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 the build command and directory specification but lacks details on permissions needed, whether it modifies files or creates outputs, error handling, or rate limits. For a tool with 4 parameters and 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 front-loads the core purpose without unnecessary details. Every word 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.

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

Given the tool's complexity (4 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what the build output entails (e.g., compiled files, errors), behavioral traits like side effects, or how it differs from sibling tools. This leaves gaps for an AI agent to correctly 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?

Schema description coverage is 100%, so the schema fully documents all parameters. The description adds minimal value beyond the schema by implying the build occurs in a specified directory, but it doesn't provide additional context like parameter interactions or usage examples. Baseline 3 is appropriate as the schema handles the heavy lifting.

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 ('build') and resource ('Onyx code file'), specifying the command 'onyx build' and location context. It distinguishes from siblings like 'run_onyx_code' by focusing on compilation rather than execution, though it doesn't explicitly contrast with 'onyx_pkg_build' which might have overlapping functionality.

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 explicit guidance on when to use this tool versus alternatives is provided. The description implies usage for building Onyx code, but it doesn't mention when to choose this over 'run_onyx_code' (for execution) or 'onyx_pkg_build' (for package builds), leaving the agent to infer context without clear exclusions or prerequisites.

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 burden but only states what the tool does without behavioral details. It doesn't disclose if this is a read-only operation, how it handles errors, rate limits, or authentication needs, which are critical for a tool interacting with GitHub.

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 waste, front-loaded with the core purpose. It's appropriately sized for a simple retrieval tool, 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. It doesn't explain what 'definitions and examples' include (e.g., code snippets, metadata), return format, or error handling, leaving gaps for a tool with external dependencies like GitHub.

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 fully documents both parameters. The description adds no additional meaning beyond implying a search capability with 'functionName', but doesn't clarify semantics like search behavior or example types. Baseline 3 is appropriate as the schema does the heavy lifting.

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 ('Onyx function definitions and examples'), specifying the source ('from GitHub'). It distinguishes from siblings like 'search_onyx_docs' or 'search_github_examples' by focusing on function definitions, but could be more specific about what 'definitions' entail (e.g., code, metadata).

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 explicit guidance on when to use this tool versus alternatives like 'search_ithub_examples' or 'get_onyx_structs' is provided. The description implies a retrieval function, but lacks context on scenarios or prerequisites, leaving usage unclear relative to siblings.

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 the tool retrieves data from GitHub but doesn't mention rate limits, authentication needs, error handling, or the format of returned data (e.g., raw JSON, structured examples). This leaves significant gaps for a tool interacting with an external service.

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 waste. It's front-loaded with the core purpose and avoids unnecessary details, 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 the complexity of fetching data from GitHub, lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'definitions and examples' entail, how results are structured, or any behavioral traits like pagination or errors, leaving the agent under-informed for 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%, so the input schema already documents both parameters ('structName' and 'limit') with descriptions and defaults. The description adds no additional meaning beyond implying a search functionality, which is already suggested by the schema. Baseline 3 is appropriate as the schema does the heavy lifting.

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 ('Onyx struct definitions and examples from GitHub'), making the purpose understandable. However, it doesn't distinguish this tool from sibling tools like 'get_onyx_functions' or 'search_github_examples', which likely have overlapping domains, so it misses full 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 siblings like 'get_onyx_functions', 'search_github_examples', and 'search_onyx_docs', there's no indication of scope, 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. It states the tool lists repositories but doesn't disclose behavioral traits such as whether it's a read-only operation, potential rate limits, authentication needs, pagination behavior, or what 'discovered' implies (e.g., cached vs. real-time). This leaves significant gaps for safe and effective use.

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 ('List all discovered GitHub repositories with Onyx code'). It wastes no words and is appropriately sized for a simple listing tool.

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 low complexity (1 optional parameter, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks details on behavior, usage context, or output format, which are needed for full completeness. The high schema coverage helps, but gaps in guidelines and transparency keep it at a baseline level.

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 a well-documented 'sortBy' parameter including enum values and a default. The description adds no parameter-specific information beyond what the schema provides, so it meets the baseline of 3 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 ('List') and resource ('GitHub repositories'), and specifies the scope ('with Onyx code'). However, it doesn't differentiate from sibling tools like 'search_github_examples' or 'search_all_sources' that might also involve GitHub repositories, leaving some ambiguity about when this specific listing tool is preferred.

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 'search_github_examples' or 'search_all_sources'. It mentions 'discovered' repositories but doesn't explain what that means or any prerequisites for usage, leaving the agent to guess about context and exclusions.

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. It states the tool builds a package, implying a write/mutation operation, but does not disclose behavioral traits like whether it modifies files, requires specific permissions, has side effects, or handles errors. This is inadequate for a build 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.

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 and method. It is front-loaded with the core action and includes no unnecessary details, 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.

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

Given the complexity of a build operation, no annotations, and no output schema, the description is incomplete. It lacks information on what the build does (e.g., compiles code, creates artifacts), potential outputs, error handling, or dependencies, which are critical for an AI agent to use this 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 100%, so the schema already documents both parameters ('directory' and 'timeout') with descriptions and defaults. The description does not add any meaning beyond what the schema provides, such as explaining the build process or parameter interactions, but the baseline is 3 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 action ('Build an Onyx package') and the method ('using "onyx pkg build"'), which is specific and actionable. However, it does not explicitly differentiate from sibling tools like 'build_onyx_code' or 'run_onyx_code', leaving some ambiguity about when to use this versus those alternatives.

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 'build_onyx_code' or 'run_onyx_code'. It mentions the directory parameter but does not specify prerequisites, such as requiring an Onyx package structure or dependencies, or 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states that the tool executes code and returns output/errors, but it lacks details on execution environment, security implications, error handling, or performance characteristics. For a code execution tool without 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 function and purpose without unnecessary words. It is front-loaded with the core action and outcome, making it easy to understand quickly. Every part of the sentence earns its place by conveying essential 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 tool's complexity (code execution with potential side effects), lack of annotations, and no output schema, the description is minimally adequate. It covers the basic purpose but fails to address critical aspects like execution safety, error formats, or output structure. For a tool in this context, more completeness 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?

The input schema has 100% description coverage, clearly documenting the parameters (code, filename, timeout) with their types and defaults. The description does not add any additional semantic meaning beyond what the schema provides, such as explaining parameter interactions or constraints. Baseline score of 3 is appropriate as the schema handles the heavy lifting.

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 Onyx code') and the outcome ('return the output/errors for testing and debugging'), making the purpose evident. However, it does not explicitly differentiate this tool from its sibling 'build_onyx_code', which might also involve code execution or compilation, leaving some ambiguity in 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 mentions 'for testing and debugging', which provides a general context for usage, but it does not specify when to use this tool versus alternatives like 'build_onyx_code' or 'run_wasm'. No explicit guidance on prerequisites, exclusions, or comparisons with siblings is provided, limiting its utility for decision-making.

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 action without disclosing behavioral traits. It doesn't mention execution environment, permissions needed, side effects, error handling, or output format. For a tool that executes code, this lack of transparency is a significant gap.

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

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

The description is a single, efficient sentence that front-loads the core action and includes the command syntax. There's zero waste—every word contributes directly to understanding the tool's purpose and usage.

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 code execution tool with no annotations and no output schema, the description is incomplete. It lacks details on execution behavior, safety, output, or error handling. Given the complexity of running WASM files, more context is needed to help an agent use this 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 100%, so the schema fully documents all three parameters. The description adds no parameter-specific information beyond implying 'wasmPath' in the command example. This meets the baseline of 3 since the schema does the heavy lifting, but the description doesn't enhance understanding of parameters.

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 ('a WebAssembly (WASM) file'), specifying the exact command used ('onyx run file.wasm'). It distinguishes from siblings like 'run_onyx_code' by focusing on WASM files rather than general Onyx code. However, it doesn't explicitly contrast with all siblings, so it's not a perfect 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 no guidance on when to use this tool versus alternatives like 'run_onyx_code' or other siblings. It mentions the command syntax but doesn't explain use cases, prerequisites, or exclusions, leaving the agent to infer usage from context 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. It mentions 'crawled content' but doesn't disclose behavioral traits such as whether this is a read-only operation, how results are ranked, if there are rate limits, or what the output format looks like. The description is too vague to provide meaningful behavioral context 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.

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

The description is a single, efficient sentence that front-loads the core action ('Search all crawled content') with clarifying examples. There is zero waste, and every word earns its place by specifying the scope and types of content.

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 a search tool with 3 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns, how results are structured, or any limitations (e.g., search scope, performance). The agent lacks sufficient context to use this 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?

Schema description coverage is 100%, so the schema already documents all parameters (query, sources, limit) with descriptions and defaults. The description adds no additional meaning beyond what the schema provides, such as explaining how the query is processed or what 'sources' like 'docs' and 'github' entail. 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.

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

The description clearly states the verb ('Search') and resource ('all crawled content') with specific examples ('docs, GitHub, URLs'). It distinguishes from siblings like search_github_examples and search_onyx_docs by indicating it searches across multiple sources. However, it doesn't explicitly mention what 'crawled content' entails beyond the examples.

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 search_github_examples or search_onyx_docs. It mentions 'all crawled content' but doesn't specify if this is for broad searches versus more targeted ones, leaving the agent to infer usage from the tool 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. While it states the search action, it doesn't describe what the tool returns (e.g., format, structure), whether it performs real-time queries or uses cached data, or any limitations like rate limits or authentication requirements for GitHub access.

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 with the core functionality and appropriately sized for a simple search tool.

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 search results look like (e.g., list of examples with metadata), how results are sorted or filtered, or any behavioral nuances. For a search tool with no structured output documentation, this leaves significant gaps for 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 input schema has 100% description coverage, clearly documenting both parameters ('topic' and 'limit') with their types and purposes. The description adds no additional parameter information beyond what the schema provides, so it meets the baseline of 3 for adequate 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 ('Search') and target ('Onyx code examples from GitHub repositories by topic'), making the purpose understandable. However, it doesn't differentiate this tool from sibling tools like 'search_all_sources' or 'search_onyx_docs', which appear to have overlapping search functionality but different targets.

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 sibling tools like 'search_all_sources' and 'search_onyx_docs' available, there's no indication of what makes this tool distinct or when it should be preferred over those options.

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 but offers minimal information. It states the search scope ('official Onyx programming language documentation') but doesn't describe response format, pagination, error conditions, authentication needs, or rate limits. For a search tool with zero annotation coverage, this leaves significant behavioral gaps.

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 appropriately sized for a simple search tool and front-loads the essential information (search action and target resource).

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 search tool with 2 parameters (100% schema coverage) but no annotations and no output schema, the description is minimally adequate. It specifies the search domain but lacks information about return values, error handling, and behavioral constraints. The description meets basic requirements but doesn't compensate for the missing structured data.

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

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 ('query' and 'limit') adequately. The description doesn't add any parameter-specific information beyond what's in the schema, such as query syntax examples or limit constraints. 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.

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

The description clearly states the verb ('Search') and resource ('official Onyx programming language documentation'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'search_all_sources' or 'search_github_examples', but the specificity about 'Onyx programming language documentation' provides some implicit 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 like 'search_all_sources' or 'search_github_examples'. It doesn't mention prerequisites, limitations, or comparative advantages, leaving the agent to infer usage context from the tool name alone.

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