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 explains the return structure (dictionary with analysis summary, risk flags, evidence sources) and mentions evidence gathering, but it does not disclose other behavioral traits such as whether any external calls are made, potential rate limits, or side effects. Basic transparency is present but lacks depth.

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 concise and well-structured with separate sections for purpose, arguments, and returns. Each sentence provides essential information without unnecessary verbosity. It is appropriately front-loaded with the core purpose.

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 simplicity (2 parameters, one required) and the presence of an output schema, the description covers the essential aspects well. It explains the parameters and the general return shape. It could be more complete by noting any limitations or when not to use it, but it is sufficient for a basic 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 0% description coverage, so the description must compensate. It does so effectively by explaining that `patches` is a sequence of patch dictionaries containing `name` and `diff_content`, and that `subsystem_hint` is an optional initial assumption. This adds significant meaning beyond the bare schema types.

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 action ('Analyze') and resource ('a series of kernel patches'), and specifies the analysis dimensions (risks, subsystems, and evidence). This distinguishes it from siblings like get_patch_details (single patch) or analyze_subsystem_risk (subsystem-level risk), as it focuses on a series of patches.

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 for analyzing multiple patches via the phrase 'series of kernel patches', but it does not explicitly state when to use this tool versus alternatives. There is no mention of exclusions, prerequisites, or when a different tool would be more appropriate. Guidance is present but not explicit.

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 discloses the return structure (dictionary with specific keys) and parameter default for time_range. It does not explicitly mention side effects or performance implications, but for an analysis tool this is adequate. The addition of return format goes beyond a vague 'analyze' statement.

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 concise and well-structured: a one-sentence summary, followed by Args and Returns sections. No unnecessary words or repetition of schema info. It is easily scannable for an AI agent.

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 description covers the tool's purpose, parameters, and return values completely enough for an agent to select and invoke it. However, it doesn't explain how to interpret the risk_score or stability_rating values, and it lacks usage context. Given the output schema exists, the return format is sufficiently disclosed. Minor gap: no mention of when not to use this.

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% because the schema only has title fields. The description compensates by providing meaningful semantics for both parameters: examples for subsystem ('mm', 'sched', 'net') and the default for time_range ('30d'). This is essential for correct invocation.

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 specific verb ('Analyze') and resource ('kernel subsystem'), and clearly states the tool's purpose: 'Analyze overall engineering risk and stability.' This distinguishes it from sibling tools that are search/lookup oriented, making it obvious when to select this 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?

Usage is implied by the description and the tool's distinct purpose, but there is no explicit guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites. The description doesn't say 'use this when you need a risk assessment' or mention 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?

No annotations are provided, so the description must carry the transparency burden. It states the return type ('Dictionary with line range attributions and evidence sources') and uses 'Get' to imply a read operation. However, it does not disclose potential limitations, performance characteristics, or error handling, which are relevant for a blame 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 compact and orderly: a one-line purpose, an Args list, and a Returns note. No redundant sentences; every line contributes information, making it highly scannable.

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 description covers the core purpose, all parameters, and return type, which is sufficient for a simple read tool. However, it lacks usage guidance and caveats (e.g., behavior with invalid line ranges or when to prefer get_file_history), so it is not fully complete given the absence of annotations.

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 no descriptions, so the description fully compensates by explaining each parameter: file_path as a relative kernel tree path, start_line and end_line as 1-indexed integers, and repo as an optional filter. This adds meaningful context beyond the raw schema types and titles.

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 'Get commit attribution for a specific line range in a file,' which identifies the tool's specific verb, resource, and scope. This distinguishes it from siblings like get_file_history (file-level history) and search_commits (query-based search).

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?

There is no guidance on when to use this tool versus alternatives. The description is purely functional, with no mention of context, exclusions, or sibling tools, leaving the agent to infer usage independently.

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 burden. It adds useful context: SHA can be a prefix, and the return is a dictionary with metadata and evidence sources. However, it does not disclose edge cases (e.g., ambiguous prefix, invalid SHA), failure modes, or any permissions/rate limits. The return type is only briefly mentioned.

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 front-loaded with the core purpose, followed by concise Args and Returns sections. Every sentence serves a clear informational purpose with no 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?

With only 2 parameters, no annotations, and an output schema present, the description covers the essential elements: what the tool does, the inputs, and the return shape. It is sufficient for a simple metadata lookup, though it could mention ambiguous SHA resolution or the specific metadata fields returned (though output schema covers the latter).

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%, but the description explicitly explains both parameters: 'sha: Complete or prefix commit SHA' and 'repo: Optional repository name filter.' This adds meaning beyond the schema's bare type definitions, though it lacks examples or format details.

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 states a specific verb+resource: 'Retrieve detailed metadata for a specific kernel commit SHA.' This clearly distinguishes it from siblings like search_commits (search vs. retrieving a specific commit) and get_patch_details (different resource).

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 when you already have a commit SHA ('specific kernel commit SHA') and mentions an optional repo filter, but it does not explicitly state when not to use it or name alternative tools. No exclusions or alternatives are provided.

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. It discloses the return format (a dictionary with title, sections, and evidence_sources) which is helpful, but it does not mention error behavior, permissions, rate limits, or any side effects. The description is functionally clear but lacks deeper behavioral 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 concise and well-structured, using a standard docstring format with a one-line purpose, an Args block with an example, and a Returns block. Every sentence earns its place, and there is no 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 simple single-parameter retrieval tool with an output schema, the description is fairly complete: it states what is retrieved and the shape of the return value. It does not explain when to use it relative to siblings, but that is a minor gap given the simplicity and clarity of the purpose.

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 explicitly explains the only parameter, doc_id, as a 'Document file path' and gives a concrete example ('Documentation/net/core.rst'). This adds substantial meaning beyond the input schema, which has no property description for doc_id.

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 the specific verb 'Retrieve' and clearly states the resource: 'full content and section headings for a kernel document.' This distinguishes the tool from sibling search tools like search_docs and lookup_symbol, which are search-oriented rather than retrieval of a full document.

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 the tool is used when one already has a doc_id (a file path) and wants the full content, but it does not explicitly state when to use it over alternatives, nor does it mention any prerequisites or exclusions. It provides context but no comparative guidance.

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 discloses the return type (dictionary with commit history and evidence sources), which is useful. However, it does not explicitly confirm this is a read-only operation, nor does it mention ordering, pagination, error behavior, or performance implications, leaving some ambiguity.

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 brief and well-structured: a purpose statement, a bulleted args list, and a returns line. Every sentence contributes meaning, no filler, and key information is front-loaded.

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?

With only 3 parameters and a provided output schema, the description covers all essential aspects: purpose, required and optional parameters, and the general return shape. It lacks usage guidance and some behavioral details, but those are counted elsewhere. For its complexity, it is reasonably 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?

The schema has 0% description coverage, so the description must compensate. It does so by explaining all three parameters: file_path (relative path), repo (optional filter), and limit (max commits). This adds genuine meaning beyond the schema. Slight vagueness remains for 'repo filter' (format not specified), but overall it's strong.

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 specific verb ('Get') and distinct resource ('commit history and symbol evolution for a kernel file path'). This clearly differentiates it from sibling tools like search_commits (generic search) and get_commit_details (single commit), as it is file-path-centric and includes symbol evolution.

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 a clear use case (file-specific history), but it does not explicitly state when to prefer this over alternatives like blame_line_range or search_commits. It gives no exclusions or alternative guidance, only the applicable context.

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 return format (dictionary with title, author, body, evidence_sources) and mentions 'symbol references' in the purpose line, but does not disclose error handling, pagination, rate limits, or any side effects. For a simple read operation, this is adequate but minimal; it does not add rich context beyond what a typical get-by-ID tool would have.

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: three short sections (purpose, args, returns) with no filler. The main purpose is front-loaded in the first sentence, and every line contributes useful information. The structure is clean and scannable.

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 single-parameter retrieval tool, the description covers the essential aspects: what it does, the parameter, and the return shape. It does not explicitly mention when to use it (e.g., 'use when you have the article ID'), but given the tool's simplicity and the presence of a separate search tool, the completeness is strong. The output schema is noted as existing, so return details are not overly necessary, but the description includes them anyway.

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 provides only the parameter name and type (article_id: string) with no description. The description compensates by labeling it as a 'Unique LWN article ID' and giving a concrete example ('LWN-900100'), which clarifies the expected format and uniqueness. This adds meaningful 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 opens with a clear, specific verb and resource: 'Retrieve full details and symbol references for an LWN article.' This immediately distinguishes it from sibling tools like search_lwn (which searches) and lookup_symbol (which likely maps symbols), and the focus on a single article by ID is evident.

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 Args section implies that this tool requires a specific LWN article ID, suggesting it is used when you already have the ID. However, there is no explicit statement about when to use this instead of search_lwn for discovery, nor any exclusions or alternatives mentioned. The guidance is implicit rather than explicit.

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 clearly indicates a read operation ('Retrieve') and adds value by naming the return contents (metadata, transition history, evidence sources). While it doesn't discuss errors or side effects, for a getter this is sufficient.

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 compact and well-structured with clear Args and Returns sections. Every sentence adds useful information, and the key purpose is front-loaded in a single sentence.

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 single-parameter getter with an output schema available, the description covers the purpose, the parameter, and the general shape of the return value. It doesn't mention error handling or prerequisites, but these are less critical given the simplicity of the tool and the presence of an output schema.

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 only provides the parameter type and title. The description's Args section adds 'Patchwork message/patch ID', which clarifies the domain of the identifier, but it doesn't include examples or format details. Given the 0% schema description coverage, the description only partially compensates.

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 the specific verb 'Retrieve' with resource 'patch' and clarifies the scope: full metadata and state transition history. This clearly distinguishes it from sibling tools like get_commit_details or search_patches, which target different entities.

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 that the tool should be used when you have a patch_id (as indicated by the required argument), but it doesn't provide explicit context for when to choose this over alternatives like search_patches or analyze_patch_series. There are no exclusions or comparisons to other 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?

No annotations are provided, so description carries full burden. It discloses the return type (dictionary) and the inclusion of an excerpt, but lacks error behavior, permission requirements, or limitations of the excerpt.

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?

Well-structured with Args and Returns sections; concise and to the point. The Args line repeats the parameter name but adds necessary context, so no wasted words.

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 one-parameter retrieval tool with an output schema, the description provides adequate operational details. However, it lacks usage guidance and edge-case behavior, making it minimally viable but not fully 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 has 0% description coverage, but the description clearly explains message_id as 'Email message ID of the thread,' adding meaning beyond the schema's bare 'Message Id' title. Essential parameter is well-defined.

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?

Clear, specific verb 'retrieve' with resource 'LKML thread' and output details ('full details and body excerpt'). Distinguishes itself from siblings like get_doc_details and get_commit_details by its unique target.

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_lkml. No mention of prerequisites (e.g., needing a message_id from a search) or 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?

With no annotations, the description carries the full burden. It discloses the return format ('Dictionary containing location, definition, and evidence sources') but does not mention error behavior, case sensitivity, repository requirements, or read-only guarantees.

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 five lines with clear 'Args' and 'Returns' sections. It front-loads the primary purpose and every sentence contributes useful information 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?

The tool is simple with three parameters, one required, and the description covers purpose, parameters, and return shape. It lacks explicit edge-case behavior (e.g., symbol not found) but is largely complete for a lookup operation. The output schema further reduces the need to detail return values.

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 Args section explains all three parameters, adding meaning absent from the schema's bare property names. It clarifies that kind and repo are optional filters, and repo filters by repository, which meaningfully goes beyond the schema structure.

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 states 'Look up a specific symbol (function, struct, macro) in kernel source,' which clearly identifies the action, resource, and scope. It distinguishes from sibling search tools by emphasizing exact symbol lookup rather than broad search.

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 'specific symbol' implies use when the exact name is known, but it does not explicitly state when to use this tool versus alternatives like search_kernel_code. No exclusions or alternative tool references are provided.

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 must disclose behavior. It states that the search covers commit messages, author, and subsystem, and returns a dictionary with evidence sources. However, it does not mention rate limits, authorization, or whether results are limited or sorted. The description is adequate for a read-only search but lacks deeper behavioral detail.

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 front-loaded with a clear one-line purpose, followed by a structured Args list and Returns summary. Every sentence adds value with no unnecessary fluff, and the formatting makes it easy to scan.

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 simplicity and the presence of an output schema, the description is fairly complete. It covers parameters and return type, and the scope is clearly defined. It could benefit from mentioning pagination or alternative sibling tools, but that is not essential for a basic search operation.

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. It provides an Args section explaining each parameter (e.g., 'query: Keywords to search in commit messages'). This adds meaning beyond the schema, though it is brief and does not detail format constraints (e.g., regex, case sensitivity).

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 'Search kernel git commits by message, author, or subsystem.' This provides a specific verb (search), resource (kernel git commits), and scope that differentiates it from sibling tools like search_patches or search_lkml.

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 lists optional filters, implying when to use this tool (e.g., filtering by author or subsystem). However, it does not explicitly mention alternatives when searching is not appropriate, such as using get_commit_details for a known commit or search_patches for patch content. Usage context is implied but not detailed.

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. It discloses that the query searches 'document titles and headings' (scope), supports an optional subsystem filter, and returns a dictionary with 'docs' and 'evidence_sources'. This provides meaningful behavioral context for a read-only search tool, though it does not mention edge cases like pagination or ordering.

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 well-structured with an opening line, Args list, and Returns line. It is concise and free of fluff, though the opening line slightly duplicates what the Args explain. Overall, it is efficient and scannable.

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 an output schema, the description covers the essential usage: purpose, parameter meanings, and return format. It could mention what 'evidence_sources' refers to, but the output schema likely details this. It is sufficiently complete for a straightforward documentation search 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?

Schema description coverage is 0%, so the description fully compensates by explaining each parameter: query searches titles/headings, subsystem is an optional filter with examples ('net', 'mm'), and limit is the max result count. It also defines the return structure, adding substantial meaning 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 uses a specific verb ('Search') and resource ('kernel documentation files'), and clarifies the filtering by 'query or subsystem'. This distinguishes it from sibling tools like search_kernel_code (code search) and get_doc_details (retrieve details). The purpose is immediately clear.

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 clearly implies the use case: finding documentation files by keywords or subsystem. However, it does not explicitly state when to prefer this over alternatives (e.g., search_kernel_code for code) or provide exclusions. It gives clear context but no direct comparison.

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 the full burden. It discloses that the search is hybrid, covers all indexed data, and returns a dictionary with results and correlated evidence sources. However, it does not mention ordering, pagination, or performance characteristics.

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 concise and well-structured: a single purpose sentence followed by an Args list and a Returns line. Every line contributes useful information without filler.

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 has an output schema and four clearly documented parameters, the description provides enough context for effective use. It explains what the tool searches, how to filter, and what kind of result to expect, though it leaves 'hybrid search' semantics open-ended.

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 Args section adds meaningful descriptions for every parameter, such as 'repo: Optional repository to filter by' and 'limit: Maximum number of results to return.' This compensates for the input schema's lack of property descriptions and clarifies each parameter's role.

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 'Search for kernel code using hybrid search across all indexed data,' identifying a specific verb, resource, and scope. However, it does not explicitly distinguish itself from sibling tools like lookup_symbol or search_patches beyond the resource type.

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 for searching kernel code across all indexed data, and optional repo/subsystem filters suggest narrowing the search. But it provides no explicit guidance on when to choose this tool over alternatives, nor does it mention 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 burden. It discloses the return format (dictionary with matching threads and evidence sources) but does not elaborate on limiting behavior, ordering, or potential errors. Basic behavior is covered, but deeper context is missing.

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 well-structured with a clear summary, args list, and return description. Every line adds value and the format is front-loaded with the main purpose.

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 description covers the tool's purpose, all parameters, and return value. Given the presence of sibling tools, additional context about specific use cases or limitations would enhance completeness, but it is generally adequate for a search 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?

Schema description coverage is 0%, and the description compensates by listing all four parameters with brief, meaningful explanations in the Args section. The descriptions are sufficient for basic usage, though not deeply detailed.

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 searches Linux Kernel Mailing List threads by subject or excerpt, using a specific verb and resource. This distinguishes it from sibling search tools like search_kernel_code and search_commits.

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 for searching LKML threads but does not explicitly mention when to use it over alternatives or any exclusions. With multiple sibling search tools, more explicit guidance would improve clarity.

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 the full burden of behavioral disclosure. It explains it searches titles and body, allows filtering by subsystem and limit, and returns a dictionary with 'articles' and 'evidence_sources'. However, it does not disclose potential rate limits, authentication needs, or pagination behavior, which for a read-only search tool might be acceptable but is not richly transparent.

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 structured into summary, args, and returns sections, with no wasted words. It is somewhat verbose with the 'Args:' block, but every sentence provides necessary information. The front-loaded summary immediately conveys the core purpose.

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 (3 params, one required, an output schema), the description covers all essential aspects: purpose, parameter semantics, and return format. It lacks details like sorting or pagination, but for a search tool this is sufficient for correct selection and invocation. The presence of an output schema reduces the need to explain return values in detail.

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, and it does thoroughly. Each parameter is explained: 'query' (keywords), 'subsystem' (optional filter with examples), and 'limit' (max articles). This exceeds schema info, giving full semantic clarity for all parameters, including defaults and nullability.

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 the resource ('Linux Weekly News (LWN) articles on kernel development'), making the tool's purpose unambiguous. It distinguishes itself from sibling tools like search_lkml (mailing list) and get_lwn_article (specific article retrieval) by using the verb 'search' and specifying the LWN domain.

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 when to use the tool (when searching LWN articles) but provides no explicit exclusions or references to alternatives. It doesn't say 'use search_lkml for mailing list posts' or 'use get_lwn_article to fetch a specific article,' leaving the agent to infer the appropriate context from the tool name and sibling list.

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 discloses that it returns a dictionary of matching patches and evidence sources but does not explain behavioral traits such as match semantics, ordering, pagination, or any constraints on the query or filters.

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 concise and well-structured, with a clear purpose sentence followed by a compact Args list and Returns line. Every sentence provides useful information 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?

The tool is relatively simple and output schema exists, but the description omits details like how results are ordered, whether query supports partial/fuzzy matching, and definitions of states or subsystems. It is adequate for basic use but lacks depth for more nuanced searches.

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%, but the description adds one-line meanings for each parameter (e.g., query searches patch name, limit is max results). This is helpful but lacks detail like valid state values or subsystem naming conventions.

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 searches submitted kernel patches by query, submitter, or state, with a specific verb and resource. It distinguishes itself from sibling tools like search_commits and search_kernel_code by targeting patches specifically.

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 for finding patches but does not provide explicit guidance on when to prefer it over related tools like get_patch_details or analyze_patch_series. It offers clear context for the search action but no exclusions or alternative recommendations.

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