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 analysis and suggestions but doesn't describe what the tool actually does (e.g., runs diagnostics, returns metrics, or provides textual advice), whether it's read-only or has side effects, or any performance or permission considerations. 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.

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

The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly. Every word earns its place.

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

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

Given the complexity of performance analysis and the lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., suggestions, metrics, or reports), how it interacts with the database, or any limitations. For a tool with no structured output and behavioral gaps, this is insufficient.

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

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

The schema description coverage is 100%, with clear descriptions for both parameters ('query' and 'database'). The description adds no additional meaning beyond what the schema provides, such as format details or usage examples. 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.

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

The description clearly states the tool's purpose as 'analyze query performance and provide optimization suggestions,' which is a specific verb+resource combination. However, it doesn't differentiate from siblings like 'detect_query_bottlenecks' or 'get_optimization_insights,' which appear to have overlapping functionality. The purpose is clear but lacks sibling distinction.

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

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

The description provides no guidance on when to use this tool versus alternatives. With siblings like 'detect_query_bottlenecks,' 'explain_query,' and 'get_optimization_insights,' there's no indication of context, exclusions, or prerequisites. This leaves the agent guessing about the appropriate use case.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action but lacks details on permissions needed, rate limits, response format, or whether it's read-only (implied by 'Get' but not explicit). This leaves gaps for a tool with 3 parameters 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.

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 unnecessary words. It's front-loaded and appropriately sized for a simple read operation, with no wasted information.

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

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

Given the complexity (3 parameters, no output schema, no annotations), the description is incomplete. It doesn't explain what schema information is returned (e.g., column names, types, constraints) or address behavioral aspects like error handling, making it inadequate for full agent understanding.

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

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

The input schema has 100% description coverage, providing clear details for all parameters. The description adds no additional parameter semantics beyond what's in the schema, such as examples or constraints, so it meets the baseline for high schema coverage without extra value.

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

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

The description clearly states the verb ('Get') and resource ('schema information for a specific table'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from siblings like 'list_tables' (which lists table names) or 'get_table_data' (which retrieves row data), missing full sibling distinction.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention use cases like schema inspection before queries or contrast with siblings such as 'list_tables' for metadata overview or 'explain_query' for query-specific schema details.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'detect and analyze' but doesn't specify whether this is a read-only operation, if it requires special permissions, its impact on database performance, or what the analysis entails (e.g., returns a report, logs data). For a diagnostic tool with zero annotation coverage, this is a significant gap in transparency.

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

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

The description is a single, efficient sentence that front-loads the core purpose ('Detect and analyze query bottlenecks in the database') with zero wasted words. It's appropriately sized for the tool's complexity and gets straight to the point without unnecessary elaboration.

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

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

Given the tool's diagnostic nature, no annotations, and no output schema, the description is incomplete. It doesn't cover what the tool returns (e.g., a list of bottlenecks, analysis details), behavioral aspects like performance impact, or how it differs from siblings. For a tool with 3 parameters and potential complexity, 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.

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

Schema description coverage is 100%, meaning all parameters are documented in the schema itself. The description adds no additional meaning beyond the schema, such as explaining how 'database' selection affects results or what 'severity_filter' implies in practice. Baseline 3 is appropriate when the schema does the heavy lifting without description enhancement.

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

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

The description clearly states the tool's purpose with a specific verb ('detect and analyze') and resource ('query bottlenecks in the database'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'analyze_query_performance' or 'get_query_performance', which likely have overlapping functionality, 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.

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 such as 'analyze_query_performance' and 'get_query_performance' available, there's no indication of specific contexts, prerequisites, or exclusions for choosing this tool, 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.

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. While 'Execute a SQL query' implies a write operation could occur, it doesn't specify whether this tool supports read-only queries, what permissions are required, whether transactions are managed, what happens with errors, or any rate limits. For a database execution tool with zero annotation coverage, this leaves significant behavioral questions unanswered.

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

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

The description is a single, efficient sentence that communicates the core purpose without any wasted words. It's appropriately sized for a straightforward execution tool and gets directly to the point.

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

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

For a database query execution tool with no annotations and no output schema, the description is insufficiently complete. It doesn't address critical context like what types of queries are supported (SELECT, INSERT, etc.), whether results are returned, error handling, transaction behavior, or security implications. Given the complexity of database operations and the lack of structured metadata, the description should provide more operational context.

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

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

The description mentions executing 'a SQL query' which aligns with the 'query' parameter, but adds no additional semantic context beyond what the 100% covered schema already provides. The schema descriptions clearly document both parameters, so the description doesn't compensate but doesn't need to given the comprehensive schema coverage.

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

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

The description clearly states the action ('Execute') and resource ('SQL query on the connected SQL Server database'), providing a specific verb+resource combination. However, it doesn't distinguish this tool from potential alternatives like 'explain_query' or 'analyze_query_performance' which might also involve query execution with different purposes.

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

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

The description provides no guidance on when to use this tool versus alternatives. With 15 sibling tools including 'explain_query', 'analyze_query_performance', and 'get_table_data', there's no indication of when this general execution tool is preferred over more specialized alternatives or what types of queries it's designed for.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'analyze performance' but doesn't specify whether this is a read-only operation, if it requires specific permissions, what the output format is (e.g., text, JSON), or any rate limits. For a tool with no annotations, 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.

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

The description is a single, efficient sentence: 'Get the execution plan for a SQL query to analyze performance.' It is front-loaded with the core purpose, avoids redundancy, and every word contributes meaning without waste. This makes it easy to scan and understand quickly.

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

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

Given the complexity of SQL query analysis and the lack of annotations and output schema, the description is incomplete. It doesn't explain what an 'execution plan' entails (e.g., visual vs. textual), performance metrics included, or error handling. For a tool with no structured output and behavioral gaps, more detail is needed to fully inform usage.

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

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

The input schema has 100% description coverage, clearly documenting all three parameters. The description adds no additional parameter semantics beyond what the schema provides (e.g., it doesn't explain query syntax requirements or database context implications). 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.

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 the execution plan for a SQL query to analyze performance.' It specifies the action ('Get') and resource ('execution plan'), and distinguishes it from siblings like 'execute_query' or 'get_query_performance' by focusing on plan analysis rather than execution or metrics. However, it doesn't explicitly differentiate from 'analyze_query_performance' or 'detect_query_bottlenecks', which might have overlapping purposes.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a valid SQL query), exclusions (e.g., not for actual query execution), or comparisons to siblings like 'analyze_query_performance' or 'get_optimization_insights'. Without such context, users must infer usage from the purpose alone.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool exports data but doesn't mention whether this is a read-only operation, if it requires specific permissions, what happens with large datasets, or if there are rate limits. The description is too minimal to provide adequate behavioral context for a data export tool.

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

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

The description is extremely concise at just 5 words, with zero wasted language. It's front-loaded with the core purpose and uses efficient phrasing. Every word earns its place in communicating the essential function.

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

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

For a data export tool with 5 parameters and no output schema, the description is insufficient. It doesn't explain what the CSV output looks like, whether headers are included, how null values are handled, or what happens when no data matches the criteria. With no annotations and no output schema, the description should provide more context about the tool's behavior and results.

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

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

Schema description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description adds no additional parameter information beyond what's in the schema, meeting the baseline expectation but not providing extra value. The description doesn't explain relationships between parameters or provide usage examples.

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

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

The description clearly states the action ('Export') and resource ('table data in CSV format'), making the tool's purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'get_table_data' which might also retrieve table data, leaving room for ambiguity about when to choose this specific export tool.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'get_table_data' or 'execute_query' for data retrieval. There's no mention of prerequisites, limitations, or specific scenarios where CSV export is preferred over other formats or methods.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't describe how it behaves: e.g., whether it returns real-time or historical data, if it requires specific permissions, what format the metrics are in, or if it has any side effects. For a diagnostic tool with zero annotation coverage, this leaves significant gaps in understanding its operation.

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

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

The description is a single, efficient sentence that directly states the tool's purpose without any wasted words. It is front-loaded with the core action ('Get') and resource, making it easy to parse. Every word earns its place by specifying 'connection pool health metrics and diagnostics'.

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

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

Given the tool's complexity (diagnostic with no parameters) and lack of annotations and output schema, the description is minimally adequate. It tells what the tool does but doesn't provide enough context for effective use, such as what metrics are returned or how to interpret them. For a health-check tool, more detail on output expectations would be helpful, but it meets a basic threshold.

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

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

The tool has 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description doesn't need to compensate for any parameter gaps, and it appropriately doesn't mention parameters. A baseline of 4 is applied since no parameter information is required, and the description doesn't mislead about inputs.

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

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

The description clearly states the tool's purpose with a specific verb ('Get') and resource ('connection pool health metrics and diagnostics'). It distinguishes itself from siblings by focusing on connection pool health rather than query performance, table data, or server info. However, it doesn't explicitly differentiate from all siblings like 'get_performance_stats' or 'get_server_info' which might overlap in monitoring domains.

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

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

The description provides no guidance on when to use this tool versus alternatives. With siblings like 'get_performance_stats', 'get_server_info', and 'detect_query_bottlenecks' that might cover related monitoring aspects, there's no indication of when this specific tool is appropriate or what scenarios it targets. Usage is implied only by the name and description.

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

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

With no annotations, the description carries full responsibility for behavioral disclosure. It only says 'get' index recommendations, implying a read-only operation, but does not explain whether it performs heavy analysis, requires special permissions, or has any side effects. This leaves significant ambiguity for a tool with four parameters.

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

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

The description is a single concise sentence with no filler words. However, it is somewhat underspecified; brevity is achieved at the cost of missing behavioral and usage context.

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

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

The tool has four optional parameters but no output schema and no annotations. The description does not explain return structure, how parameters interact, or typical usage scenarios. For an optimization tool that may have performance implications, this is incomplete.

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

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

The input schema has 100% parameter description coverage, so the schema already documents each parameter clearly. The description adds no additional meaning beyond what the schema provides, meeting the baseline but not exceeding it.

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

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

The description clearly states the tool gets index recommendations for database optimization, using the verb 'get' and a specific resource. It is distinguishable from broader optimization tools like get_optimization_insights, though it does not explicitly contrast with siblings.

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

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

No guidance is provided about when to use this tool versus alternatives such as analyze_query_performance or detect_query_bottlenecks. The phrase 'for database optimization' gives only general context, not actionable direction.

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

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

With no annotations provided, the description must carry the full burden, but it only states a generic outcome. It does not disclose whether the operation is read-only, what permissions are needed, or any side effects. The parameter-level note about analysis_period is in the schema, not the main description, so it doesn't count here.

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

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

The main description is a single concise sentence with no fluff, and the schema's detailed parameter descriptions are well-structured. It earns high marks for brevity, though the generic wording makes it slightly less impactful than it could be.

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

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

Given the lack of an output schema and the presence of several closely related sibling tools, the description is incomplete. It fails to explain what kind of 'insights' are returned, how they differ from get_index_recommendations or get_performance_stats, or any practical usage context. The parameter-level details help but do not compensate for this gap.

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

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

Schema description coverage is 100%, so both parameters are already well documented in the input schema. The main description adds no extra meaning beyond what the schema provides, which aligns with the baseline of 3 for high schema coverage.

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

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

The description uses a clear verb ('Get') and resource ('database optimization insights and health analysis'), so an agent understands what the tool does. However, it does not distinguish this tool from siblings like get_index_recommendations or analyze_query_performance, so it lacks differentiation.

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

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

The description provides no guidance on when to use this tool versus alternatives. No context, no exclusions, and no mention of sibling tools, leaving the agent to guess based on the generic wording.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'overall performance statistics and health summary', implying a read-only operation, but fails to specify details like response format, data freshness, or any limitations (e.g., rate limits, authentication needs). This leaves significant gaps for a tool that likely returns critical system data.

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

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

The description is a single, efficient sentence that directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, 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.

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

Given the tool's moderate complexity (performance statistics with a timeframe parameter) and lack of annotations or output schema, the description is minimally adequate. It covers the basic purpose but lacks details on behavior, output format, and differentiation from siblings, leaving room for improvement in completeness.

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

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

The input schema has 100% description coverage, fully documenting the single parameter 'timeframe' with its enum values and meaning. The description adds no additional parameter information beyond what the schema provides, so it meets the baseline score of 3 for adequate but not enhanced semantics.

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

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

The description clearly states the tool's purpose with a specific verb ('Get') and resource ('overall performance statistics and health summary'), making it easy to understand what it does. However, it doesn't explicitly differentiate from sibling tools like 'get_query_performance' or 'get_connection_health', 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.

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_query_performance' or 'get_connection_health'. It lacks any context about prerequisites, exclusions, or specific scenarios where this tool is preferred, leaving the agent to infer usage from the name alone.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but lacks critical details such as whether this is a read-only operation, potential side effects, rate limits, authentication requirements, or the format of the returned breakdown. For a tool with no annotations, this is a significant gap in transparency.

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

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

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly, earning the highest score for conciseness.

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

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

Given the complexity of query performance analysis, no annotations, and no output schema, the description is incomplete. It doesn't explain what 'detailed breakdown' entails, the structure of returned data, or how it differs from similar sibling tools. This leaves the agent with insufficient context to use the tool effectively beyond basic invocation.

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

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

The input schema has 100% description coverage, fully documenting all three optional parameters (limit, tool_filter, slow_only) with their types and defaults. The description adds no parameter-specific information beyond what the schema provides, so it meets the baseline score of 3 for high schema coverage without adding extra value.

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

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

The description clearly states the action ('Get') and resource ('detailed query performance breakdown by tool'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'analyze_query_performance' or 'get_performance_stats', which appear related, so it misses the highest score for sibling differentiation.

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

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

The description provides no guidance on when to use this tool versus alternatives. With multiple sibling tools like 'analyze_query_performance' and 'get_performance_stats' that seem related to performance analysis, there's no indication of context, prerequisites, or exclusions, 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.

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 retrieving 'configuration, status, and logging information,' which implies a read-only operation, but doesn't specify permissions needed, rate limits, or what the output format looks like. This leaves gaps for a tool that could return sensitive or 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.

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 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.

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 for a tool that retrieves server information. It lacks details on what specific configuration or status data is returned, how logging information is formatted, or any behavioral traits like error handling. This leaves significant gaps for an AI agent to understand the full context.

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

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

The input schema has 100% description coverage, with one optional parameter ('include_logs') well-documented in the schema. The description doesn't add any parameter-specific details beyond what the schema provides, so it meets the baseline score of 3 for high schema coverage without extra value.

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

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

The description clearly states the verb ('Get') and resource ('MCP server configuration, status, and logging information'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_connection_health' or 'get_performance_stats', which also retrieve server-related information, so it doesn't reach the highest clarity level.

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

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

The description provides no guidance on when to use this tool versus alternatives. With siblings like 'get_connection_health' and 'get_performance_stats' that might overlap in retrieving server data, there's no indication of context, prerequisites, or exclusions for this tool's use.

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

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

No annotations are provided, so the description carries the full behavioral burden. It does not explicitly state that the operation is read-only, what 'sample' means (e.g., arbitrary rows vs. first N), or how results are returned. 'Get' implies a read, but side effects and output behavior are left to inference.

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

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

The description is a single, tightly worded sentence that front-loads the core purpose and then states the optional capabilities. There is no filler or repetition.

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

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

Parameter-level guidance is fully provided by the schema, making invocation straightforward. However, there is no output schema and the description does not describe the return shape, nor does it position this tool against similar data-access siblings, leaving some context incomplete.

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

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

The input schema covers all six parameters with detailed descriptions, including defaults and a pagination caveat for offset. The tool description only summarizes 'filtering and limiting' and adds no additional meaning beyond the schema, so the baseline score of 3 applies.

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

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

The description clearly identifies the resource ('a table') and the operation ('Get sample data'), and adds the scope ('optional filtering and limiting'). It is distinguishable from siblings like describe_table and list_tables, but does not explicitly call out how it differs from execute_query or export_table_csv.

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

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

The phrase 'sample data' implies this is for lightweight table exploration, but there is no explicit when-to-use guidance or mention of alternatives such as execute_query or export_table_csv. Usage context is only implied, not stated.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. While 'List all databases' implies a read-only operation, it doesn't specify whether this requires specific permissions, how results are formatted, if there are rate limits, or what happens with large result sets. The description adds minimal behavioral context beyond the basic operation.

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

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

The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core functionality and appropriately sized for a simple listing operation with no parameters.

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

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

For a simple listing tool with no parameters and no output schema, the description is minimally complete. However, without annotations or output schema, it should ideally provide more context about the return format, permissions needed, or limitations. The description meets basic requirements but leaves gaps in behavioral understanding.

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

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

With 0 parameters and 100% schema description coverage, the baseline would be 4. The description appropriately indicates this is a parameterless operation that returns all databases, which aligns perfectly with the empty input schema.

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

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

The description clearly states the specific action ('List all databases') and the resource ('on the SQL Server instance'), which distinguishes it from siblings like list_tables or list_foreign_keys. It uses precise terminology that aligns with database administration tasks.

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

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

The description provides no guidance on when to use this tool versus alternatives like get_server_info (which might include database information) or other listing tools. There's no mention of prerequisites, context, or comparison with sibling tools.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but lacks details on permissions required, output format (e.g., list of relationships, JSON structure), pagination, or error handling. This is inadequate for a tool that likely interacts with a database schema.

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

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

The description is a single, clear sentence with zero wasted words. It front-loads the core purpose efficiently, 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.

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 output looks like (e.g., a list of foreign keys with details like table names, columns), potential side effects, or usage context in relation to sibling tools. This leaves significant gaps for an AI agent to use it effectively.

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

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

Schema description coverage is 100%, so the input schema fully documents the two optional parameters ('database' and 'schema'). The description adds no additional meaning beyond implying a scope ('in a schema'), which is already covered by the schema's descriptions. This 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.

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

The description clearly states the action ('List all') and resource ('foreign key relationships in a schema'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'describe_table' or 'list_tables', which might also provide relationship information, so it misses the top score.

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

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

The description provides no guidance on when to use this tool versus alternatives. For example, it doesn't clarify if this is for metadata exploration, performance tuning, or schema validation, nor does it mention sibling tools like 'describe_table' that might overlap in functionality.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'all tables' which implies completeness, but doesn't address pagination, permissions needed, rate limits, or what happens when database/schema parameters are omitted. For a read operation with zero annotation coverage, this leaves significant behavioral questions unanswered.

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

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

The description is a single, efficient sentence that gets straight to the point. Every word earns its place - 'List' (verb), 'all tables' (scope), 'in a specific database' (context). There's no wasted language or unnecessary elaboration for this straightforward operation.

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

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

For a simple listing tool with 2 optional parameters and 100% schema coverage, the description is minimally adequate. However, with no output schema and no annotations, it should ideally mention something about the return format (e.g., 'returns table names and metadata') or typical use cases. The description works but leaves the agent guessing about what exactly gets returned.

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

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

Schema description coverage is 100%, so the schema already fully documents both parameters. The description adds no additional parameter information beyond what's in the schema - it mentions 'specific database' which aligns with the 'database' parameter but provides no extra context about format, validation, or interaction between parameters. This 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.

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

The description clearly states the verb ('List') and resource ('tables in a specific database'), making the purpose immediately understandable. It distinguishes from siblings like 'list_databases' by specifying tables rather than databases, but doesn't explicitly differentiate from other table-related tools like 'describe_table' or 'get_table_data'.

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

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

The description provides no guidance on when to use this tool versus alternatives. With siblings like 'describe_table', 'get_table_data', and 'list_databases', there's no indication of when this listing operation is preferred over more detailed or different scoped operations. The description only states what it does, not when to use it.

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