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

With no annotations provided, the description carries full burden but only states basic functionality without disclosing behavioral traits like rate limits, authentication needs, response formats, or potential errors. It mentions optional model selection but doesn't explain implications or defaults, leaving gaps 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 extremely concise with two short sentences that directly state the tool's purpose and required inputs. Every word earns its place, and it's front-loaded with the core functionality, making it efficient and well-structured.

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 AI text generation, no annotations, and no output schema, the description is incomplete. It lacks details on response handling, error cases, model defaults (though schema covers this), and behavioral aspects like token limits or safety considerations, making it inadequate for full contextual 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?

Schema description coverage is 100%, so the schema already documents all parameters (prompt, model, maxTokens). The description adds minimal value by mentioning 'prompt and optional model name' but doesn't provide additional meaning beyond the schema, such as prompt best practices or model selection guidance.

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 ('Generate text') and the resource/technology ('using Google Gemini'), which is specific and unambiguous. However, it doesn't differentiate from sibling tools (none of which are text generation tools), so it doesn't reach the highest score of 5.

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 minimal guidance with 'Provide a prompt and optional model name,' but lacks explicit when-to-use instructions, alternatives, or context about when this tool is preferred over others. No sibling tools are text generators, so differentiation isn't needed, but general usage context is missing.

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 describes what the tool does (fetches and returns .d.ts text) but lacks details on behavioral traits such as error handling (e.g., what happens if a package doesn't exist), performance (e.g., rate limits or timeouts), or side effects (e.g., whether it caches results or makes network calls). The description is functional but misses key operational context.

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: the first sentence states the core functionality, and the second provides usage context. Every sentence earns its place with no redundant information, making it efficient and easy to parse.

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

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

Given the tool's moderate complexity (fetching TypeScript definitions for dependencies), no annotations, no output schema, and low schema description coverage, the description is incomplete. It covers the purpose and usage well but lacks details on parameters, behavioral traits, and output format (beyond mentioning '.d.ts text'). This leaves gaps for an AI agent to fully understand how to invoke and interpret 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?

The input schema has 0% description coverage, so the description must compensate. It mentions 'array of npm package names (and optional versions)', which aligns with the 'dependencies' parameter in the schema. However, it doesn't explain the structure (e.g., that 'dependencies' is an array of objects with 'name' and optional 'version'), provide examples, or detail constraints (e.g., format of package names). This adds minimal semantic value beyond the bare 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 tool's purpose: 'fetch whether each package ships its own TypeScript definitions or has a corresponding @types/… package, and return the raw .d.ts text.' This specifies the verb (fetch/return), resource (TypeScript definitions), and output (raw .d.ts text). However, it doesn't explicitly distinguish this tool from its siblings (like ai_generate or run_js), which are unrelated but still 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?

The description provides clear context for when to use this tool: 'Useful when you're about to run a Node.js script against an unfamiliar dependency and want to inspect what APIs and types it exposes.' This gives a specific scenario (inspecting dependencies before running a script) but doesn't explicitly state when not to use it or mention alternatives among the sibling tools, which are unrelated to dependency analysis.

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 and discloses key behavioral traits: it requires manual cleanup ('manually stop the sandbox to free resources'), specifies execution environment constraints ('valid ESModules', 'Node.js processes'), and describes persistence mechanisms ('mounted volume', './files directory'). It doesn't mention error handling, timeouts, or resource limits, but covers essential operational aspects.

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 (4 sentences) and front-loaded with core functionality. Every sentence adds value: first states purpose, second covers cleanup requirement, third provides usage context and ESModules requirement, fourth explains file system constraints. Minor redundancy exists in mentioning 'Node.js processes' after 'JavaScript code'.

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 4 parameters, no annotations, and no output schema, the description provides substantial context about execution environment, persistence, cleanup, and sibling differentiation. It lacks details about return values/output format and error cases, but covers most operational aspects needed for effective use given the structured data available.

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 parameters thoroughly. The description adds minimal parameter-specific context beyond the schema, mainly reinforcing that code must be ESModules and dependencies are npm packages. It doesn't provide additional syntax examples or constraints not already in schema descriptions.

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 specific verbs ('Install npm dependencies and run JavaScript code') and resource ('inside a running sandbox container'). It distinguishes from siblings like 'run_js_ephemeral' by emphasizing environment reuse across multiple executions, and from 'sandbox_exec' by specifying JavaScript/ESModules context.

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 explicit guidance on when to use this tool ('Best for complex workflows where you want to reuse the environment across multiple executions') and when not to (implied by mentioning ephemeral alternatives). It also states prerequisites ('After running, you must manually stop the sandbox to free resources') and file system constraints ('always need to read from and write to the "./files" directory').

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 effectively describes key behavioral traits: the ephemeral/disposable nature, automatic cleanup, ESModules requirement, file persistence rules (read/write to './files' directory), and support for various file types. It doesn't mention execution time limits, error handling, or output format, which keeps it from 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. Every sentence adds value: the first defines the tool, the second specifies ESModules requirement and use case, the third explains file persistence rules, and the example illustrates usage. It could be slightly more concise by integrating the file persistence note with the example.

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 (ephemeral execution with dependencies) and no annotations or output schema, the description does well to cover purpose, usage, behavioral traits, and provide an example. It lacks details on execution limits, error responses, or output structure, but for a tool with rich schema coverage and clear context, it's largely complete.

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 three parameters thoroughly. The description adds minimal parameter semantics beyond the schema—it implies the 'code' parameter must be valid ESModules and mentions the './files' directory context, but doesn't provide additional syntax or format details. 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 tool's purpose with specific verbs ('run', 'clean up') and resources ('JavaScript snippet', 'temporary disposable container', 'npm dependencies'). It distinguishes from siblings like 'run_js' (which likely lacks the ephemeral/cleanup aspect) and 'sandbox_exec' (which may require manual sandbox management).

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 explicitly states when to use this tool ('ideal for simple one-shot executions without maintaining a sandbox or managing cleanup manually') and provides clear context for alternatives. It distinguishes from siblings by emphasizing the ephemeral nature and automatic cleanup, which contrasts with tools like 'sandbox_exec' that likely require manual sandbox management.

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 but only states the prerequisite. It lacks details on behavioral traits such as execution environment, error handling, output format, or security implications (e.g., destructive potential of shell commands). This is a significant gap for a tool that executes commands.

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 two sentences, front-loaded with the core action, and every word earns its place without redundancy. It's efficiently structured and appropriately sized for the tool's complexity.

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

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

Given the complexity of executing shell commands in a container, no annotations, and no output schema, the description is incomplete. It misses critical details like what the tool returns, how errors are handled, or execution limits. This inadequately supports an AI agent in using the tool 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 0%, so the description must compensate, but it doesn't explain parameters beyond what the schema implies. It mentions 'shell commands' and 'sandbox container', which loosely map to 'commands' and 'container_id', but adds no syntax, format, or constraints. Baseline 3 is appropriate as the schema defines parameters clearly.

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 shell commands') and the target ('inside a running sandbox container'), which is specific and actionable. It distinguishes from siblings like 'sandbox_initialize' by focusing on execution rather than setup, though it doesn't explicitly contrast with 'run_js' tools.

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?

It provides clear context by stating 'Requires a sandbox initialized beforehand', which implicitly guides when to use this tool versus 'sandbox_initialize'. However, it doesn't explicitly mention alternatives like 'run_js' for non-shell commands or exclusions for when not to use it.

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 creating an 'isolated Docker container' and a 'sandbox session for multiple commands,' which hints at a persistent environment, but fails to detail critical aspects like resource limits, session lifecycle, cleanup behavior, or error handling. This leaves significant gaps for a tool that likely involves system-level operations.

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 two sentences, front-loaded with the core purpose and followed by usage context. Every word earns its place with zero redundancy, making it highly efficient and 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 the complexity of initializing a Docker container with Node.js, no annotations, no output schema, and incomplete parameter documentation (50% coverage), the description is insufficient. It lacks details on what the tool returns (e.g., container ID, session handle), error conditions, or operational constraints, making it inadequate for safe and effective use by an AI agent.

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 50% (only the 'port' parameter has a description), and the description adds no additional parameter information beyond what the schema provides. It doesn't explain the 'image' parameter (e.g., default values or constraints) or clarify the relationship between parameters. Since schema coverage is moderate, the baseline score of 3 is appropriate, as the description doesn't compensate for the gaps.

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 ('Start a new isolated Docker container') and resource ('running Node.js'), with the specific purpose of setting up a sandbox session for multiple commands. However, it doesn't explicitly differentiate from sibling tools like 'sandbox_exec' or 'sandbox_stop', 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 implies usage context ('Used to set up a sandbox session for multiple commands and scripts'), suggesting this tool initiates a session while others like 'sandbox_exec' might operate within it. However, it lacks explicit guidance on when to use this versus alternatives (e.g., 'run_js_ephemeral' for one-off scripts) or any exclusions, leaving room for ambiguity.

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 clearly indicates destructive behavior ('Terminate and remove'), which is helpful. However, it lacks details on potential side effects (e.g., data loss, cleanup processes), error conditions, or confirmation requirements, leaving gaps for a mutation 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 two sentences with zero waste—each sentence adds critical information (action and usage context). It is appropriately sized and front-loaded with the core purpose, making it highly efficient.

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 (destructive operation with one parameter), no annotations, and no output schema, the description is reasonably complete. It covers purpose and usage well but could improve by addressing behavioral aspects like data persistence or error handling, which are relevant for a cleanup tool.

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 has 0% description coverage, so the description must compensate. It doesn't explicitly mention the 'container_id' parameter, but the context ('a running sandbox container') implicitly clarifies what this parameter refers to. Since there's only one parameter, the baseline is high, but the lack of explicit parameter discussion slightly reduces the score.

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 ('Terminate and remove') and resource ('a running sandbox container'), distinguishing it from sibling tools like sandbox_initialize and sandbox_exec. It precisely defines what the tool does without being vague or tautological.

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

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

The description explicitly states when to use this tool ('after finishing work in a sandbox initialized with sandbox_initialize') and implies when not to use it (e.g., while still working in the sandbox). It provides clear context and references a specific alternative/sibling tool for setup.

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