Baguskto Saham

Server Quality Checklist

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Latest release: v1.0.5
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap. For example, 'get_historical_data' retrieves price data, while 'get_stock_analysis' provides technical analysis, and 'get_stock_info' gives company details. The descriptions make it easy to differentiate between tools like 'compare_stocks' for multi-stock comparisons and 'search_stocks' for finding stocks by name or ticker.
Naming Consistency5/5
All tools follow a consistent verb_noun naming pattern, primarily using 'get_' or 'compare_' prefixes. There are no deviations in style, such as mixing snake_case with camelCase, and the verbs are descriptive and uniform across the set. This predictability enhances usability and reduces confusion.
Tool Count5/5
With 9 tools, the server is well-scoped for its purpose of Indonesian stock market analysis. Each tool serves a specific function, such as data retrieval, comparison, or search, without redundancy. The count is neither too sparse nor overwhelming, fitting typical expectations for a domain-specific server.
Completeness4/5
The tool set covers core aspects of stock market analysis, including data access, comparisons, sector performance, and technical analysis. Minor gaps exist, such as the lack of tools for real-time updates or portfolio management, but agents can work around these with the available tools for historical and overview data.
Average 3/5 across 9 of 9 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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This repository includes a README.md file.
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}
```
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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 mentions 'compare performance' but doesn't specify what 'performance' entails (e.g., returns, volatility, metrics), how results are formatted, or any constraints like rate limits or authentication needs. For a tool with no annotations, this leaves significant gaps in understanding its 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 front-loaded and appropriately sized, with every word contributing to understanding the core functionality, 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 complexity of comparing stocks and the lack of annotations and output schema, the description is insufficient. It doesn't explain what 'performance' means, the format of results, or any behavioral traits. With no structured data to compensate, the description should provide more context to be complete for this type of tool.
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 descriptions for 'tickers' and 'period' in the input schema. The description adds no additional parameter semantics beyond what the schema provides, such as explaining the meaning of 'performance' or details on period options. 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: 'Compare performance of multiple stocks over a specified period.' It specifies the verb ('compare'), resource ('stocks'), and scope ('multiple' and 'over a specified period'). However, it doesn't explicitly differentiate from sibling tools like 'get_historical_data' or 'get_stock_analysis,' which might offer overlapping functionality, so it doesn't reach a 5.
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 such as 'get_historical_data' and 'get_stock_analysis,' there's no indication of when this comparison tool is preferred or what its specific use cases are. It lacks explicit when/when-not statements or named alternatives, leaving usage unclear.
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 mentions retrieving historical price data but fails to add context such as data format, update frequency, rate limits, or error handling. This leaves significant gaps in understanding how the tool behaves beyond its 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.
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 and understand 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 retrieving historical data, lack of annotations, and no output schema, the description is incomplete. It doesn't cover return values, data granularity, or potential limitations, which are crucial for an agent to use the tool effectively in context with sibling tools.
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 ('ticker' and 'period') with descriptions and an enum for 'period'. The description adds no additional meaning beyond what the schema provides, such as examples or constraints, resulting in a baseline score of 3.
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 ('historical price data for a specific stock'), making it easy to understand what it does. However, it doesn't explicitly differentiate from sibling tools like 'get_stock_info' or 'get_stock_analysis', which might also retrieve stock-related data, so it misses 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 like 'get_stock_info' or 'get_stock_analysis', nor does it mention any prerequisites or exclusions. It only states what it does, leaving the agent to infer usage from context without explicit direction.
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 mentions 'comprehensive technical analysis' but lacks details on what that entails, such as response format, data sources, rate limits, or error handling. This leaves significant gaps for a tool that likely returns complex data.
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 concise and front-loaded in a single sentence, efficiently stating the core function without unnecessary words. However, it could be slightly improved by structuring key details more explicitly, but it earns a high score for brevity.
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 technical analysis and the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'comprehensive' means, what indicators or recommendations are included, or how results are formatted, making it inadequate for guiding an agent 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?
The description adds minimal meaning beyond the input schema, which has 100% coverage. It implies parameters for stock ticker and period but doesn't elaborate on semantics like what 'comprehensive' includes or how indicators are calculated. Baseline 3 is appropriate as the schema adequately documents the 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's purpose with a specific verb ('Get') and resource ('comprehensive technical analysis for a stock'), including what it provides ('indicators and recommendations'). However, it doesn't explicitly differentiate from sibling tools like 'get_historical_data' or 'get_stock_info', which might also provide stock-related data, 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. It doesn't mention sibling tools like 'get_historical_data' or 'get_stock_info', nor does it specify contexts or exclusions for usage, leaving the agent without clear direction.
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 'gets detailed information' but doesn't specify what that includes (e.g., price, volume, company details), whether it's real-time or delayed, if there are rate limits, or what the output format is. This leaves significant gaps in understanding the tool's behavior beyond basic purpose.
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 purpose without unnecessary words. It is front-loaded with the key action and resource, making it easy to parse. There is no wasted language, and it fits well within the context of the tool's simplicity.
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 (a read operation with one parameter) and lack of annotations or output schema, the description is incomplete. It doesn't explain what 'detailed information' entails, how results are returned, or any behavioral traits like data freshness or limitations. For a tool with no structured output or annotations, more context is needed to fully understand its 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 the 'ticker' parameter fully documented in the schema. The description adds no additional meaning beyond the schema, as it doesn't elaborate on ticker formats, examples beyond those in the schema, or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
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 detailed information') and resource ('specific Indonesian stock'), making the purpose evident. It distinguishes from siblings like 'get_historical_data' or 'get_stock_analysis' by focusing on current detailed info rather than historical data or analysis. However, it doesn't explicitly differentiate from 'get_available_stocks' or 'search_stocks', which might also provide stock information.
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 when to choose this over 'get_historical_data' for historical trends, 'get_stock_analysis' for deeper insights, or 'search_stocks' for broader searches. There are no exclusions or prerequisites stated, 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.
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 searches for stocks but doesn't describe how results are returned (e.g., list format, pagination, sorting), potential limitations (e.g., rate limits, authentication needs), or error handling. For a search tool with zero annotation coverage, this leaves significant gaps in understanding its behavior 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with zero wasted words. It's front-loaded with the core action ('Search for stocks') and efficiently specifies the search criteria. Every part of the sentence earns its place, making it highly concise and well-structured for quick understanding.
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 moderate complexity (search functionality with one parameter) and the absence of both annotations and an output schema, the description is incomplete. It doesn't cover behavioral aspects like result format, pagination, or error cases, which are crucial for an agent to use the tool effectively. With no structured data to compensate, the description should provide more context to be fully helpful.
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 the 'query' parameter documented as 'Search query (company name or partial ticker)'. The description adds minimal value by restating this ('Search for stocks by company name or ticker symbol'), but doesn't provide additional context like examples, formatting tips, or search algorithm details. 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 ('Search') and resource ('stocks'), and specifies the search criteria ('by company name or ticker symbol'). It distinguishes itself from siblings like 'get_available_stocks' or 'get_stock_info' by focusing on search functionality rather than retrieval of predefined lists or detailed information. However, it doesn't explicitly differentiate from 'compare_stocks' which might also involve searching, making it a 4 rather than a 5.
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 when to prefer 'search_stocks' over siblings like 'get_available_stocks' (which might list stocks without search) or 'get_stock_info' (which might require a specific ticker). There's no context on prerequisites, exclusions, or typical use cases, 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.
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 but offers minimal information. It states what data is retrieved but doesn't describe format, freshness, rate limits, authentication needs, or potential side effects. This is inadequate for a tool with zero 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 wasted words. It's appropriately sized for a simple data retrieval tool and front-loads the essential information.
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 data retrieval tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what 'performance data' includes (e.g., metrics, timeframes), the return format, or how results are structured, leaving significant gaps for an AI agent to use it correctly.
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 tool has 0 parameters with 100% schema description coverage, so the schema fully documents the absence of inputs. The description appropriately doesn't discuss parameters, maintaining focus on the tool's purpose without redundancy. A baseline of 4 is justified for zero-parameter tools.
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 performance data') and target resource ('all IDX sectors'), providing a specific verb+resource combination. However, it doesn't distinguish this tool from potential siblings like 'get_market_overview' or 'get_historical_data' that might also provide sector-related data, preventing 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 doesn't mention what makes it different from sibling tools like 'get_market_overview' or 'get_historical_data' that might offer overlapping functionality, nor does it specify any prerequisites or exclusions for its use.
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 this is a read operation ('Get list'), implying it's non-destructive, but doesn't mention any behavioral traits such as rate limits, authentication needs, response format, or potential side effects. This is inadequate for a tool with zero 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 any wasted words. It's appropriately sized for a simple tool with no parameters, making it easy for an agent to parse quickly.
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 simplicity (0 parameters, no output schema), the description is minimally complete but lacks depth. It doesn't explain what the returned list includes (e.g., ticker symbols only, with metadata), how it's structured, or any limitations (e.g., dataset size, freshness). With no annotations, this leaves gaps in understanding the tool's behavior.
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 tool has zero parameters, and the schema description coverage is 100%, so there are no parameters to document. The description appropriately doesn't discuss parameters, earning a baseline score of 4 for not introducing unnecessary complexity.
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 list') and resource ('all available stock tickers in the historical dataset'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this tool from sibling tools like 'search_stocks' or 'get_stock_info', which prevents 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 like 'search_stocks' or 'get_stock_info'. It mentions the scope ('historical dataset') but doesn't specify use cases, prerequisites, or exclusions, leaving the agent with minimal contextual direction.
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 states the tool retrieves information (implying read-only), but doesn't disclose behavioral traits like authentication needs, rate limits, error conditions, or what 'coverage' entails (e.g., temporal, geographical). For a tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves operationally.
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 ('Get information about the historical dataset') and adds specific details ('including last update and coverage'). There is zero wasted language, and every word earns its place by clarifying scope without redundancy.
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 low complexity (0 parameters, no output schema, no annotations), the description is complete enough for basic understanding. It specifies what information is retrieved, which compensates for the lack of output schema. However, without annotations or more detailed behavioral context, it doesn't fully address operational aspects like error handling or data format, keeping it at an adequate but minimal level.
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 tool has 0 parameters, and schema description coverage is 100% (since there are no parameters to describe). The description doesn't need to add parameter semantics, so it meets the baseline of 4 for tools with no parameters, as it appropriately doesn't discuss nonexistent inputs.
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 'information about the historical dataset', specifying what data is retrieved (last update and coverage). It distinguishes from siblings like 'get_historical_data' (which likely returns actual data points) and 'get_stock_info' (which focuses on individual stocks). However, it doesn't explicitly contrast with all siblings, keeping it at 4 rather than 5.
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_historical_data', 'get_market_overview', and 'get_stock_info', there's no indication of whether this tool is for metadata about datasets versus actual data retrieval, or any prerequisites for usage. It relies entirely on the agent to infer context from the name and description 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 describes what data is returned (IHSG index, volume, top movers) but doesn't cover critical aspects like whether this is a read-only operation, potential rate limits, authentication needs, data freshness, or error conditions. For a tool with zero annotation coverage, this leaves significant gaps in understanding its 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 wasted words. It's front-loaded with the core action ('Get Indonesian stock market overview') and includes specific details (IHSG index, volume, top movers) that earn their place by clarifying scope.
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 simplicity (0 parameters, no annotations, no output schema), the description is minimally adequate. It explains what data is returned but doesn't cover behavioral aspects like data format, potential limitations, or how it differs from siblings. Without annotations or output schema, more context on return values or usage constraints would improve completeness for this data-fetching tool.
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 tool has 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description appropriately doesn't discuss parameters, which is correct for this case. A baseline of 4 is applied since no parameter information is needed, and the description doesn't attempt to add unnecessary 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 tool's purpose: 'Get Indonesian stock market overview including IHSG index, volume, and top movers.' It specifies the verb ('Get'), resource ('Indonesian stock market overview'), and key components (IHSG index, volume, top movers). However, it doesn't explicitly differentiate from siblings like 'get_sector_performance' or 'get_historical_data' beyond the general 'overview' scope.
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 when to choose this over siblings like 'get_sector_performance' for sector-specific data or 'get_historical_data' for time-series analysis. There's no explicit context, exclusions, or prerequisites stated.
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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