MCP Yahoo Finance

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.0.0
Disambiguation4/5
Most tools are clearly distinct, focusing on specific financial data types like cashflow, dividends, or news. However, there is some overlap between get_current_stock_price, get_stock_price_by_date, and get_stock_price_date_range, which could cause confusion about which to use for price queries, though their descriptions help clarify the differences.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern with 'get_' prefix and snake_case, such as get_cashflow and get_historical_stock_prices. This predictability makes it easy for agents to understand and navigate the tool set without naming conflicts or inconsistencies.
Tool Count5/5
With 10 tools, the server is well-scoped for financial data retrieval, covering key aspects like prices, financial statements, news, and recommendations. Each tool serves a distinct purpose without being overly broad or sparse, fitting typical needs in this domain.
Completeness4/5
The tool set provides comprehensive coverage for retrieving financial data, including core elements like prices, statements, and news. A minor gap is the lack of tools for updating or managing data (e.g., no create or delete operations), but this is reasonable for a read-only finance server focused on data access.
Average 2.9/5 across 10 of 10 tools scored. Lowest: 1.8/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure but fails completely. It doesn't indicate whether this is a read-only operation, whether it requires authentication, what format the dates are returned in, whether there are rate limits, or what happens with invalid symbols. The description provides zero behavioral context beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
While technically concise with just three words, this is under-specification rather than effective conciseness. The description doesn't front-load important information and fails to provide the minimal necessary context for understanding the tool's purpose. Every word should earn its place, but here the words don't provide enough value to justify their inclusion.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that this is a financial data tool with 2 parameters, no annotations, and no output schema, the description is woefully incomplete. It doesn't explain what 'earning dates' means (earnings announcement dates, reporting dates, etc.), doesn't describe the return format, and provides no context about data sources or limitations. For a tool that presumably returns time-sensitive financial information, this level of documentation is inadequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description provides no parameter information, but the input schema has 100% description coverage with clear documentation for both 'symbol' (stock symbol format) and 'limit' (default behavior and usage guidance). Since schema_description_coverage is high (>80%), the baseline score of 3 is appropriate even though the description adds no value beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get earning dates' is a tautology that merely restates the tool name without adding meaningful context. It specifies the verb 'Get' and resource 'earning dates', but doesn't distinguish this tool from sibling tools like get_dividends or get_historical_stock_prices in terms of what specific financial data it retrieves. The description lacks specificity about what 'earning dates' actually means in this context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides absolutely no guidance about when to use this tool versus alternatives. There's no mention of use cases, prerequisites, or comparison to sibling tools like get_income_statement or get_cashflow that might provide related financial information. The agent receives no contextual help for tool selection decisions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves cashflow data but lacks details on rate limits, authentication needs, data freshness, error handling, or response format. For a tool with no annotations, this leaves significant gaps in understanding its operational behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any fluff. It's appropriately sized and front-loaded, making it easy to understand at a glance. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of financial data tools, no annotations, and no output schema, the description is insufficient. It doesn't explain what the cashflow data includes (e.g., operating, investing, financing activities), how it's formatted, or any limitations. For a tool with siblings offering related financial data, more context is needed to ensure proper use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with clear descriptions for both parameters (symbol and freq). The description adds no additional parameter semantics beyond what the schema provides, such as examples or edge cases. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 cashflow for a given stock symbol.' It specifies the verb ('Get') and resource ('cashflow'), and distinguishes it from siblings like get_income_statement or get_dividends by focusing on cashflow data. However, it doesn't explicitly differentiate from all siblings (e.g., it could mention it's for financial statements vs. price data).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention scenarios like financial analysis, comparisons with other statements, or prerequisites. With siblings like get_income_statement and get_dividends, there's no indication of when cashflow data is preferred over other financial metrics.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Get' implies a read operation, it doesn't specify whether this requires authentication, has rate limits, returns real-time or delayed data, or what format the price is in (e.g., currency, decimals). For a financial data tool with zero annotation coverage, this leaves significant gaps in understanding its behavior and constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that efficiently conveys the core functionality without any wasted words. It's appropriately sized for a simple tool and front-loads the essential information, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for a tool that likely returns financial data. It doesn't explain what the output includes (e.g., price alone, with timestamp, currency), potential errors (e.g., invalid symbol), or behavioral aspects like data freshness. For a stock price tool with no structured output documentation, more context is needed to fully understand its operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description mentions 'based on stock symbol', which aligns with the single parameter 'symbol' in the input schema. Since schema description coverage is 100% (the schema already describes 'symbol' as 'Stock symbol in Yahoo Finance format'), the description adds minimal value beyond what's in the structured data. This meets the baseline of 3 for high schema coverage without additional param details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'current stock price', making the purpose immediately understandable. It specifies 'based on stock symbol' which adds useful context. However, it doesn't explicitly differentiate from sibling tools like 'get_stock_price_by_date' or 'get_stock_price_date_range', which would require more specific language about the 'current' aspect.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. With multiple sibling tools for stock price data (e.g., 'get_stock_price_by_date', 'get_stock_price_date_range', 'get_historical_stock_prices'), there's no indication of when this specific tool for 'current' prices is appropriate versus those for historical or date-specific queries. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't add any behavioral traits beyond that—such as whether it's a read-only operation, potential rate limits, authentication needs, error handling, or the format of returned data. This leaves significant gaps in understanding how the tool behaves in practice.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded, consisting of a single sentence that directly states the tool's purpose. There is no wasted language or unnecessary elaboration, making it efficient and easy to parse for an AI agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of financial data retrieval, lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'dividends' entail (e.g., historical dividends, upcoming dividends, dividend yields), the return format, or any limitations. This leaves the agent with insufficient context to use the tool effectively beyond the basic parameter.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the single parameter 'symbol' fully documented in the schema as 'Stock symbol in Yahoo Finance format.' The description adds no additional meaning beyond this, as it only repeats the need for a stock symbol without providing extra context or semantics. With high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get dividends') and the target resource ('for a given stock symbol'), which provides a specific verb+resource combination. However, it doesn't differentiate this tool from its siblings (like get_earning_dates or get_historical_stock_prices), which also retrieve financial data for stock symbols, so it doesn't fully distinguish its specific purpose within the family of tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, exclusions, or compare it to sibling tools (e.g., get_earning_dates for earnings data or get_historical_stock_prices for price history), leaving the agent with no context for selection among similar financial data retrieval tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't describe how it behaves: there's no information about data format, pagination, rate limits, error handling, or authentication requirements. For a data-fetching tool with zero annotation coverage, this leaves significant gaps in understanding its operational characteristics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that communicates the core purpose without unnecessary words. It's appropriately sized for a straightforward data retrieval tool and front-loads the essential information immediately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of annotations and output schema, the description is incomplete for a tool with three parameters. While it states the purpose clearly, it doesn't address behavioral aspects like return format, data structure, or error conditions that would help an agent use it correctly. The schema handles parameters well, but overall context for invocation is lacking.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with all three parameters well-documented in the schema itself (symbol format, period options with defaults, interval options with defaults). The description adds no additional parameter information beyond what's already in the schema, so it meets the baseline of 3 where the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and resource 'historical stock prices' with the target 'for a given stock symbol', making the purpose immediately understandable. It distinguishes from siblings like 'get_current_stock_price' by specifying 'historical' data, though it doesn't explicitly contrast with other historical tools like 'get_stock_price_by_date' or 'get_stock_price_date_range'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. With multiple sibling tools for stock data (e.g., 'get_current_stock_price', 'get_stock_price_by_date', 'get_stock_price_date_range'), there's no indication of when this historical price tool is preferred over others or what specific use cases it serves.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the basic action ('Get income statement') without mentioning any behavioral traits such as data freshness, rate limits, authentication needs, error handling, or response format. This is inadequate for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It is appropriately sized and front-loaded, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what the income statement data includes, how it's structured, or any limitations (e.g., historical range, data sources). For a financial data tool with no structured output, more context is needed to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with clear documentation for both parameters ('symbol' and 'freq'), including defaults and valid values. The description adds no additional meaning beyond what the schema provides, so it meets the baseline score of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('income statement for a given stock symbol'), making it easy to understand what the tool does. However, it doesn't differentiate from sibling tools like 'get_cashflow' or 'get_earning_dates', which serve similar financial data retrieval functions, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. With siblings like 'get_cashflow' and 'get_earning_dates' available, it fails to specify scenarios where an income statement is preferred over other financial statements or data types, leaving the agent without usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states what the tool does without detailing traits like rate limits, authentication needs, data freshness, or response format. This is a significant gap for a tool that likely interacts with external APIs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no wasted words. It is front-loaded and efficiently conveys the core purpose, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It does not explain what 'news' entails (e.g., headlines, articles, sources) or the return format, leaving gaps in understanding the tool's behavior and output for the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage, with the 'symbol' parameter fully described in the schema. The description adds no additional meaning beyond what the schema provides, such as examples or constraints, so it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'news for a given stock symbol', making the purpose specific and understandable. However, it does not explicitly differentiate from sibling tools like get_recommendations or get_earning_dates, which might also provide news-related information, so it falls short of a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or comparisons to siblings such as get_current_stock_price or get_historical_stock_prices, leaving the agent without context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't describe traits like data freshness, rate limits, authentication needs, error handling, or what 'analyst recommendations' entail (e.g., ratings, targets). This leaves significant gaps for an agent to understand operational behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with zero waste. It's front-loaded with the core purpose and efficiently conveys the essential information without unnecessary elaboration, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of financial data tools and lack of annotations or output schema, the description is incomplete. It doesn't explain what 'analyst recommendations' include (e.g., buy/sell ratings, price targets), potential limitations, or how results are structured, leaving the agent with insufficient context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the parameter 'symbol' fully documented in the schema as 'Stock symbol in Yahoo Finance format.' The description adds no additional meaning beyond this, such as examples or constraints, so it meets the baseline for high schema coverage without compensating value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and resource ('analyst recommendations for a given symbol'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'get_news' or 'get_current_stock_price', which also retrieve financial data for symbols, so it's not fully specific to sibling context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. For example, it doesn't specify if this is for investment decisions, research, or how it differs from other data-fetching siblings like 'get_news' or 'get_historical_stock_prices'. The description lacks context on prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't cover important aspects like whether it's a read-only operation, potential rate limits, error handling, data freshness, or the format of returned data (e.g., time series). This is inadequate for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part of the sentence contributes directly to understanding the tool's function, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., price data format, frequency), error conditions, or usage constraints. For a financial data tool with three parameters, this leaves significant gaps for an AI agent to operate effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear documentation for all three parameters (symbol format, date formats). The description adds minimal value beyond the schema, only implying that parameters are used together to fetch price data over a range. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('stock prices'), and specifies the scope ('for a given date range for a given stock symbol'). However, it doesn't explicitly differentiate from sibling tools like 'get_historical_stock_prices' or 'get_stock_price_by_date', which likely have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_current_stock_price' (for single-point data) or 'get_historical_stock_prices' (which might serve a similar purpose), leaving the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool's function but lacks behavioral details such as data source (implied Yahoo Finance from schema), potential rate limits, error handling, or return format (e.g., price value, currency).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with zero waste, front-loaded with the core purpose, and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and 100% schema coverage, the description is adequate but incomplete. It covers the purpose but lacks details on behavior, output, or usage context, leaving gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters (symbol and date) with formats. The description adds no additional parameter semantics beyond what the schema provides, meeting the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'stock price', specifies the target 'for a given stock symbol on a specific date', and distinguishes it from siblings like get_current_stock_price (current vs. specific date) and get_stock_price_date_range (single date vs. range).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving historical stock prices at a specific date, but does not explicitly state when to use this tool versus alternatives like get_current_stock_price or get_stock_price_date_range, nor does it mention exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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