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 and does so exceptionally well. It explicitly warns about the sandbox environment ('⚠️ SANDBOX ONLY - executes arbitrary code'), discloses output handling behavior ('🛡️ Auto-truncates large outputs'), and provides practical implementation guidance. This goes well beyond what the input schema provides about 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 perfectly sized and front-loaded with the core purpose. Every sentence earns its place: the first states the primary function, the second provides critical warnings, and the third offers practical implementation advice. The emoji usage enhances scannability without adding fluff.

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

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

For a complex tool that executes arbitrary code with 5 parameters and no output schema, the description provides excellent context about behavior, warnings, and practical usage. The only minor gap is the lack of information about return values or error handling, which would be helpful given there's no output schema. However, the description covers the most critical aspects well.

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 100% schema description coverage, the input schema already thoroughly documents all 5 parameters. The description adds some value by reinforcing the script parameter's support for file placeholders, but doesn't provide additional semantic context beyond what's already in the schema descriptions. This meets the baseline expectation when schema coverage is complete.

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 ('Execute server-side JavaScript in ServiceNow using Background Scripts') and resource ('ServiceNow'), distinguishing it from sibling tools like execute_table_operation and execute_updateset_operation which likely operate on different resources. It provides a precise verb+resource combination that leaves no ambiguity about what the tool does.

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

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

The description provides clear context for when to use this tool ('Execute server-side JavaScript in ServiceNow') and includes practical guidance for handling large scripts ('Use {{file:path}} for large scripts'). However, it doesn't explicitly state when NOT to use it or directly compare it to sibling alternatives, which prevents a perfect score.

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 and does so comprehensively. It explicitly warns about data modification in sandbox environments, mentions auto-limiting of large results, provides pagination advice, and describes file placeholder support. This covers critical behavioral aspects like safety, performance, and data handling that aren't in the 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 efficiently structured with key information front-loaded (CRUD operations, API type, supported methods) followed by important warnings and usage tips. The use of emojis helps visually organize critical points. While slightly dense due to the tool's complexity, every sentence adds value without redundancy.

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

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

For a complex tool with 16 parameters, no annotations, and no output schema, the description provides substantial context about behavioral traits, safety warnings, and usage patterns. It covers the tool's scope, data modification implications, performance considerations, and file handling. The main gap is the lack of output format description, but given the tool's complexity, the description does remarkably well.

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%, so the schema already documents all 16 parameters thoroughly. The description adds some context about query syntax, batch operations, and file placeholders, but doesn't provide additional parameter-specific semantics beyond what's in the schema descriptions. This meets the baseline expectation when schema coverage is complete.

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 performs 'CRUD operations on ServiceNow tables via Table API' with specific HTTP methods (GET/POST/PUT/PATCH/DELETE) and mentions query syntax and batch operations. It distinguishes itself from sibling tools like 'execute_background_script' and 'execute_updateset_operation' by focusing on table operations rather than scripts or updatesets.

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

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

The description provides clear context for usage with warnings like '⚠️ SANDBOX ONLY - reads/modifies data' and guidance on handling large datasets ('Use pagination for big datasets'). However, it doesn't explicitly state when to use this tool versus the sibling tools, though the different focus areas (tables vs. scripts vs. updatesets) are implied.

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 does well by warning about sandbox-only usage and modification of update sets, mentioning auto-limiting of large results, and providing pagination guidance. It also hints at file handling capabilities. However, it doesn't cover all behavioral aspects like error handling, authentication requirements, or rate limits, which prevents a perfect score.

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

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

The description is appropriately sized and front-loaded with the core purpose. The warning icons and file handling note are useful additions. However, the structure could be slightly improved by separating the core purpose from the usage notes more clearly, and some phrasing ('large data' vs 'big datasets') is slightly redundant.

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 complex tool with 18 parameters, no annotations, and no output schema, the description provides good basic context about sandbox restrictions and data handling. However, it doesn't explain what the tool returns (no output schema), doesn't cover all behavioral aspects, and doesn't differentiate from sibling tools. Given the complexity, more comprehensive guidance would be expected.

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%, so the schema already documents all 18 parameters thoroughly. The description doesn't add any meaningful parameter semantics beyond what's in the schema - it mentions file placeholders but this is already covered in the 'data' parameter description. This meets the baseline expectation when schema coverage is high.

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 manages ServiceNow update sets with specific operations (lifecycle, XML reassignment, working set tracking). It specifies the resource (ServiceNow update sets) and general action verbs (manage, track). However, it doesn't explicitly differentiate from sibling tools like execute_background_script or execute_table_operation, 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 clear usage context with '⚠️ SANDBOX ONLY - modifies update sets' warning and guidance for handling large datasets ('Use pagination for big datasets'). It also offers file handling advice ('📁 Use {{file:path}} for large data'). However, it doesn't explicitly mention when to use this tool versus the sibling tools (execute_background_script, execute_table_operation), which would be needed for a perfect score.

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