Indian NSE Stock Insights

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

With no annotations, the description carries the behavioral disclosure. It clearly tells what the tool computes (quote, indicators, trend, return, pattern) and the constraint of 2-5 symbols. This is substantial behavioral context, though it stops short of clarifying data source freshness or any implicit read-only nature. Still, it provides meaningful information beyond the tool name.

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 entire description is one concise sentence, front-loading the core purpose and then listing key features. No redundant or filler words exist. Every phrase adds value.

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

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

The description covers the tool's purpose, input constraints, and key output metrics, which is sufficient for basic selection and invocation. The output schema likely details return structure, so it is not missing that. However, the timeframe parameter is left ambiguous, and there is no mention of edge cases (e.g., invalid symbol count), so it is not fully complete for a complex comparison 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 0% description coverage. The description adds meaning to the 'symbols' parameter by specifying '2-5 symbols,' which gives a range and validates the expected input. However, the 'timeframe' parameter is not mentioned at all, leaving its allowed values and effect unexplained. Thus the description only partially compensates for the missing schema descriptions.

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

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

The description begins with 'Side-by-side comparison of 2-5 symbols,' which is a specific verb and resource, and it lists concrete included metrics (quote, RSI/MACD/ADX, trend, 30d return, latest pattern). This clearly distinguishes it from sibling tools like get_stock_quote (single symbol) or get_technical_indicators (likely one symbol and not a comparison).

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

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

The description implies the use case: when you need to compare multiple symbols together. It states the symbol count limit (2-5), which helps an agent decide if this tool is appropriate. However, it does not explicitly mention alternatives or conditions when not to use it, leaving a slight gap.

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 for behavioral disclosure. It does add an ordering trait ('newest first') and clarifies the pattern type ('single-bar'), but it omits whether the operation is read-only, any side effects, rate limits, or other operational constraints. The agent cannot infer the safety profile.

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

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

The description is a single, well-structured sentence that gets straight to the point, including examples and ordering. It is efficient and front-loaded, though it sacrifices completeness for brevity.

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

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

The tool has three parameters, no annotations, and a closely related sibling (get_chart_patterns). The description is insufficient because it does not explain how to control recency via limit/timeframe or when to choose this tool over the sibling, despite the presence of an output schema.

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

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

Schema description coverage is 0%, and the description provides no information about the parameters (symbol, limit, timeframe). It does not even hint that parameters exist, leaving the agent to rely solely on the schema for their meaning.

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

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

The description clearly states what the tool returns: recent single-bar candlestick patterns identified by names like HAMMER, DOJI, and ENGULFING. It distinguishes from the sibling get_chart_patterns by emphasizing 'single-bar', though it lacks an explicit action verb like 'get' or 'list'.

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

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

No explicit guidance is given on when to use this tool versus alternatives. The qualifier 'single-bar' indirectly implies a use case, but it does not mention that get_chart_patterns might be for multi-bar patterns or provide any other exclusions or context.

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

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

With no annotations provided, the description must fully disclose behavioral traits. It adds useful context by explaining that 'ACTIVE' encompasses PENDING/CONFIRMED states, which clarifies the status parameter. However, it does not describe what the tool returns (relying on the output schema) or any limitations, making the transparency only partial.

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 only two sentences that both carry meaningful information. It front-loads the core purpose and then adds a key clarification about the status value. There is no fluff or redundancy, making it well-structured 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?

The tool is relatively simple and has an output schema, which reduces the need to explain return values. However, the description leaves gaps about valid parameter values (e.g., timeframe, status options) and lacks any guidance on invocation context. It is adequate for basic usage but not fully complete for an agent to handle all edge 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 description coverage is 0%, so the description must compensate. It only explains the meaning of 'ACTIVE' for the status parameter but leaves 'symbol' and 'timeframe' undefined. While symbol and timeframe might be intuitive, the description does not clarify allowed values, formats, or defaults beyond the schema, which is insufficient for a tool with zero 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 retrieves multi-bar chart patterns with specific examples (DOUBLE_TOP, HEAD_AND_SHOULDERS). The verb 'get' is implied by the tool name, and the term 'Multi-bar chart patterns' distinguishes it from related tools like candlestick patterns, though it does not explicitly contrast with 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 is provided on when to use this tool versus alternatives such as get_candlestick_patterns or get_support_resistance. The only hint is the mention of 'multi-bar' patterns, but there is no clear exclusionary or comparative language, leaving the usage context underdefined.

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 of behavioral disclosure. It only states that the data is split into demand_zones and supply_zones, but does not explain the meaning of DBR/RBR/RBD/DBD, side effects (though likely read-only), or any filtering behavior beyond what defaults imply.

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 concise sentence with no redundant phrases. It front-loads the key concept and adds the output structure, though the unexplained acronyms slightly reduce 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?

Although an output schema exists, the description is too sparse to be considered complete for an agent. It does not explain the domain terminology, parameter selection, or relationship to sibling tools, leaving significant gaps for a three-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 description coverage is 0%, so the description must compensate for the three parameters, but it mentions none of them. The text does not clarify the roles of symbol, status, or timeframe, leaving the agent to infer or rely on conventions.

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 identifies the resource as demand/supply zones and clarifies the result is split into demand_zones and supply_zones, which goes beyond the tool name. It distinguishes from sibling tools like get_support_resistance by referencing DBR/RBR/RBD/DBD, though it lacks an explicit verb like 'returns' or 'calculates'.

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?

There is no guidance on when to use this tool versus alternatives such as get_support_resistance or get_chart_patterns. No prerequisites, context, or exclusions are mentioned, so the description does not help an agent choose among related technical-analysis tools.

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

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

With no annotations, the description carries full responsibility for explaining behavior. It discloses that the tool returns the latest swings in both directions with specific retracement levels, but does not mention potential limitations, such as how swings are determined, data freshness, or any prerequisites (e.g., valid symbol format). The description adds context beyond the name but lacks deeper operational detail.

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, tightly packed sentence: 'Latest fibonacci retracement swings (UP/DOWN) with 236/382/500/618/786 levels.' Every word conveys meaningful information, and it is front-loaded with the core purpose. It achieves high clarity with minimal 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?

For a relatively simple data retrieval tool with an output schema present, the description covers the essential 'what' (Fibonacci levels) and key attributes. It does not explain output structure, but the output schema can cover that. The only gap is lack of usage context such as prerequisites or whether the symbol/timeframe apply to both UP/DOWN swings, but overall it is sufficiently complete given the tool's simplicity.

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 does not compensate by explaining how to use 'symbol' or 'timeframe'. The parameter names are self-explanatory, but no guidance is given on valid values, formats, or the effect of the default timeframe. The description provides no added meaning beyond the schema's property names.

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 'Latest fibonacci retracement swings (UP/DOWN) with 236/382/500/618/786 levels,' specifying both the resource (Fibonacci retracement) and key attributes (direction and ratios). This distinctly differentiates it from sibling tools like support/resistance or candlestick patterns, making the purpose unambiguous.

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

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

The description implies usage when Fibonacci retracement levels are needed, but it offers no explicit guidance on when to use this tool over alternatives or when not to use it. There are no direct comparisons to sibling tools, so the agent must infer applicability from the title and description alone.

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

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

No annotations are provided, so the description carries the full burden. It discloses the categories of data returned, which is adequate for a read-only, zero-parameter snapshot tool, but it does not mention data freshness, latency, or other behavioral characteristics. It is not misleading.

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, information-dense sentence with a clear structure: a label and a comma-separated list of content categories. Every word earns its place, with no redundancy.

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

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

Given the tool has no parameters, an output schema exists, and the purpose is a straightforward read-only market overview, the description fully covers the surface area. Usage guidance would be nice but is not essential for completeness in this simple case.

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%. The description adds no parameter-level detail, but none is needed; the baseline of 4 applies because no parameter ambiguity exists.

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 'Market-wide snapshot: indices, top_gainers, top_losers, most_active, breadth' clearly identifies a market-wide aggregate resource and enumerates its components, distinguishing it from sibling stock-specific tools. It lacks an explicit verb in the description itself, but the name provides 'get' and the scope is specific.

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

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

The description provides no guidance on when to use this tool versus alternatives, no exclusions, and no recommendations. Context from sibling names implies it is for market-level data, but the description itself does not state this.

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 behavioral traits like 'newest first' and 'limit capped at 500', but does not mention rate limits, data delays, or authentication requirements. These are useful 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?

The description is a single concise sentence with semicolons separating key details. It is front-loaded with the core purpose and includes only necessary constraints, with no wasted words.

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

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

For a simple data retrieval tool with an output schema, the description covers the essential context: timeframes, limit cap, and ordering. It omits details like the definition of 'recent' or error behaviors, but these are minor given the tool's simplicity.

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 adds meaning for 'timeframe' (enumerated values) and 'limit' (cap at 500), but does not explain 'symbol' beyond the schema name. The added context is helpful but not comprehensive.

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

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

The description clearly states the tool retrieves recent OHLCV candles, specifying the resource (OHLCV data) and the action (get). It also lists valid timeframes and constraints, distinguishing it from sibling tools like get_technical_indicators or get_stock_quote.

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 fetching recent candle data but does not explicitly state when to use this instead of alternatives. No exclusions or sibling tool references are mentioned, so guidance is limited.

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 of behavioral disclosure. It lists the fields included and indicates a read-only snapshot, which is good. However, it does not mention potential caveats like data delays, market hours, or symbol normalization, leaving some ambiguity for a real-world quote tool.

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

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

The description is a single, tightly packed sentence that conveys all essential elements without fluff. It is front-loaded with the core purpose and uses an em-dash list for fields, making it very readable.

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

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

For a simple one-parameter tool with an output schema, the description is largely sufficient. It states the tool's purpose and return fields. However, the lack of symbol format guidance and any usage caveats, combined with low parameter semantics, leaves a small gap. Still, overall completeness is high given the tool's simplicity.

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

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

The schema has a single 'symbol' parameter with no description, and schema description coverage is 0%. The description only says 'NSE stock', implying the symbol is for NSE, but doesn't specify format (e.g., 'RELIANCE' vs 'RELIANCE.NS') or any validation rules. The description adds minimal 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 uses a specific verb 'snapshot' and identifies the resource as 'NSE stock', listing the exact fields returned (last close, prev close, change %, day H/L, volume). This clearly distinguishes it from siblings like get_ohlc_data (historical data) and get_technical_indicators (indicator calculations).

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 phrase 'Latest price snapshot' implies this tool is for current market data, contrasting with historical or analytical tools. However, there is no explicit statement of when to use this versus alternatives, nor any exclusions. The context is clear but not fully spelled out.

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 disclosure burden. It mentions ordering but not calculation methodology, data windows, or reliability caveats. Missing details on how pivot points and historical levels are derived.

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, front-loaded sentence with no filler. It efficiently conveys the core output and ordering, earning 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?

Despite an output schema, the description lacks usage guidance, parameter detail, and behavioral context. For a tool with two parameters and no annotations, this is insufficient for confident selection and correct 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 coverage is 0%, so the description must explain 'symbol' and 'timeframe'. It omits any explanation of 'timeframe' and only hints at symbol via 'support/resistance levels', leaving parameter semantics 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?

The description clearly states the tool returns pivot points and historical support/resistance levels, ordered by price descending. This distinguishes it from sibling tools like get_demand_supply_zones and get_fibonacci_levels, which target alternative level types.

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

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

No guidance is provided on when to use this tool versus alternatives. It does not mention use cases, exclusions, or relationships to siblings like get_technical_indicators, leaving the agent to infer appropriate scenarios.

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

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

There are no annotations provided, so the description carries the full burden of behavioral disclosure. It only says 'Latest' which implies recency, but it does not disclose return format, pagination, authentication requirements, rate limits, or any side effects. As a 'get' tool, it is likely read-only, but this is 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 extremely concise, but it is a fragment, not a well-structured sentence. It front-loads the key term 'technical indicators' and lists examples, which is efficient, but it omits critical context such as the action and parameters. It is concise but under-specified, so it does not earn a high score.

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

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

Given the tool's complexity (multiple indicator types) and the absence of annotations, the description is insufficient. It does not explain what 'latest' means (e.g., relative to current price or a fixed date), how the timeframe parameter affects results, or what the output structure looks like. Even though an output schema exists, the description remains incomplete for a tool with two parameters and many indicators.

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

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

The input schema has two parameters, 'symbol' and 'timeframe', but the schema includes no descriptions (0% coverage). The tool description does not mention either parameter or explain their purpose, default values, or allowed values. Thus, the description adds no meaning beyond the raw schema, failing to compensate for the lack of schema descriptions.

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

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

The description is a noun phrase 'Latest technical indicators (EMAs, SMAs, ...)' rather than a full sentence with a verb. It clearly identifies the type of data (technical indicators) and lists specific examples, which helps differentiate from sibling tools like candlestick patterns or support/resistance. However, it does not explicitly state the action (e.g., 'get' or 'retrieve') or the resource (e.g., 'for a symbol'), making the purpose somewhat vague.

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

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention any exclusions, prerequisites, or alternative tools. The only hint is the list of indicators, which implies it is for technical analysis, but there is no explicit comparison to other market analysis tools.

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

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

No annotations are provided, so the description must disclose behavioral traits. It lists outputs but does not mention read-only nature, data sources, limitations, or any side effects. The description gives no insight into how results are computed or what the response structure looks like, beyond the output 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 a single line that front-loads the purpose ('Volume diagnostic') and then lists specific outputs in a compact, readable list. Every phrase adds information with no redundancy.

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

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

The tool is relatively simple with one parameter and has an output schema, so the description doesn't need to explain return values. However, it doesn't clarify what 'top 5min hotspots' or 'peak slot' mean, which could be ambiguous to an AI agent. Considering the richness of the output schema, the description is minimally adequate but leaves room for interpretation.

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

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

The schema has one parameter 'symbol' with no description, and schema description coverage is 0%. The description does not mention the parameter at all, relying on the schema's property name. For a trivial parameter this is somewhat acceptable, but 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 defines the tool as a volume diagnostic and lists specific computed outputs: top 5min hotspots, peak slot, OBV 20-day trend, and today/20-day volume ratio. This distinguishes it from sibling tools focused on other chart patterns or indicators.

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 volume analysis but provides no explicit guidance on when to use it vs. alternatives like get_technical_indicators or get_market_overview. There are no exclusion criteria or preferred use cases 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?

With no annotations, the description must fully disclose behavior. It only lists filter keys and a requirement. It does not explain how filters are combined (AND/OR), whether results are returned as a list, or any side effects. This leaves significant behavioral 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?

The description is a single sentence that front-loads the action. It lists the keys efficiently and has no fluff. Every word earns its place, making it highly concise and well-structured.

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

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

Despite having an output schema (not visible), the description lacks critical context about the tool's behavior and filter semantics. It does not explain how multiple filters interact or what the output represents. For a tool with a complex filter object and no schema descriptions, this is 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 schema is generic (an object with additionalProperties), so the description is the only source of parameter meaning. It lists key names but lacks types, formats, or examples. With 0% schema coverage, the description only partially compensates; users would not know if rsi_min is a number or trend is a 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 'Filter the active universe' clearly states the tool's purpose with a specific verb and resource. It distinguishes from sibling tools that focus on individual stock analysis or specific data retrieval, as this is a screening tool for the entire universe.

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 filtering stocks based on criteria but does not explicitly state when to use this tool versus alternatives like get_technical_indicators. The 'At least one required' note gives minimal usage guidance but lacks exclusions or alternative references.

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