing and data coverage information. Free.' It is vague about what specific pricing or coverage data it returns, and it does not differentiate from siblings like get_bars or get_company.

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 versus alternatives. The description does not mention usage context, 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 provided, the description fully discloses that the tool is free, uses real data, and is identical in shape to paid queries, setting appropriate expectations 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?

Two concise sentences with no fluff. Front-loaded with key attraction ('FREE, no payment') and purpose, then details. 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 tool's simplicity (0 parameters, fixed data), the description fully covers purpose, usage, and behavioral context. No output schema needed for such a sample tool.

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

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

The schema has no parameters, so description coverage is 100%. The description reinforces that it takes no arguments, which matches the schema, but adds no new semantic meaning beyond what's already clear.

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 FREE real intraday OHLCV bars for AAPL on a fixed date, and explicitly says 'Call this first to verify data quality before spending', distinguishing it from sibling tools like get_bars or query_minute_bars.

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

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

It gives explicit guidance: 'Call this first to verify data quality before spending', implying use before paid queries, and notes it takes no arguments, so it's not for other tickers/dates.

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 only states 'Free', which is not a behavioral trait. It fails to disclose read-only status, cost implications beyond 'free', or other behaviors.

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

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

The description is too brief (two words) and lacks necessary detail. It is under-specified rather than concisely informative.

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 parameters and no output schema, the description should explain what statistics are returned. It is incomplete and does not help an agent understand the tool's output.

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, so schema coverage is 100%. The description adds no parameter info but this is acceptable as 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 'Platform statistics. Free.' is vague and does not specify what kind of statistics. It barely adds to the name 'get_stats' and fails to differentiate from siblings like get_bars or get_pricing.

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 versus alternatives. The description lacks context for invocation 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?

No annotations provided, so the description carries full burden. It mentions a cost but does not disclose pagination, data freshness, rate limits, or return format. Critical behavioral details are missing for a list operation.

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 with one sentence plus cost. It is front-loaded with the action and contains no superfluous information. 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 simple parameter and no output schema, the description is mostly complete for a basic list operation. However, it lacks indication of what the output looks like (e.g., list of ticker strings). Slightly 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 coverage is 100% for the single parameter. The description does not add extra meaning beyond the schema's 'YYYY-MM-DD' format. Baseline 3 is appropriate since schema already describes it adequately.

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 all tickers that traded on a given date. The verb 'list' and resource 'tickers' are specific, and the date condition is explicit. It distinguishes from sibling tools that are GET operations for individual data or queries.

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 versus alternatives. The description only states what it does and the cost, without mentioning exclusions or when not to use it. Siblings are different, but no explicit comparison.

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

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

Without annotations, the description details output content (OHLC + fractional change) and cost ($0.02/ticker, no limit). This adds significant value beyond basic schema, though error handling or rate limits are omitted.

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 purpose, no wasted words. Every sentence adds distinct 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?

Output schema is absent, but the description partially explains return values. However, it lacks guidance on sibling tool differentiation and does not address error scenarios or limits beyond cost.

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

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

Schema coverage is 100% with descriptions for both parameters. The description does not add further syntax or meaning to the parameters themselves, but confirms the batch nature.

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 bars for multiple tickers on a single date, with specific output fields (OHLC and fractional change). This distinguishes it from siblings like get_bars which likely handle single tickers.

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 batch queries over multiple tickers for one date, but does not explicitly compare to alternatives like query_daily or get_bars. No when-not-to-use guidance is 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 carries the transparency burden. It discloses daily aggregates, derived metrics (range_pct, true_range_pct capturing volatility), cost, and single ticker scope. Could add that it is read-only and returns a time series of bars, but current disclosure is solid.

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 tightly written sentences with no fluff. Purpose is front-loaded, critical details (cost, derived metrics) follow efficiently. 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?

For a tool with 3 simple parameters and no output schema, the description provides sufficient context: it names the return fields and includes cost. Could explicitly mention it returns a list/array of bars, but the description is largely complete for typical agent use.

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

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

Schema coverage is 100% with descriptions for ticker, start, end. The description adds significant meaning: explains that start/end are dates, ticker is a stock ticker, and crucially describes the output (OHLCV plus VWAP and volatility metrics). This goes well beyond the schema's parameter 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 returns daily OHLCV bars plus VWAP, range_pct, and true_range_pct for one ticker over a date range. It distinguishes from sibling tools like query_minute_bars (intraday) and get_pricing (likely raw pricing) by specifying 'day-level aggregates' and highlighting cost efficiency for long histories.

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 long-term historical analysis with 'cheapest way to cover long histories' and $0.001/year cost. It provides clear context but does not explicitly state when to use versus alternatives like get_bars or query_batch, nor list 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?

Discloses return fields (open/high/low/close, percentage changes, volume, transactions) and cost ($0.025 USDC). No annotations exist, so this provides useful behavioral context. Does not cover error handling or access 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?

Two concise sentences: first states purpose and interval, second details output and cost. No fluff; information 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?

No output schema exists, but description explains return values with field names. Covers enough for a simple data retrieval tool; lacks pagination/limit info but is sufficient for basic use.

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

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

Schema coverage is 100%, so the schema already documents parameters. The description adds minor clarification (interval units, default), but largely repeats schema info. Baseline 3 is appropriate.

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

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

Clear purpose: retrieves intraday minute bars for a single US stock over a full trading day. The interval range (3-240 min) and default are specified, distinguishing it from daily or range tools among siblings.

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

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

No explicit guidance on when to use this vs. alternatives like get_bars or query_range. The description implies it's for intraday data but doesn't state 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?

Without annotations, the description adds value by disclosing that bars carry absolute OHLC plus fractional change from that day's open (reset daily), and pricing is $0.01/day with no day limit. It does not cover read-only nature explicitly but implies 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?

Two sentences, no redundancy, front-loaded with core purpose and key constraints. 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?

Although no output schema is provided, the description adequately explains return values (bars with OHLC and fractional change) and pricing limits. Could mention rate limits or pagination, but for a simple query tool it is fairly complete.

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

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

Schema coverage is 100% with good parameter descriptions. The description adds context about interval range (3-240 min) and default value (3m), but does not significantly enhance understanding beyond what the schema already provides.

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

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

The description clearly states it provides multi-day intraday bars for one ticker with a specified interval range (3-240 min, default 3m). It distinguishes from siblings by specifying 'multi-day' and the unique data fields (fractional change from day's open).

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 historical intraday data over multiple days (no day limit) but does not explicitly contrast with sibling tools like get_bars, query_daily, or query_minute_bars. No when-to-use or alternatives 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 full burden. It discloses that criteria are in percent (5=5%) but outputs are fractional (0.05=5%), which is critical for correct usage. It also notes the cost. However, it does not mention rate limits, result ordering, or behavior when no matches occur.

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 that immediately convey purpose, return fields, and a key format nuance. Every sentence adds value; no wasted words. Front-loaded with the essential 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?

For a tool with 9 parameters and no output schema, the description covers the scanning scope (US stocks), criteria types, returned fields, and input/output format. It mentions cost. Missing details like default limit (50) are not stated, but overall it is sufficiently complete for agent 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 100% with each parameter described. The description adds a note about percent/fractional conversion, which applies globally but does not significantly enhance individual parameter understanding beyond the schema's existing 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 it scans all US stocks on a given date for custom criteria (gaps, volume, change) and lists the returned metrics. This is specific verb+resource+scope, differentiating it from siblings like get_bars (raw data) or list_tickers (symbols only).

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

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

No explicit guidance on when to use this tool versus alternatives. It mentions cost ($0.10 USDC) as a practical note, but does not specify when not to scan or compare with related tools like get_bars or query_daily.

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