Financial Analysis MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes, but there is some overlap between the three plotting tools (mcp_plot_comparison_chart, mcp_plot_financial_metric, mcp_plot_price_chart) which could cause confusion about when to use each. The retrieve tools are clearly differentiated by the type of data they fetch.
Naming Consistency3/5
The naming is mixed: most tools use a verb_noun pattern (e.g., retrieve_current_price, calculate_financial_metrics), but the mcp_ prefix is inconsistently applied (some have it, some don't), and tools like retrieve_splits lack clear verb consistency. This creates a somewhat readable but inconsistent convention.
Tool Count5/5
With 13 tools, the count is well-scoped for a financial analysis server, covering data retrieval, calculations, and visualization without being overwhelming. Each tool serves a clear purpose in the domain.
Completeness4/5
The toolset provides good coverage for financial analysis, including data retrieval, metric calculations, and plotting. Minor gaps exist, such as no explicit tools for updating or deleting data, but these are not critical for the analysis-focused purpose, and agents can work around them.
Average 3.1/5 across 13 of 13 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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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

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 action ('Retrieves') without details on permissions, rate limits, data freshness, or output format. For a retrieval tool with zero annotation coverage, this leaves critical behavioral traits unspecified, though it doesn't contradict anything.
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 with zero waste. It's front-loaded and appropriately sized for the simple action, making it easy to parse quickly without unnecessary elaboration.
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 (a retrieval tool with no annotations, no output schema, and 0% schema coverage), the description is incomplete. It doesn't explain what 'splits' are, the return format, or behavioral aspects like data scope. This inadequately supports the agent in using the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description mentions 'ticker' but adds no meaning beyond the input schema, which has 0% description coverage and only defines 'ticker' as a string. No details on ticker format, examples, or constraints are provided. With low schema coverage, the description fails to compensate, leaving the parameter poorly documented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the action ('Retrieves') and resource ('splits for the given ticker'), providing a basic purpose. However, it's vague about what 'splits' specifically refers to (e.g., stock splits, data splits) and doesn't distinguish from siblings like 'retrieve_dividends' or 'retrieve_stock_data', which might handle similar financial data. This meets the minimum viable threshold but lacks specificity.
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. It doesn't mention prerequisites, context (e.g., historical vs. current splits), or exclusions. Given siblings like 'retrieve_dividends' and 'retrieve_stock_data', the agent has no help in choosing between them, leading to potential misuse.
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 action ('retrieves') without detailing traits like whether it's a read-only operation, requires authentication, has rate limits, returns historical or real-time data, or what format the output takes. 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 extremely concise with a single sentence, front-loaded and without any wasted words. It directly states the tool's purpose, making it efficient in structure, though this brevity contributes to gaps in other dimensions.
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 (a financial data retrieval tool), lack of annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't explain what 'dividends' entails (e.g., dividend history, yields, dates), return values, or error handling, leaving significant gaps for the agent to understand the tool fully.
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 adds minimal meaning beyond the input schema, which has 0% coverage. It mentions 'for the given ticker', implying the 'ticker' parameter, but doesn't specify format (e.g., stock symbol conventions), constraints, or examples. With low schema coverage, the description doesn't fully compensate, but it at least hints at the parameter's role.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool's purpose ('Retrieves dividends for the given ticker'), which is clear but vague. It specifies the verb ('retrieves') and resource ('dividends'), but doesn't distinguish it from sibling tools like 'retrieve_stock_data' or 'retrieve_financial_statements', leaving ambiguity about what exactly is being retrieved.
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. The description doesn't mention any context, prerequisites, or exclusions, such as whether it's for historical or current dividends, or how it differs from other retrieval tools in the server. This leaves the agent without clear usage instructions.
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 full burden. It discloses that metrics require specific financial statements (e.g., income_statement, balance_sheet) and mentions a return format example, but lacks critical behavioral details like error handling, data format expectations for financial_data, or computational assumptions. This is inadequate for a tool with complex inputs and no 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is moderately structured with a clear purpose statement, bulleted metric list, and parameter/return sections. However, it includes redundant or conflicting information (e.g., window parameter not in schema, return example that might mislead about output format). Some sentences could be more efficient, but it's not overly verbose.
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 tool's complexity (2 parameters with nested objects, no annotations, no output schema), the description is incomplete. It lacks details on financial_data structure, error cases, or how metrics are computed. The return example is minimal, and the mismatch with the schema (window parameter) adds confusion. More context is needed for reliable use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds significant meaning: financial_data is a Dict with financial data, indicator is a str from supported metric names (listing 15 examples), and window is an int for window size (though window is not in the input schema, indicating a mismatch). This clarifies parameter purposes beyond the bare schema, but the schema-description inconsistency limits the score.
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 calculates financial metrics from provided data, specifying the verb 'calculates' and resource 'financial metrics'. It lists 15 specific metrics, which helps distinguish it from siblings like mcp_calculate_growth_rates or mcp_calculate_technical_indicators, though it doesn't 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.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like mcp_calculate_growth_rates or retrieve_financial_statements. The description implies usage by listing metrics and their data requirements but lacks explicit when/when-not instructions or prerequisites for the financial_data parameter.
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 full burden but provides minimal behavioral disclosure. It mentions the tool calculates indicators but doesn't cover error conditions, performance characteristics, data format requirements beyond ordering, or what happens with insufficient data points. The return format is vaguely described as 'Dict with indicator values' without specifying structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded with the core purpose. The Args/Returns structure is clear, though some sentences could be more efficient (e.g., 'Dict with indicator values' is vague).
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?
For a calculation tool with 3 parameters, 0% schema coverage, no annotations, and no output schema, the description is incomplete. It lacks details on parameter constraints, error handling, return structure, and performance considerations that would help an agent use it correctly.
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 0%, so the description must compensate. It adds meaning by explaining price_data should be 'List of closing prices ordered oldest to newest' and listing valid indicator values, which the schema doesn't specify. However, it doesn't explain window parameter semantics or provide examples, leaving gaps for a tool with 3 parameters.
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 calculates technical indicators on stock price data, listing specific indicators (SMA, EMA, RSI, MACD, volatility). It distinguishes from some siblings like financial metrics or plotting tools, but doesn't explicitly differentiate from all calculation siblings.
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 on when to use this tool versus alternatives is provided. The description doesn't mention when to choose technical indicators over financial metrics, growth rates, or other calculation tools, nor does it specify prerequisites or appropriate contexts.
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 full burden. It mentions the tool creates a plot and can optionally save it, but doesn't disclose important behavioral traits: whether it requires specific file permissions, what happens if the filename already exists, what image format is generated, or any performance/rate limit considerations. The description is minimal and lacks 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and well-structured with clear sections for Args and Returns. Each sentence serves a purpose: stating the core function, listing parameters, and describing the return value. There's no wasted text, though the parameter explanations could be more detailed given the 0% schema coverage.
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?
For a 5-parameter tool with no annotations and no output schema, the description is incomplete. It doesn't explain the tool's behavior in detail, doesn't clarify when to use it versus sibling plotting tools, and provides minimal parameter guidance. The return format is mentioned but without schema details. Given the complexity and lack of structured documentation, the description should do more to help an agent understand this tool's 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?
Schema description coverage is 0%, so the description must compensate. It lists all 5 parameters with brief explanations, providing basic semantic meaning beyond the schema's property names. However, it doesn't explain parameter relationships (e.g., dates and values arrays must match in length), data formats (date string format, value types), or constraints (minimum array lengths). The description adds value but doesn't fully compensate for the schema's lack of descriptions.
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: 'Plots a financial metric over time.' This is a specific verb ('plots') with a clear resource ('financial metric') and temporal scope ('over time'). However, it doesn't explicitly differentiate from sibling tools like 'mcp_plot_comparison_chart' or 'mcp_plot_price_chart', which likely have overlapping charting 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. With multiple plotting tools in the sibling list (mcp_plot_comparison_chart, mcp_plot_price_chart, mcp_plot_trading_opportunities), there's no indication of what makes this tool distinct or when it's the appropriate choice over those other visualization 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. It states this is a retrieval operation but doesn't disclose behavioral traits like whether it requires authentication, has rate limits, what happens if the ticker is invalid, or if it's a read-only operation. For a tool with zero annotation coverage, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded with the core purpose. The Args and Returns sections add structure, though they could be more integrated. Every sentence earns its place, but the formatting is slightly verbose for such a simple tool.
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 (financial data retrieval), lack of annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't explain what information is returned (e.g., price, volume, company details) or handle edge cases, making it inadequate for reliable agent 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 description adds minimal semantics beyond the input schema, which has 0% description coverage. It clarifies that 'ticker' is 'the ticker symbol of the stock', but doesn't specify format (e.g., uppercase, with exchange prefix) or constraints. With low schema coverage, this provides some value but doesn't fully compensate.
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 ('retrieves') and resource ('information about the given ticker'), making the purpose understandable. However, it doesn't distinguish this from sibling tools like 'retrieve_current_price' or 'retrieve_stock_data', which likely retrieve similar information but with different scopes or formats.
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 'retrieve_current_price' and 'retrieve_stock_data', it's unclear what specific information this tool provides that others don't, leaving the agent to guess based on tool names alone.
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 calculates growth rates and returns a dict with specific keys, but lacks details on edge cases (e.g., handling of empty series, negative values, or single data points), computational methods (e.g., formula used), or error handling. This is a significant gap for a calculation 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with the core purpose stated first, followed by parameter and return details. Each sentence adds value, and there's no redundant information. It could be slightly more structured (e.g., bullet points), but overall it's efficient and clear.
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 the tool's moderate complexity (calculation-based, no annotations, no output schema), the description is minimally adequate. It covers the purpose, parameter semantics, and return structure, but lacks behavioral details like error handling or method specifics. Without an output schema, it partially compensates by describing the return dict, but more context would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful semantics beyond the input schema. The schema only indicates 'series' is an array with 0% description coverage. The description clarifies it's a 'List of numbers ordered oldest to newest,' providing crucial ordering and data type context. However, it doesn't specify minimum length or validation rules, leaving some gaps.
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: 'Calculates growth rates for a time series (e.g., revenue, earnings).' It specifies the verb ('calculates'), resource ('growth rates'), and domain context ('time series'). However, it doesn't explicitly differentiate from sibling tools like 'mcp_calculate_financial_metrics' or 'mcp_calculate_technical_indicators', which might also involve calculations on financial 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 mentions examples ('e.g., revenue, earnings') but doesn't specify contexts, prerequisites, or exclusions. With sibling tools like 'mcp_calculate_financial_metrics' available, there's no indication of how this tool differs or when it's preferred, leaving usage ambiguous.
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 plots data and returns an image, but lacks details on permissions, rate limits, side effects (e.g., file creation), or error handling. The description doesn't contradict annotations (none exist), but it's insufficient for a tool that generates outputs without covering behavioral traits like resource usage or potential failures.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with the core purpose. The Args and Returns sections are structured clearly, with each sentence adding value (e.g., parameter explanations). There's minimal waste, though it could be slightly more concise by integrating defaults into the parameter descriptions more seamlessly.
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 the tool's complexity (plotting with signals), lack of annotations, and no output schema, the description is moderately complete. It covers parameters well and specifies the return format ('Dict with 'file_path' and 'base64''), but lacks behavioral context (e.g., how signals are generated, image format, or error cases). For a tool with no structured support, it's adequate but has clear gaps in operational guidance.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds significant meaning beyond the input schema, which has 0% description coverage. It explains 'prices' as a list of dicts with specific keys ('date', 'open', etc.), defines 'short_window' and 'long_window' for moving averages with defaults, and clarifies 'title' as the chart title. This compensates well for the schema's lack of descriptions, though it doesn't cover all potential nuances (e.g., data format validation).
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: 'Plots price data with trading signals/opportunities.' It specifies the verb ('plots') and resource ('price data with trading signals/opportunities'), which is specific and actionable. However, it doesn't explicitly differentiate from sibling tools like 'mcp_plot_price_chart' or 'mcp_plot_comparison_chart', which likely have overlapping charting 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 or contexts where this specific plotting tool is preferred over others (e.g., 'mcp_plot_price_chart' or 'mcp_plot_comparison_chart'). Usage is implied only through the description of what it does, with no explicit when/when-not instructions or prerequisites.
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 data, implying a read-only operation, but doesn't cover important aspects like authentication needs, rate limits, error handling, data freshness, or whether it's a safe operation. The description is minimal and lacks behavioral context beyond the basic function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with the core purpose. The 'Args' and 'Returns' sections add structure without unnecessary verbosity. Every sentence contributes to understanding the tool, though the 'Returns' section could be slightly more specific about the dict structure.
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 the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is somewhat complete but has gaps. It covers the basic function and parameters well, but lacks details on behavioral traits, usage context, and output specifics. Without annotations or an output schema, more information on data format, errors, and constraints would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds significant meaning beyond the input schema, which has 0% description coverage. It explains that 'ticker' is for a stock symbol and 'indicator' specifies the type of financial statement (e.g., 'income_statement'), including examples. This compensates well for the schema's lack of descriptions, though it doesn't detail all possible values for 'indicator' or format requirements for 'ticker'.
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: retrieving specific financial statements (income statements, balance sheet, or cash flow) for a given stock ticker. It uses specific verbs ('retrieves') and resources ('annual income statements, balance sheet, or cash flow'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'retrieve_stock_data' or 'retrieve_ticker_info', which might also retrieve financial 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 sibling tools like 'retrieve_stock_data' or 'retrieve_ticker_info', nor does it specify prerequisites, exclusions, or contextual cues for choosing this tool over others. Usage is implied only by the tool's name and description, with no explicit when/when-not instructions.
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 the action is a retrieval but doesn't cover critical aspects like rate limits, authentication needs, data freshness, error handling, or response format details beyond 'JSON-serializable dict'. This leaves significant gaps for a tool that likely interacts with external data sources.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded with the core purpose in the first sentence. The Args and Returns sections are structured but slightly verbose for a single parameter; every sentence earns its place, though minor trimming could improve efficiency.
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 tool's complexity (financial data retrieval with no annotations or output schema), the description is incomplete. It doesn't explain what 'related info' includes in the return, potential errors, data sources, or limitations, making it inadequate for reliable agent use in this context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful context for the single parameter 'ticker', explaining it's 'the ticker symbol of the stock to retrieve the current price for'. With 0% schema description coverage and only one parameter, this adequately compensates, though it doesn't specify format constraints (e.g., case sensitivity, exchange prefixes).
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 specific action ('Retrieves') and resource ('current price for the given ticker'), distinguishing it from siblings like retrieve_dividends or retrieve_financial_statements. It precisely identifies what the tool does without being vague or tautological.
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 like retrieve_stock_data or retrieve_ticker_info. The description lacks context about use cases, prerequisites, or exclusions, leaving the agent with no direction on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 discloses that the tool generates a plot and returns a dict with file details, which is useful. However, it lacks behavioral details like whether it saves files automatically, requires specific permissions, has rate limits, or handles errors. The description doesn't contradict annotations, but it's insufficient for a mutation 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 appropriately sized and front-loaded, with the purpose stated first, followed by a bullet-like list of parameters and returns. Every sentence earns its place by providing essential information without redundancy, making it efficient and easy to parse.
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 nested objects in the input schema, the description is moderately complete. It covers the basic purpose and parameters but lacks details on behavioral traits, error handling, or output structure beyond a brief mention. For a tool with 4 parameters and mutation potential (plotting/saving), it should do more to compensate for the missing structured data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds meaningful semantics by explaining each parameter: 'dates' as 'List of date strings,' 'series' as 'Dict where keys are labels and values are lists of values for each date,' 'title' as 'Chart title,' and 'filename' as 'Optional filename to save the plot.' This clarifies the structure and purpose beyond the bare schema, though it could detail formats (e.g., date format) more.
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: 'Plots a comparison chart for multiple tickers or financial metrics over time.' It specifies the verb ('Plots') and resource ('comparison chart'), and distinguishes from siblings like mcp_plot_price_chart by mentioning 'multiple tickers or financial metrics.' However, it doesn't explicitly differentiate from mcp_plot_financial_metric, which might also handle multiple metrics, making it slightly less specific.
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 sibling tools like mcp_plot_price_chart and mcp_plot_financial_metric, it's unclear when this tool is preferred, such as for comparing multiple series versus single series. No exclusions or prerequisites are mentioned, leaving usage context implied at best.
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 mentions that the tool generates and optionally saves a chart, but lacks critical details such as required permissions, file format (e.g., PNG, JPEG), whether it overwrites existing files, error handling, or performance characteristics like rate limits. This is inadequate for a tool that creates files.
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 efficiently structured with a purpose statement followed by bullet-like sections for Args and Returns, using minimal sentences that each add value. It avoids redundancy and is appropriately sized for the tool's complexity, making it easy to scan and understand.
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 and no output schema, the description partially meets contextual needs. It explains parameters well and specifies the return format, but lacks behavioral details like file handling or error conditions. For a chart-generation tool with file output, more context on operational aspects would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds significant meaning beyond the input schema, which has 0% description coverage. It clearly defines the 'prices' parameter structure (list of dicts with specific keys), explains 'chart_type' options ('line' or 'candlestick'), and notes that 'filename' is optional for saving. This fully compensates for the schema's lack of documentation.
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 tool's purpose with specific verbs ('generates a price chart') and resources ('from OHLCV data'), distinguishing it from siblings like mcp_plot_comparison_chart or mcp_plot_financial_metric by focusing on price data visualization. It explicitly mentions chart types (line or candlestick), making the scope unambiguous.
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 mcp_plot_comparison_chart and mcp_plot_financial_metric, there is no indication of when price charting is preferred over other visualization tools, nor any mention of prerequisites or typical use cases.
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 mentions the tool retrieves data using yfinance but lacks details on behavioral traits such as rate limits, error handling, authentication needs, or data freshness. The description does not contradict annotations, but it is insufficient for a mutation-free 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded with the core purpose, followed by structured parameter and return sections. Every sentence adds value, though it could be slightly more concise by integrating examples inline.
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 4 parameters, the description covers input semantics well but lacks behavioral context (e.g., rate limits, errors) and detailed output explanation. It is adequate for basic use but has clear gaps for robust agent interaction.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
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 explaining all four parameters with clear semantics, examples, and the return structure. It adds significant meaning beyond the basic schema, detailing ticker_list as a list of symbols, date formats, interval options, and the output mapping.
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 tool's purpose with a specific verb ('Retrieves') and resource ('stock data for a list of tickers using yfinance'), distinguishing it from siblings like retrieve_current_price or retrieve_financial_statements by specifying historical data retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for historical stock data retrieval but does not explicitly state when to use this tool versus alternatives like retrieve_current_price (for real-time data) or other siblings. No exclusions or specific contexts are provided.
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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