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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It mentions that the AUM data is 'approximate' and that data is monthly, which provides some insight, but lacks details on rate limits, authentication needs, 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 moderately concise, with a clear list of provided data and a relevant pricing note. It is front-loaded with the core purpose. However, the pricing information could be considered slightly tangential.

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

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

Given zero input parameters and the presence of an output schema, the description adequately covers the main outputs (AUM, SIP flows, folios, scheme breakdown). It does not repeat the output schema but adds context about data sources and approximation.

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

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

The tool has zero parameters, and the description adds meaning by explaining what the tool returns, which goes beyond the empty schema. According to guidelines, 0 parameters gets a baseline of 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 the tool provides AMFI mutual fund industry data, including specific data points like total AUM, SIP flows, and scheme counts by category. It distinguishes itself from sibling tools by focusing on industry-level aggregate data, which is unique among the listed siblings.

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

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

The description provides context by mentioning that similar data costs $17,500/year from Morningstar Direct, implying this tool offers a free alternative. However, it does not explicitly state when to use this tool versus alternatives, nor provide exclusions.

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

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

No annotations are provided, so the description carries the burden. It mentions the tool returns 'instant sector + stock impact mapping' and lists sectors detected, but does not disclose any behavioral traits like rate limits, authentication needs, or what happens if used outside the designated date.

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 detailed with an example and a list of detected sectors. It is front-loaded with purpose but could be more concise; some sentences like the example could be shortened.

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

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

Given only one parameter and presence of an output schema (as per context), the description covers the use case well, including input format, output concept, and sectors detected. It is sufficient for an agent to understand and invoke the 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 only parameter 'text' has 0% schema description coverage. The description adds a clear semantic explanation: 'Budget speech transcript text (paste directly from live broadcast)', which is more informative than the schema's bare type definition.

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

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

The description clearly states the tool is a 'Real-time budget speech analyzer' and specifies the use case: on Feb 1st as the FM speaks. It differentiates from siblings like 'get_budget_impact' by emphasizing real-time usage and providing an example of input-output.

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

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

The description explicitly states when to use ('on Feb 1st as FM speaks') and what input to provide ('Paste the Finance Minister's speech text'). It does not explicitly state when not to use or list alternatives, but the context is clear enough.

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

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

No annotations are provided, so the description carries the full burden. It describes the analysis and return values but does not explicitly state that the tool is read-only or non-destructive. It also omits details on rate limits, data freshness, or error handling. For an analysis tool, the disclosure is adequate but not thorough.

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

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

The description is well-structured with an intro, input format, and output list. It is reasonably concise but could be trimmed slightly (e.g., the output list could be more compact). Overall, it balances detail and readability.

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

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

Given the tool's complexity and that an output schema exists (though not shown), the description covers key return values. However, it lacks details on error cases, data sources, or time periods for analysis. The presence of sibling tools like 'portfolio_analysis' suggests potential gaps in scope differentiation.

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 0% description coverage, leaving parameters undefined. The description compensates fully by specifying the required fields (symbol, qty, avg_price) and the optional buy_date, along with a concrete example and array format. This adds essential meaning beyond the generic schema.

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

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

The description clearly states it performs 'deep risk + return analysis for your holdings' and lists specific outputs like P&L, XIRR, sector concentration, and risk flags. This makes the tool's purpose specific and actionable. However, it does not explicitly differentiate from the sibling tool 'portfolio_analysis', which may cause 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?

The description lacks explicit guidance on when to use this tool versus alternatives. With many sibling tools, especially 'portfolio_analysis', it does not explain scenarios where one is preferred over the other. No 'use this when' or 'instead of' hints are provided.

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

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

The description explains the strategy logic (cross conditions) and compares to buy-and-hold. Since no annotations exist, it adequately covers behavior. The presence of an output schema reduces the need to describe return values.

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 well-structured with sections (PRO tag, strategy, args, examples) and is informative without being excessively long. Minor redundancy (repeating defaults in examples) could be trimmed.

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 strategy, parameters, and examples adequately. It might benefit from mentioning data prerequisites or output format, but the output schema exists. Overall complete for a backtesting 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?

With 0% schema description coverage, the description fully compensates by listing and explaining all five parameters, including defaults and examples (e.g., symbol, short_window, long_window, period, initial_capital).

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: backtesting a Simple Moving Average (SMA) crossover strategy. It uses specific verbs and resources, distinguishing it from the many sibling tools.

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

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

The description provides clear usage context and examples, showing when to use this tool for SMA crossover backtesting. However, it does not explicitly mention when not to use it or suggest alternative tools.

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

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

No annotations provided. The description implies a read-only data retrieval but does not disclose any behavioral traits such as data latency, required permissions, or limits. It is minimally adequate.

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

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

The description is well-structured with a brief summary, return items listed, an Args section, and examples. Every part serves a purpose with no redundant text.

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 an output schema, the description does not need to detail return values. It provides all necessary information for an agent to invoke the tool correctly, including parameter usage and example calls.

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?

Both parameters are explained with examples and default values. The description adds significant meaning beyond the schema, clarifying that symbol is a stock ticker and quarterly controls annual vs quarterly data.

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 'Get balance sheet for a company' with a specific verb and resource. It lists the items returned and provides examples, distinguishing it from sibling tools like cash_flow and income_statement.

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 is self-contained but fails to mention that this is for balance sheet data only, not for other financial statements or ratios.

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 security-relevant behavior (API key stays in local .env, never committed) and explains it shows status and instructions. However, it does not detail return values or error handling, which might be covered by 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 concise, with a clear front-loaded purpose sentence. The security note and example add value without unnecessary length. It earns its place but could be slightly tighter.

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 an existing output schema, the description adequately covers the tool's functionality and adds security context. It is complete for a simple status check tool, though it could mention output specifics.

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

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

The tool has zero parameters, so the description cannot add parameter semantics. The schema coverage is 100% trivially. The description adds context about the tool's purpose and security, which is valuable 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?

The description clearly states 'Check Angel One SmartAPI integration status and get setup instructions', which is a specific verb-resource pair. It explicitly names 'Angel One', distinguishing it from sibling broker status tools like fyers_status and icici_status.

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

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

The description indicates when to use the tool: to check if Angel One credentials are configured and working, and to get setup instructions. It provides clear context but does not explicitly state when not to use or list alternatives, though the sibling tools suggest other brokers.

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 lists the types of data provided but lacks details on limitations (e.g., only top 1000 companies, data freshness), error handling, or authentication. It implicitly mentions the scope via SEBI mandate but does not disclose behavioral traits like rate limits or response format beyond what is listed.

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

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

The description is front-loaded with the core purpose but includes several paragraphs of background (e.g., comparison to paid sources) that are informative but not essential for tool invocation. The list of disclosures adds redundancy. It could be trimmed to improve conciseness.

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

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

Given the single parameter and existence of output schema, the description sufficiently covers what output to expect, including specific data types like filing links and disclosure categories. It is complete enough for a data retrieval tool, though it does not mention potential errors or empty results.

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

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

The single parameter 'symbol' is described as NSE stock symbol with concrete examples (RELIANCE, TCS), adding meaning beyond the schema which only defines type string. This compensates well for the 0% schema description coverage, though it does not include constraints like uppercase or format.

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

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

The description clearly states the tool provides BRSR ESG data for listed companies, including filing links and disclosures. While it does not explicitly start with a verb, the purpose is unambiguous and specific. It does not explicitly differentiate from siblings but is sufficiently distinct.

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 background on BRSR mandate and free availability but does not explicitly guide when to use this tool versus alternatives. No exclusions, prerequisites, or contextual usage tips are given, leaving the agent without clear selection criteria.

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

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

No annotations provided. Description states it returns real-time data with price, change, volume, etc., but does not disclose side effects, auth needs, or data freshness. Adequate 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?

Two paragraphs with clearly separated args and examples. No fluff. Could be slightly more structured 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 simplicity (1 param) and presence of output schema (context signal), description covers return fields and parameter. Adequate for selection and 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 0% but description adds examples (e.g., RELIANCE, TCS) and explains symbol field meaning, compensating effectively. Parameter is well-illustrated.

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 'Get real-time BSE quote for an Indian stock' with specific verb and resource. It lists return fields and distinguishes from siblings like nse_quote by explicitly naming the exchange.

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 vs alternatives. Does not mention prerequisites, exclusions, or context where it should not be used.

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. It details calculation rules including capital loss carry forward, but doesn't explicitly state it has no side effects. Still sufficiently 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?

Well-structured with overview, rules, args, returns, examples. Front-loaded purpose. Could be slightly more concise, but each section earns its place.

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

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

Complete for the tool's complexity: covers all required and optional parameters, explains return value format, includes examples. Output schema exists but description still provides adequate 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?

Schema has 0% coverage, but description includes an Args section with explanations for all parameters, including optional ones like symbol and asset_type. Provides format, default, and examples, adding significant 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?

Description clearly states it calculates Indian LTCG/STCG tax liability for equity/mutual fund trades, with specific tax rules. Unambiguous and distinct from sibling tools.

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

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

Explicitly indicates applicable tax rules (post July 2024 Budget) and asset types. While it doesn't list alternatives, the specialized nature makes usage 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 must cover behavioral aspects. It omits any mention of data freshness, pagination, rate limits, or error handling. The tool is clearly read-only, but this is not stated explicitly.

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, using bullet points for key return fields and a clear args/examples section. Every sentence adds value, and the structure is front-loaded with the main purpose.

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

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

Given the presence of an output schema (not shown but indicated), the description adequately explains return values. It covers main use cases but lacks details on error scenarios or edge cases like invalid symbols.

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

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

Schema coverage is 0%, but the description explains both parameters thoroughly: symbol with examples of valid tickers, quarterly with explanation of default behavior. This adds significant value beyond the bare schema.

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

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

The description clearly states it retrieves cash flow statements and lists specific components. Examples with different stocks and quarterly toggle clarify usage. It distinguishes itself from siblings like balance_sheet and income_statement implicitly, but does not explicitly contrast with them.

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 cash flow analysis through examples and listed metrics. However, it does not explicitly state when to use or avoid this tool compared to siblings, nor does it mention prerequisites 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, the description discloses that it manually triggers checks, runs automatically normally, and returns the count of updated signals. It does not mention idempotency, side effects, or limitations, but covers the core behavior.

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

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

Three sentences: purpose, context, and output. No fluff, every sentence adds value. Front-loaded with the main 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 no parameters and no annotations, the description covers the essential aspects: what it does, when to use it, and what it returns. The output schema is mentioned as present, but the description already states the return value. Slightly lacking in edge cases or error conditions.

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

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

The input schema has no parameters, so the description does not need to add parameter information. The baseline is 3 due to 100% schema coverage, and the description provides sufficient context about the tool's function.

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 action ('trigger outcome checking') and the resource ('pending signals'). It distinguishes itself from sibling tools by focusing on outcome checking, unlike signal creation or analysis tools.

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

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

The description explains when to use it (force-check when windows have elapsed) and that it is normally automatic, providing context. It does not explicitly mention alternatives, but the use case is 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 provided, so the description carries the full burden. It states return fields but doesn't disclose potential limitations, authentication needs, or whether the data is real-time or delayed. The description is minimal on behavioral aspects beyond the return list.

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?

Concise two sentences for purpose, a clean list of return fields, clear argument section, and relevant examples. No fluff or redundancy; every element adds value.

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

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

For a simple tool with one parameter and an output schema, the description covers the essentials. It lists return fields and gives usage examples. Could mention if the profile is for the latest available data, but not a major gap.

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

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

Schema has one parameter 'symbol' with no description. The description adds semantic meaning by specifying 'Stock ticker (e.g., RELIANCE, TCS, AAPL, MSFT)', including examples of both Indian and US tickers. This compensates for the 0% schema description coverage.

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

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

The description clearly states 'Get company overview and profile information' and lists specific return fields (name, sector, industry, etc.). Examples like company_profile('RELIANCE') show typical usage. Distinguishes from siblings by focusing on profile data rather than financial statements or market 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?

Provides examples but no explicit guidance on when to use versus alternatives like stock_quote, key_ratios, or other sibling tools. No 'when not to use' or alternative suggestions 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?

No annotations provided, so the description carries the burden. It describes a read-only operation returning a comparison table, which is adequate but does not mention any specific behaviors beyond that.

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 well-structured with a header, list of metrics, and examples. Slightly verbose but still concise for the information provided.

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 output fields, parameter usage, and examples. Given the tool's simplicity and the presence of an output schema, it is complete.

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

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

The sole parameter 'symbols' is explained with format (comma-separated, 2-5 stocks) and examples, adding significant meaning beyond the bare schema.

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

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

The description clearly states the tool compares 2-5 stocks side by side and lists specific financial metrics displayed, distinguishing it from other stock-related tools like stock_screener or company_profile.

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

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

The description implies when to use (for side-by-side comparison) and provides examples, but lacks explicit guidance on when not to use 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?

With no annotations, the description carries full burden. It discloses the data source (Finance Ministry, public) and leading indicator behavior, but does not explicitly confirm it is read-only or describe any limitations like data freshness or accuracy.

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 succinct, with no wasted words. It front-loads the core concept, provides an example, and clearly separates sections for sectors and args.

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 an output schema (so return values need not be described) and only one parameter, the description covers the tool's purpose, usage, and sector scope adequately. Minor gaps include lack of detail on the prediction methodology or confidence intervals.

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 parameter 'sector' lacks schema descriptions (0% coverage), but the description compensates by listing valid sector values (Auto, FMCG, etc.) and explaining the 'all' option for a full report. This adds significant meaning beyond the schema's default.

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

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

The description clearly states the tool correlates GST data to sector performance as a leading indicator, with a concrete example ('GST from auto sector up 24% YoY → MSIL/Bajaj Auto historically follow in 2-3 months'). It distinguishes itself from sibling tools like sector_performance by specifying its predictive nature.

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

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

The description explains when to use the tool (for leading indicators based on GST data) and lists covered sectors. However, it does not explicitly state when not to use it or mention alternative tools, leaving some ambiguity.

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 mentions data source (SEBI filings) and output fields, but omits details on data freshness, rate limits, error handling, or confirmation that it's read-only. 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?

The description is well-structured with a clear purpose, context, output list, args, and examples. It front-loads the core function. Some marketing language about Bloomberg pricing is non-essential but not harmful. Efficient overall.

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

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

For a single-parameter tool with an output schema, the description covers the main use case and output fields. It lacks notes on error cases or data limits, but the presence of an output schema reduces the burden. 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 0%, but the description fully explains the 'symbol' parameter with examples (RELIANCE, TATAMOTORS, ADANIENT) and context (NSE stock symbol). This adds significant meaning beyond the schema.

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

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

The description clearly states the tool retrieves credit ratings for Indian listed companies from SEBI-mandated filings. It specifies the resource (credit ratings), context (Indian, SEBI), and distinguishes from sibling tools that deal with other financial data.

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

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

The description implies when to use this tool (need credit ratings, especially free alternative to Bloomberg) but does not explicitly contrast with siblings like company_profile or nse_quote that might also offer rating data. It provides clear context but lacks direct 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 full burden for behavioral disclosure. It fails to mention any traits like rate limits, data source, error handling, or read-only nature. The agent gains no insight beyond the basic functionality.

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 a single line plus a bulleted list of args and two examples. Every sentence serves a purpose, and the structure is front-loaded with 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 the tool has an output schema, return values need not be explained. However, the description omits context like timezone, data source, or whether data is adjusted for splits/dividends. It is minimally complete for a simple historical data tool but leaves gaps.

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

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

The input schema has 0% description coverage, so the description must compensate. It explicitly lists the three parameters (symbol, period, interval) and provides acceptable values and examples, which adds meaning beyond the schema's empty fields.

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

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

The description clearly states 'Get historical cryptocurrency price data' with a specific verb and resource, and the examples further clarify the tool's purpose. It effectively distinguishes from siblings like crypto_price (current price) and stock_historical (stock data).

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

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

No guidance is provided on when to use this tool versus alternatives such as crypto_price for live prices or stock_historical for stocks. The description does not mention scenarios or exclusions, leaving the agent to infer usage from the name 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 full burden. It clearly states it returns live price, 24h change, market cap, volume, and all-time high. It implies a read-only, real-time operation. However, it does not disclose potential rate limits, data freshness guarantees, or authentication needs, which would elevate it to a 5.

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: one sentence for purpose, one line for returned fields, and a brief args section with examples. It is front-loaded with key information. However, the three example lines could be condensed into a single sentence, making it slightly tighter.

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 a single parameter and an output schema (mentioned in context signals but not displayed), the description adequately covers the use case. It lists key return fields and gives usage examples. It is sufficient for an agent to invoke correctly.

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

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

The input schema has a single required string parameter 'symbol' with 0% description coverage. The description compensates well by explaining it is a crypto ticker and providing explicit examples (BTC, ETH, SOL, etc.), clarifying the expected format. This adds meaning beyond the schema alone.

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 'Get live cryptocurrency price in USD' with a specific verb and resource. The list of returned fields (price, 24h change, market cap, etc.) and the examples differentiate it from sibling tools like crypto_historical or 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 provides examples of typical symbols (BTC, ETH, SOL) but does not explicitly state when to use this tool versus alternatives (e.g., crypto_historical for historical data). Usage is implied through context, but no direct guidance on when-not-to-use 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?

The description lists criteria scanned (volume spike, price surge, etc.), expected output (pump_probability, red_flags, verdict+recommendation), and includes a realistic example. This fully compensates for the lack of annotations.

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 well-structured with clear sections: purpose, scan criteria, example, returns, and args. It is front-loaded with the key action. However, the inclusion of a quote and example adds some verbosity that could be trimmed for conciseness.

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

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

For a single-parameter tool with an output schema (implied by 'Returns'), the description covers input, analysis logic, and output fields thoroughly. No critical gaps remain given the tool's complexity.

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

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

With 0% schema description coverage, the description adds value by specifying 'NSE symbol (most useful for small/micro caps)', providing context beyond the schema's bare 'string' type. It could further specify format (e.g., 'RELIANCE') but is adequate.

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

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

The description clearly states 'Detect pump-and-dump operator activity in an NSE stock' which is a specific verb+resource. It uniquely identifies the tool's purpose among many sibling tools, as no other tool explicitly targets pump-and-dump detection.

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 guidance on when the tool is most applicable by noting 'most useful for small/micro caps' in the args section. However, it does not explicitly compare to siblings or state when not to use it.

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

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

No annotations provided, so description bears full burden. It details the four signals and output format (alert level, verdict, alerts, findings), making behavior transparent. Does not mention permissions or side effects, but as a read-only monitoring tool this is acceptable.

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?

Well-structured with bullet points listing the four signals, an example output, and clear sections for args and returns. Every sentence adds value; no fluff.

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

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

Given the presence of an output schema (not shown), the description adequately explains return fields. The tool is standalone and the description covers all essential aspects: inputs, signals, output, and example.

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. The description adds examples (e.g., RELIANCE, HDFC) and clarifies it is an NSE stock symbol, compensating for schema lack.

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 detects smart money and unusual activity for NSE stocks by scanning four specific signals. It is distinct from sibling tools like nse_insider_trading or nse_bulk_deals which cover only individual aspects.

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

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

The description implies when to use (to detect unusual activity) but does not explicitly state when not to use or mention alternative tools. However, it provides clear context for its composite nature.

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

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

With no annotations, the description must disclose behavioral traits. It lists return values but lacks information on error handling, data coverage (e.g., all historical periods), or any prerequisites. Minimal extra context 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 concise and well-structured: a summary sentence, bullet-style return info, 'Args' section, and examples. No redundancy or fluff.

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

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

Given one required parameter and an output schema, the description covers the tool's function adequately. It explains what is returned and provides examples. However, missing details like error responses or historical depth slightly reduce completeness.

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

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

The single parameter 'symbol' is described as a stock ticker with examples (ITC, COALINDIA, AAPL, MSFT), adding significant meaning beyond the bare schema which has no property description. Schema coverage is 0%, so the description compensates well.

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: 'Get historical dividend payments for a stock.' It specifies the returned data (dates, amounts, totals, averages, latest) and differentiates from sibling 'dividend_history_deep' by implying this is the basic 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?

The description does not provide guidance on when to use this tool versus alternatives like 'dividend_history_deep' or other stock data tools. Examples are given but no explicit when-to-use or when-not-to-use criteria.

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

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

Discloses data source (yfinance), output structure (up to 60 entries, annual totals, trailing yield), and examples. No annotations provided, so description carries full burden; could mention rate limits or error handling but sufficient.

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

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

Well-structured with sections (overview, benefits, output list, args, examples). Every sentence adds value, no fluff.

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

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

Describes all key outputs (full history, annual totals, trailing yield, total dividends) and includes examples. Output schema exists but description is still thorough.

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 parameter 'symbol' is explained with examples (NSE or global stocks like RELIANCE, TCS, AAPL). Schema has 0% description coverage, but description fully compensates.

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 provides 10-year dividend history with yield calculation and annual summary, listing specific outputs. It distinguishes from sibling 'dividend_history' by being 'deep' and mentioning data sources.

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

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

Explicitly contrasts with paid alternatives (Bloomberg/FactSet) and mentions free source (yfinance). Provides usage examples and context for when to use this tool (deep history vs basic).

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

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

Discloses that it returns earnings dates and analyst estimates (EPS and revenue). However, with no annotations, it does not mention any behavioral traits such as data source, rate limits, or scope (e.g., which exchanges). Adequate but could add 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?

Concisely structured with two paragraphs plus Args and Examples sections. The examples are helpful but slightly verbose; otherwise no wasted text.

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 that an output schema exists, the description need not detail return values. It adequately explains the purpose and primary output (earnings dates and estimates) for a simple lookup 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 provides 0% description coverage for the 'symbol' parameter. The description compensates by stating it is a stock ticker with examples (RELIANCE, AAPL) and noting it is required, adding significant 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 uses a specific verb ('Get') and resource ('upcoming earnings dates') and clearly distinguishes from sibling tools like predict_earnings and nse_quarterly_results by focusing on expected upcoming reports.

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 examples of usage but no explicit when-to-use or when-not-to-use guidance relative to sibling tools. Implies usage from examples but does not exclude alternatives like predict_earnings for forward-looking estimates.

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

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

The description discloses that this is not a claim of the full live system's accuracy and is meant as a defensible evaluation layer. However, with no annotations, it lacks details on safety (e.g., read-only nature), auth needs, or rate limits.

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

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

The description is short and front-loaded with purpose, followed by important caveats and parameter list. No fluff, but the structure could be tighter with 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?

Given the tool's simplicity (3 parameters, one required) and existence of an output schema, the description provides moderate completeness. It covers limitations but omits behavior like whether the tool modifies state or requires specific permissions.

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 briefly explains lookback_months as 'historical window for checkpoints' and holding_days as 'forward return horizon for hit evaluation', but symbol is merely 'NSE symbol'. The semantics are minimal and could be more detailed.

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

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

The description clearly states the tool evaluates signal quality, serving as a lightweight evaluation/proof layer for the signal engine's price-action core. It distinguishes itself by emphasizing honesty and defensibility, but could better differentiate from siblings like get_signal_accuracy.

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 merely mentions it is an honesty tool and a proof layer, leaving usage context to inference. Given the many sibling tools, this is a significant 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?

No annotations are provided, so the description carries full burden. It does not disclose any behavioral traits such as read-only nature, permissions, or side effects beyond stating what it returns.

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?

A single sentence that is efficient and front-loaded, containing no filler or redundant information.

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

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

Given zero parameters and the presence of an output schema, the description is complete enough to understand the tool's purpose and usage.

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

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

There are zero parameters, so baseline 4 applies. The description does not need to add parameter info.

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

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

The description clearly states the tool returns basic server metadata and useful links, specifying both verb and resource. However, 'basic server metadata' is somewhat vague and could benefit from more specificity, and it does not differentiate from 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 usage context, exclusions, or alternatives are mentioned. With many sibling tools, this lack of guidance increases risk of incorrect selection.

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

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

The description lists return fields (current rate, change, high/low, 52-week range) but does not disclose that it is a read-only operation, nor mention rate limits, error conditions, or data frequency. Since no annotations exist, the description carries the full burden but falls short of comprehensive disclosure.

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

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

The description is concise and well-structured: a one-line purpose, a list of return fields, an Args section, and five examples. Every sentence adds value, and the front-loading makes it easy to scan.

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

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

Given the simplicity of the tool and the presence of an output schema, the description covers essential aspects. It lists return fields and provides usage examples. However, it lacks error handling details (e.g., invalid currency codes) and data source information.

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

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

The description provides clear parameter semantics in the Args section (e.g., source and target currency codes with examples) and lists a default for to_currency. This compensates for the schema's lack of descriptions (0% coverage). Additional validation hints (like valid codes) could improve it.

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: 'Get live forex exchange rate between two currencies.' It uses a specific verb and resource, and differentiates from sibling tools like stock_quote or crypto_price by focusing on forex.

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 you need forex rates), but does not explicitly state when not to use it or suggest alternative tools. No guidance on prerequisites 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 carries full responsibility for behavioral disclosure. It only states the tool returns historical OHLCV candles, but omits critical details like rate limits, authentication requirements, data freshness, error handling, or the return format. This is insufficient for a data retrieval tool.

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

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

The description is reasonably concise, using a docstring format with a clear block for arguments. The list of interval options is necessary but slightly lengthy. Overall, every sentence adds value without redundancy.

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

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

Despite having an output schema (context signal), the description does not explain the return format or any data structure. It lacks details on date ranges, pagination, or potential limits. For a financial data tool with no annotations, the description is too sparse to be fully actionable.

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

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

The schema has 0% description coverage, so the description must compensate. It does so by specifying that 'symbol' is an NSE symbol with an example (RELIANCE), listing all interval options, and clarifying 'days' as 'Number of days of history'. This adds meaningful semantic value beyond the bare schema.

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

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

The description clearly states the tool fetches historical OHLCV candles from the Fyers API v3, and lists the interval options and parameters. The name 'fyers_candles' distinguishes it from other candle tools like icici_candles or nse_historical, 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 provides no guidance on when to use this tool versus alternatives, such as icici_candles or stock_historical. It does not mention prerequisites, limitations, or exclusions. The user is left to infer usage context from the tool name 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?

With no annotations, the description carries the burden. It discloses 'zero delay when configured' as a behavioral trait, lists returned fields, and implies a read-only operation. However, it doesn't mention error behavior or data freshness guarantees beyond zero delay.

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 a purpose sentence, output list, setup instructions, and parameter details. The setup section is slightly detailed but may be extraneous if the environment is already configured. Overall, no wasted sentences.

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

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

Given an output schema exists, the description covers main return fields and setup. It lacks information on error handling, rate limits, or symbol validation, but for a simple live quote tool, it is mostly complete.

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

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

The input schema has 0% description coverage, but the description provides parameter meaning: 'symbol: NSE symbol (e.g. RELIANCE, TCS, INFY)'. This adds clarity beyond the schema's type-only definition, though it could include more examples or accepted formats.

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 'real-time NSE stock quote via Fyers API v3' with specific output fields (LTP, O/H/L/C, volume, change%, 52W high/low). It distinguishes from siblings by specifying the Fyers API source, which sets it apart from other live quote tools like nse_quote or 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 NSE stocks via Fyers but does not explicitly state when to use this tool versus alternatives (e.g., nse_quote, bse_quote). No guidance on when not to use or prerequisites beyond setup credentials.

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, and the description does not disclose behavioral traits such as whether the operation is read-only, requires authentication, or has any side effects. 'Check' suggests a read operation, but this is not explicit.

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 of nine words, front-loaded with the key action and resource. 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?

The description is minimal but adequate given zero parameters and the presence of an output schema. However, it lacks detail on what the status includes (e.g., connection status, API enabled) and no annotations are provided.

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 meaning by explaining the tool's purpose, meeting the baseline of 4 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?

Description clearly states the tool checks Fyers API v3 configuration status and setup instructions. It uses a specific verb ('check') and resource ('Fyers API v3 configuration status and setup instructions'), distinguishing it from sibling tools like fyers_candles, fyers_live_quote, and broker_setup_status.

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

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

No explicit guidance on when to use this tool versus alternatives such as broker_setup_status or icici_status. The description implies usage for Fyers status but does not state when not to use it or provide context for selection.

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

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

No annotations are provided, so the description must disclose behavioral traits. It describes the output (flags unusual resolutions) but does not mention side effects, data sources, or limitations. The tool appears read-only, but this is not explicit.

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 structured with a summary, bullet points of flags, a contextual quote, and an Args section. While informative, it is somewhat lengthy; the bullet points and quote could be condensed without losing meaning.

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

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

The tool has an output schema (not shown but presence noted), so return values are covered. The description explains purpose and flags but lacks usage guidelines and behavioral transparency. For a tool with one parameter, it is fairly complete 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?

The input schema has one required parameter 'symbol' with no description. The description adds examples (e.g., RELIANCE, ZEEL, ADANIENT) and context (NSE symbol), providing meaning beyond the schema. Schema coverage is 0%, so the description compensates well.

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 an 'AI briefing for upcoming AGM/EGM resolutions from NSE filings' and lists specific flags (e.g., large debt issuance, management salary hikes). This distinguishes it from sibling tools like get_stock_brief or get_morning_brief, which cover different scopes.

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 pre-AGM/EGM analysis by listing scenarios like 'This company is passing a resolution to raise ₹500Cr debt next week.' However, it does not explicitly state when not to use or mention alternatives, though the context is 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 bears the full burden. It describes the output content but does not disclose behavioral traits such as read-only nature, authentication requirements, rate limits, or error handling. This is 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?

The description is concise, front-loading the purpose and then detailing outputs and arguments. Each sentence adds value, and there is no redundant information.

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

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

Given a simple single-parameter tool with an output schema, the description adequately covers what the tool does and its parameter. It does not mention pagination or error handling, but for this use case, it is sufficiently complete.

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

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

The sole parameter 'year' has a default and is described with examples ('2025', '2024', '2023'), clarifying the expected format. With 0% schema description coverage, this adds necessary context, though it could explicitly state it refers to the fiscal year.

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 'Historical Union Budget impact by year' and lists specific outputs (key announcements, sector winners/losers, market reaction). It distinguishes itself from the sibling 'analyze_budget_live' by focusing on historical data.

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

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

The description implies usage for historical budget data but does not provide explicit guidance on when to use this tool versus alternatives like 'analyze_budget_live' or any exclusions. It lacks when-not-to-use instructions.

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

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

With no annotations, the description bears full responsibility for behavioral disclosure. It details the signal logic, data source (quarterly NSE shareholding), and return fields including divergence_type, signal, confidence, interpretation, historical_implication, and raw data. It does not mention any side effects or rate limits, but as a read-only analytical tool, the description is sufficient.

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

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

The description is organized into clear sections: introduction, signal conditions, example quote, data source, and args/returns. While it includes an illustrative example, it is not overly verbose. It front-loads the main use case effectively.

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

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

Despite having only one parameter, the description covers the tool's purpose, behavior, input format, and output details comprehensively. The presence of an output schema is noted, but the description still explains the return structure, making the tool fully understandable 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 coverage is 0%, so the description must compensate. It explicitly defines the 'symbol' parameter as an NSE symbol and provides examples (HDFCBANK, RELIANCE, TATAMOTORS). This adds meaningful context beyond the schema's type-only definition.

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

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

The description explicitly states it detects 'FII vs retail divergence' and describes it as the 'highest-conviction signal in Indian markets'. It uses a specific verb 'Detect' and clearly distinguishes the resource (FII and retail divergence on stocks). Among siblings, this is unique.

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

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

The description explains when to use this tool: for detecting divergence between FII and retail actions. It provides a concrete example with HDFC Bank and mentions the data source (public NSE shareholding disclosures). However, it does not explicitly state when not to use it or mention alternative tools.

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

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

No annotations provided, so the description carries the full burden. It discloses outputs (action, confidence, strike zone, factors, risk flags) but does not mention data freshness, fallbacks, or any potential side effects. Sufficient but could be more 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?

Five sentences front-loaded with purpose, followed by output details. Generally concise but could tighten phrasing. No unnecessary words, but one or two sentences could be combined.

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

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

Given one optional parameter with default and an output schema, the description fully explains what the tool returns (action, confidence_pct, strike zone, approve_message, factors, risk flags). Appropriate for a decision support tool with limited parameters.

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 default 'NIFTY'. Schema description coverage is 0%, but the tool description explicitly states it works for 'NIFTY / BANKNIFTY', implying the parameter accepts these values. This adds meaning beyond the schema, though it could be more explicit.

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 builds an options setup for NIFTY/BANKNIFTY intraday decisions. It specifies the verb 'build', the resource 'options setup', and the scope (intraday, NIFTY/BANKNIFTY). It lists included indicators and outputs, making it distinct from sibling tools which focus on other data.

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

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

The description implies usage for intraday options decisions on NIFTY/BANKNIFTY. It lacks explicit when-not-to-use or alternative tools, but the context is clear. No explicit exclusions or comparisons to 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 provided; description discloses data source (public SEBI SAST) and output fields but lacks details on read-only nature, error handling, rate limits, or data freshness. The performance claim adds context but is not a behavioral trait.

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

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

The description is mostly concise and front-loaded with the purpose. However, the marketing-like sentence about conviction signal adds some verbosity. Overall 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?

For a tool with one parameter and no annotations, the description adequately explains the output and usage. Missing details like return type or error handling but still sufficient for simple 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?

The single required parameter 'symbol' is explained with a clear description ('NSE symbol (e.g. RELIANCE, INFY, ZEEL)') and examples in the Args section, compensating for the 0% schema description coverage.

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

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

The description clearly states it performs 'Insider trading pattern analysis from SEBI SAST disclosures' and enumerates specific outputs (signal, net_signal, transactions). This distinguishes it from sibling tools like nse_insider_trading and get_signal_history.

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 assessing insider conviction but does not explicitly state when to use this tool versus alternatives. No mention of 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 are provided, so the description carries full burden. It transparently states the data source (public AMFI disclosures), lists supported funds, and describes the output fields. However, it does not disclose potential delays, data freshness, or any limitations (e.g., only Indian funds). Overall, it provides solid 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 appropriately sized and front-loaded with purpose. It includes a useful example and clearly structured argument information. It could be slightly more concise, but overall it is effective and well-organized.

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 that there is an output schema, the description appropriately details return values (overlap_pct, common_stocks, etc.) without redundancy. It covers the main functionality but could mention error handling or unsupported fund cases. Overall, it is sufficiently complete for an analysis 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 input schema only defines two string parameters with no descriptions (0% coverage). The description compensates fully by providing concrete examples (e.g., 'HDFC Flexi Cap') and listing supported fund names, which is critical for correct invocation. This adds significant meaning beyond the bare schema.

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

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

The description clearly states it is a 'Mutual fund overlap analyzer' using public AMFI disclosures, with a specific verb ('analyzes') and resource ('overlap between mutual funds'). It lists supported funds and what it returns, distinguishing it from sibling tools like portfolio_analysis or mutual_fund_nav.

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 an example of when to use the tool (checking diversification with an overlap example), but does not explicitly contrast it with alternative tools or state when not to use it. The usage context is implied but lacks explicit guidance on selection 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?

The description discloses the output format (structured JSON + morning_text) and lists the data components, but does not explicitly state read-only behavior, authentication needs, or data freshness. Without annotations, these gaps reduce transparency for an 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 very concise, using a clear bullet list to enumerate components, and front-loads the key purpose. No wasted words, and the output format is described efficiently.

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, the data components it includes, and the output format. Given the output schema exists, it need not detail return types. Minor gaps: no mention of data freshness or prerequisites, but largely complete.

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

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

The tool has zero parameters, so the schema coverage is trivially 100%. The description appropriately omits parameter details, and no additional meaning is 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 explicitly states the tool compiles a comprehensive pre-market brief for Indian traders, listing specific components. It is clearly differentiated from sibling tools like get_morning_fno_brief and get_stock_brief by its scope and timing.

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 pre-market briefing at 8:15 AM but does not explicitly state when to use this tool over alternatives or when not to use it. No exclusion criteria or comparisons to other briefs are provided, leaving the agent to infer from the name.

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 explains that the tool aggregates multiple data points into one call, which is transparent about its behavior. It does not mention any side effects or permissions, but for a read-only data retrieval tool, this is adequate.

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

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

The description is concise, front-loaded with the core purpose, and uses a bullet list for clarity. Every sentence adds value 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?

Given the tool has no parameters and a well-defined purpose (morning F&O brief), the description is complete. It covers all major return components, and an output schema exists for additional detail, though not provided here.

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

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

The input schema has no parameters (empty properties), so schema coverage is 100%. The description adds meaning by detailing what the tool returns, fulfilling the baseline expectation for a parameterless tool.

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

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

The description clearly states it is an 'F&O-focused morning brief for NIFTY and BANKNIFTY traders' and enumerates the specific sections it compiles (GIFT Nifty, India VIX, etc.). This distinguishes it from sibling tools like 'get_morning_brief' and 'get_nifty_outlook'.

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 it is intended for morning preparations for F&O trading by listing its contents, but it does not explicitly state when to use this tool versus alternatives or provide exclusions. The context is clear enough for an informed agent.

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

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

With no annotations, the description fully discloses the tool's behavior: it aggregates six specified signals, returns a probability and supporting factors, and provides raw inputs. It does not mention latency or failure conditions, but for a read-only, parameterless tool, the transparency is thorough.

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

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

The description is well-structured with a clear lead sentence, bullet points for signals, and an example. It is slightly verbose in signal explanations but remains focused 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?

Description fully accounts for no parameters, provides complete output schema details (fields and example), and explains all six signals. The tool's behavior is fully specified given its simplicity and available structured information.

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 score is 4. The description does not need to add parameter semantics, and it correctly omits any parameter discussion.

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

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

The description clearly states that the tool computes the probability of Nifty 50 closing up in the next session by aggregating six market signals. This specific verb-resource combination ('get nifty outlook') differentiates it from sibling tools like gift_nifty, nifty_index, or individual signal tools.

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

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

No explicit guidance is provided on when to use this tool versus its many siblings (e.g., gift_nifty, nifty_pcr_trend, get_signal_accuracy). The description does not indicate scenarios where this aggregated outlook is preferred over individual signal analysis.

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

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

No annotations are provided, but the description explains the tool's logic (checks pledge % and QoQ change), defines risk levels, and describes the return fields. It adds context about the margin call risk. However, it does not explicitly state it is a read-only 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 informative with sections for purpose, risk levels, example, and returns. It is slightly verbose but each sentence adds value and the 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?

For a simple tool with one parameter and no annotations, the description covers purpose, parameters, return fields, and risk context. It is complete enough for an agent to invoke correctly, though it lacks mention of error handling or limitations.

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

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

The single parameter 'symbol' is described as 'NSE symbol (e.g. ADANIENT, ZEEL, any stock)', which adds meaning beyond the schema's title. With 0% schema description coverage, the description fully compensates.

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 'Promoter pledge early warning for an NSE stock' and explains it checks pledge percentage and QoQ change velocity, with defined risk levels. This differentiates it from siblings like 'promoter_pledge' (raw data) and 'scan_pledge_risks' (scanning multiple stocks).

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 stock early warning but does not explicitly state when to use this versus siblings like 'promoter_pledge' or 'scan_pledge_risks'. No when-not-to guidance is provided.

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

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

No annotations provided, so description must convey behavior. It adds context about severity classification and historical significance, but lacks details on read-only nature, error handling, or rate limits. The description adds some value beyond structural elements.

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 moderately concise but includes a promotional quote and example that add length without critical information. It could be more streamlined while retaining essential 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?

For a single-parameter tool with an output schema, the description covers purpose and parameter usage. However, it does not describe output format or specify behavior for invalid inputs, leaving some gaps. Generally adequate but not fully comprehensive.

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

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

The input schema only defines the 'sector' parameter with a default. The description expands with examples ('Banking', 'SME/Micro', 'Broking') and clarifies that 'all' provides full report. Since schema coverage is 0%, the description compensates well.

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 fetches SEBI orders and classifies them by severity and sector, with a purpose of early warning. However, it does not explicitly distinguish from sibling tools, though no sibling seems directly analogous.

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 implies use for monitoring regulatory actions but does not mention when not to use or suggest other tools, leaving the agent without clear decision context.

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

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

No annotations provided, so description must cover behavioral traits. It lists what the tool shows but does not disclose data source, update frequency, or any side effects. For a read-only tool, this is adequate but could be more 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?

Description is concise, uses bullet points effectively, and includes example usage. Every sentence adds value with no redundancy.

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

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

With one parameter and output schema present, description adequately covers inputs and outputs. However, it could briefly explain the output structure (e.g., JSON fields) for 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%, but description adds 'NSE symbol' and examples (RELIANCE, HDFCBANK, INFY). This provides essential meaning beyond the schema's type and title.

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 'Sector and peer context for a stock' and lists specific outputs (peer basket, rank, valuation, sector context). This distinguishes it from siblings like sector_performance (overall sector) and compare_stocks_tool (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?

Description implies usage for obtaining peer and sector context, but does not explicitly state when to avoid or provide alternatives. Context is clear but lacks explicit guidance on 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?

Explains how signals are logged and checked after 7 days. Discloses return fields (accuracy %, avg return, breakdown). No annotations provided, so description carries burden well.

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?

Well-structured: purpose first, then mechanism, use cases, args, returns. Every sentence adds value, no redundancy.

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

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

Covers purpose, mechanism, use cases, param details, and return values. Output schema exists, so return details are sufficient. All aspects addressed for an analysis 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?

Adds meaning to all 3 parameters (source filters with allowed values, symbol as NSE, days as look-back). Schema has 0% coverage, so description compensates effectively.

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

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

Clearly states the tool shows accuracy of FinStack signals backed by real outcome data. Differentiates from siblings like get_signal_history by focusing on accuracy metrics.

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 explicit use cases (prove accuracy, compare sources, find best stocks). Lacks explicit when-not-to-use or alternatives, but context is clear.

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

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

The verb 'view' implies read-only, but without annotations, the description does not explicitly state non-destructive behavior or potential side effects like rate limits or data latency.

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?

Well-structured with a brief purpose, row format description, usage advice, and arg list. A minor improvement could be merging the first two sentences for conciseness.

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

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

With an output schema present, the description adequately covers tool behavior, argument details, and usage context. No gaps for the given complexity.

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

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

Despite 0% schema description coverage, the description fully compensates by explaining both parameters: symbol (NSE symbol or empty string for all) and limit (default 20, max 100), adding 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?

Clearly states it retrieves recent signals with actual outcomes, distinguishing from sibling tools like check_signal_outcomes and get_signal_accuracy by focusing on logged history and outcome labels.

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 explicit use cases: audit the model, build trust, export. However, it does not mention when not to use this tool or explicitly name 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 provided; description explains scraping, classification, signal generation, and fallback behavior. Does not discuss rate limits or potential errors, but covers main behavioral aspects.

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

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

Well-structured with front-loaded summary, but includes lengthy setup instructions; could be slightly more concise.

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

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

Covers input, output, fallback, and setup. Lacks details on error handling or invalid inputs, but overall sufficient for the tool's complexity.

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

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

Schema coverage is 0%, but description explains symbol as NSE stock symbol and limit with default 100 and max 200, adding significant value beyond 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 analyzes social media sentiment for NSE stocks using Reddit and Twitter, specifies subreddits and fallback, and distinguishes from siblings like market_news.

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 sentiment analysis but lacks explicit when-to-use alternatives. Mentions fallback to yFinance if social APIs not configured, providing context.

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

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

With no annotations, the description fully discloses the behavior: it runs 6 expert personas, returns consensus and debate data, and uses Indian market data. It does not mention rate limits or auth, but the read-only nature is clear.

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 well-structured with bullet points for agents and clear Args/Returns sections. It is somewhat lengthy but every sentence adds value. Front-loaded with the core concept.

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 output schema exists, the description adequately covers the return structure. It lacks details on error handling or edge cases, but for a simple parameter tool, it is sufficiently complete.

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

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

Schema coverage is 0%, but the description explicitly explains the single parameter 'symbol' with examples (e.g., RELIANCE, TCS, HDFCBANK), adding full meaning beyond the schema.

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

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

The description clearly states it is a multi-agent AI stock brief with 6 personas debating BUY, HOLD, or SELL, using real Indian market data. This distinctively separates it from siblings like get_stock_debate and 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 use when a consensus from multiple perspectives is desired, but does not explicitly state when to use this tool versus alternatives like get_stock_debate or get_nifty_outlook. No when-not conditions or alternatives are mentioned.

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

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

Describes the full 3-round process, agent interactions, and all return fields (rounds, debate_edges, minds_changed, final_consensus). No annotations, but description carries the burden well, though it omits prerequisites or rate limits.

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

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

Well-structured with bullet points and section headers. Front-loaded with purpose. Every sentence adds value, no fluff. Appropriate length for the complexity.

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?

Covers all major aspects: rounds, influence graph, consensus. Mentions visualization use case. Lacks error details or preconditions, but for a debate tool this is sufficient.

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 'symbol' has 0% schema description coverage, but the description adds examples and clarifies it's an NSE symbol. Adds some value beyond the schema, but not extensive detail.

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 '3-round sequential debate' and distinguishes from sibling get_stock_brief. Uses specific verb 'runs a live debate' and describes resource (stock symbol) with detailed round mechanics.

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 contrasts with get_stock_brief (parallel analysis) and advises watching for minds_changed to identify complex setups. Could be more explicit about when not to use, but provides clear context.

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

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

No annotations provided. The description only lists components and outputs, lacking disclosure of behavioral traits like read-only nature, permissions, rate limits, or any side effects. As a data retrieval tool, it is likely read-only, but this is not 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?

Description is a bullet list, front-loaded with the main result. It includes detailed component names, which could be seen as slightly verbose but still reasonably concise. Every element 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?

Output schema exists (not shown) so return values are covered. Description lists return fields adequately for a scoring tool. However, it lacks usage context and does not guide the agent on when this composite score is appropriate compared to other 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?

Only one parameter 'symbol' with no schema description (0% coverage). However, the meaning is obvious (stock ticker). The description does not add further meaning, but for a single, clear parameter, this is acceptable.

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 a stock ranking score (0-100) with signal, automation_rank, top supports/risks, and full component breakdown. It differentiates from sibling tools like get_stock_brief or technical_indicators by being a composite score from multiple signals.

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

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

No guidance on when to use this tool versus alternatives. Among siblings, there are many analysis tools (check_signal_outcomes, evaluate_signal_quality, get_signal_accuracy) but the description provides no context for selection.

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

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

No annotations exist, and the description does not disclose behavioral traits such as data freshness, pagination, rate limits, or error handling. While it is a read operation, the description fails to add context beyond listing event categories.

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: a one-line purpose statement, a one-line usage hint, and a two-line Args section. No redundant or extraneous words, earning its place efficiently.

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?

An output schema exists, so missing return value details are excusable. However, the description lacks specifics on event ordering, date range scope, or the precise type of events included beyond the listed categories. It is adequate but incomplete for a timeline 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 description includes an explicit Args block explaining 'symbol: NSE symbol (e.g. RELIANCE, HDFCBANK, TCS)' and 'max_events: max timeline events to return', adding meaning beyond the schema's raw property names and types. The default for max_events is noted in the schema but clarified in context.

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 'Unified stock timeline: news, results, insider, bulk deals, sentiment, pledge, smart money' and identifies it as the 'what changed recently?' tool for a stock. This verb+resource combination distinguishes it from sibling tools.

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

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

The description implies usage for recent changes but offers no explicit guidance on when to use this tool versus alternatives like nse_quarterly_results or nse_insider_trading. No direct exclusion criteria or when-not-to-use are provided.

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

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

No annotations are provided, so the description carries the full burden. It indicates the tool is a comparison (read-only) operation and mentions live tracking as an enhancement, but does not disclose potential side effects, authentication requirements for the base database, or data freshness. The behavioral traits are adequate but not fully transparent.

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

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

The description includes a front-loaded purpose and inline argument documentation, but contains a verbose tweet-like quote ('I tracked 50 Indian stock tip channels for 30 days...') that adds little value and increases length. While efficient in parts, the fluff reduces conciseness.

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

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

Given the tool's simplicity (one optional parameter, no required params, output schema provided), the description covers the key usage scenarios, parameter behavior, and optional live tracking setup. It lacks error handling details or data freshness notes, but is largely complete for an agent to invoke correctly.

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

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

Schema description coverage is 0%, and the description fully compensates by explaining the only parameter 'channel': it specifies the format (handle with @, e.g., '@NSEBSEtips') and the behavior when left empty (full comparison database). This adds complete meaning beyond the schema's default and 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 identifies the tool as a Telegram signal tracker that compares 50 Indian stock tip channels by accuracy, average return, and pump-and-dump probability. This verb+resource definition is specific and distinguishes it from sibling tools like 'get_signal_accuracy' or 'detect_pump', which focus on different aspects.

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 comparing Telegram stock tips and provides optional live tracking setup, but lacks explicit guidance on when to use this tool versus alternatives. It does not mention when not to use it or scenarios where another tool would be better, such as for individual signal analysis.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool is free and provides links to live sources, but does not discuss rate limits, data freshness, or any constraints. It also does not describe what happens if data is unavailable. Adequate but leaves important behavioral traits undisclosed.

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 well-structured with a summary sentence, a note about cost, a bullet list of outputs, and an example. It is somewhat lengthy but every sentence adds value. Could be slightly more concise, but overall effective.

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 the presence of an output schema, the description covers all necessary aspects: what data is returned (Nifty close/change, global indices, status, links) and the context (pre-market, free). It is complete for a data retrieval tool with no inputs.