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

There are no annotations, so the description bears full responsibility for behavioral transparency. It clearly frames the tool as an analysis operation ('Analyze what code is affected'), and its Returns section implies no side effects, but it does not explicitly state that the tool is read-only or mention any requirements (e.g., git history availability) or limitations. It does disclose that it can trace conditional logic and examine git history, which is useful, but it leaves the safety profile implicit.

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-organized with clear sections (Purpose, Use Cases, Args, Returns) and the core purpose is front-loaded in the first sentence. The use-case bullet list is helpful but slightly redundant with the subsequent 'Critical for...' sentence, adding minor verbosity. The Args section is necessary given the 0% schema coverage, so the length is justified.

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, use cases, parameters, and a high-level return description, which is good given no output schema. However, it omits prerequisites (e.g., the repository must be a valid Git repository for historical changes), potential failure modes (e.g., unknown config key), and the structure of the returned analysis (e.g., whether it includes file paths, line numbers, or severity ratings). For a tool with six parameters and no output schema, this leaves some contextual ambiguity.

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 includes a dedicated 'Args:' section that explains each of the six parameters, including the required config_key with examples ('DEBUG', 'DATABASE_URL'), the boolean flags' purposes (trace dependent logic, default handling, historical changes), and path semantics. This fully compensates for the 0% schema coverage, giving the agent a clear understanding of what each parameter controls.

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 specific statement: 'Analyze what code is affected by specific configuration values,' which clearly identifies the verb (analyze), resource (code affected by config values), and scope (config-specific). This differentiates it from sibling tools like trace_dependency_impact or analyze_execution_paths, which are not config-specific. The Returns section further clarifies the output is an analysis with affected code paths and recommendations.

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 a bulleted list of use cases ('Use this tool when you need to understand: what code will behave differently...'), which gives explicit when-to-use guidance. However, it does not mention when not to use the tool or contrast it with sibling alternatives, so it stops short of a 5.

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 burden of disclosing behavior. It clearly indicates a read-only analysis operation ('Discover', 'Returns comprehensive analysis') and explains evolution analysis via git history. It does not explicitly state that it makes no modifications, but the language strongly implies a non-destructive analytical 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 well-structured with a clear opening statement, bullet-point use cases, and an Args list. It is appropriately sized for an 8-parameter tool; no sentence is superfluous. The only minor point is that the 'Returns' line is somewhat vague, but it does not add unnecessary bulk.

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 8 parameters, no output schema, and no annotations, the description covers use cases, parameters, and return type adequately. It lacks a precise output format description, but for this analysis tool the provided 'comprehensive analysis with patterns and recommendations' is sufficient context for an agent to understand what to expect.

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, but the description's 'Args' section provides concise, meaningful explanations for all 8 parameters, including override behavior for custom patterns and the purpose of flags like include_antipatterns and show_evolution. This compensates well for the lack of schema-level 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 opens with a specific verb+resource: 'Discover error handling patterns and inconsistencies in the codebase.' This clearly distinguishes the tool from sibling code-analysis tools, none of which focus on error handling patterns.

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 an explicit 'Use this tool when you need to understand' bullet list covering common scenarios, and states it is 'Essential for implementing proper error handling.' It does not explicitly mention when not to use it or name alternative tools, so it stops short of a 5.

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 implies a read-only analysis operation ('Analyze') and describes the output, but does not explicitly state non-mutating behavior, performance implications, or limitations such as language support or repository requirements. It adds some context with 'decision points and complexity assessment' but lacks deeper behavioral disclosure.

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 clear sections: an opening summary, a bulleted list of use cases, an Args list, and a Returns statement. It is slightly longer than necessary, with 'Perfect for...' being somewhat redundant, but the organization makes it easy to scan and the extra detail is useful.

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 there is no output schema, the description does explain the return value: 'Execution path analysis with decision points and complexity assessment'. It covers usage context, parameters, and returns. However, it lacks details on edge cases, input validation, or how the analysis is performed, which would make it more complete for a complex static analysis 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% coverage, but the description compensates with an Args section that adds meaning: 'max_depth: How deep to analyze nested function calls', 'include_call_contexts: Whether to show how the function is called', and 'highlight_complex_paths: Whether to identify complex/risky paths'. Repo_path and working_directory are minimally described but acceptable. Overall, the description adds value beyond the raw 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 function: 'Analyze all possible execution paths through a function and what triggers each path.' This specific verb+resource pairing distinguishes it from sibling tools like trace_data_flow or trace_dependency_impact, which focus on data flow and dependencies rather than control flow.

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 an explicit 'Use this tool when you need to understand' list covering branches, conditions, edge cases, and decision points. It gives clear context for when to invoke it, though it doesn't explicitly mention when not to use it or name alternative 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 carries the burden of behavioral disclosure. It explains the core behavior (aggregates changes, ranks by impact) and mentions the return type, but does not disclose potential side effects, performance implications, or prerequisites (e.g., git history availability). The read-only nature is implied by 'analyze' but not explicitly stated.

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 and concise: a two-sentence summary, followed by a clear Args list and a Returns note. Every line provides necessary information without redundancy or 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?

Given the tool has six parameters and no output schema, the description provides adequate context by listing all parameters with explanations and summarizing the return value. It is not overly detailed about ranking metrics or edge cases, but covers the essential usage and output shape well enough for an agent to understand the tool's purpose and invocation.

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

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

The schema has zero parameter descriptions, so the description's 'Args' block compensates by providing brief but meaningful explanations for each of the six parameters (e.g., 'days_back: Number of days to look back for commits'). While not extremely detailed, it adds value beyond parameter names and covers all 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 tool's purpose: analyze recent changes using CodeRank to identify important modifications. It specifies the resource ('recent changes'), the method ('CodeRank'), and the scope (aggregated over the last N days, ranked by impact). This distinguishes it from siblings like 'coderank_analysis' which may be broader, and other change-related 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?

The description implies when to use the tool ('to identify most important modifications' from recent commits) but does not explicitly provide usage vs. alternative tools or any exclusions. It gives context but lacks direct guidance on when not to use it or when to prefer a sibling.

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. It discloses that the tool returns 'Testing strategy analysis with patterns, gaps, and recommendations,' which gives some insight into behavior. However, it does not mention whether it is read-only, potential performance implications, or any side effects, leaving a 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 organized with a clear opening, a bulleted use-case list, an Args section, and a Returns clause. It is longer than necessary but every section serves a purpose, especially given the schema descriptions are absent. The front-loading of purpose is effective.

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?

Despite having 8 parameters and no output schema, the description covers the main intent, parameter meanings, and a brief return summary. However, the parameter name mismatches create a completeness problem, and the return description is vague ('patterns, gaps, and recommendations') without an output schema to fill in details. More information on expected output structure would be needed.

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 adds valuable meaning by explaining each parameter, but it contains critical mismatches: it lists 'custom_file_patterns' while the schema uses 'test_file_patterns', and 'custom_framework_patterns' versus 'framework_patterns'. This could lead an agent to pass invalid parameter names and cause tool failure. The explanations are helpful but the inaccuracies outweigh that benefit.

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 specific verb and resource ('Analyze testing strategies') and clearly distinguishes this tool from its siblings (e.g., contextual_keyword_search, get_repo_symbols) by focusing on test coverage and patterns. The listed use cases further pin down its unique role.

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 when-to-use guidance through the 'Use this tool when you need to understand' list and the 'Perfect for...' sentence. It does not mention when not to use it or make direct comparisons to alternatives, but the use cases are clear enough.

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 fully disclose behavioral traits. It does mention the use of commit history and the return of predictions, which implies a read-only analytic function. However, it does not explicitly confirm that it makes no modifications, nor does it discuss potential dependencies or performance considerations, leaving some transparency 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 compact and well-structured, starting with a two-sentence summary, followed by an Args list and a Returns line. Every sentence is purposeful and the key purpose is front-loaded, achieving high conciseness without sacrificing clarity.

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 action, method, parameters, and return value, which is mostly sufficient for a read-only analysis tool with no output schema. However, the return value description is generic, and no context is provided about alternatives or prerequisites, leaving minor completeness gaps.

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 provides no descriptions (0% coverage), but the description's Args section gives clear, one-line meanings for all four parameters, including a useful example for changed_module. This fully compensates for the schema's lack of documentation, though some explanations are terse.

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

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

The description clearly states the tool's purpose with a specific verb ('Analyze') and resource ('how changes in one module historically propagate to others'). It also distinguishes itself from sibling tools by emphasizing historical commit-based analysis and predicting ripple effects, making its unique scope 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 description implies usage for predicting ripple effects based on commit history, but it does not explicitly state when to use this tool over alternatives like trace_dependency_impact or when not to use it. There is no mention of prerequisites or conditions, so guidance is only implicit.

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 doesn't disclose whether this is a read-only operation, any required permissions, or potential side effects. While it describes the return value, this is insufficient to understand the tool's behavioral profile.

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 concise first sentence that captures the purpose, followed by a clear Args section and a Returns section. Every line provides useful information without redundancy or 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?

The description covers purpose, all parameters, and the return value. However, it lacks information about when to use the tool, any limitations, or behavioral expectations. Given the absence of annotations and output schema, this is a minor gap, but the tool is relatively simple.

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 compensates well by explaining each parameter in the Args section, including types and intended use (e.g., absolute path for repo_path, comma-separated list for external_modules, format options for output_format). This adds significant value beyond the 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 it analyzes repository importance using the CodeRank algorithm to identify critical modules, providing a specific verb and resource. It distinguishes itself from sibling tools by referencing a specific algorithm, though it doesn't explicitly contrast with similar hotspot analysis 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?

The description implies usage when repository importance is needed but gives no explicit guidance on when to use this tool versus alternatives, nor does it mention any exclusions or prerequisites. The context is clear enough for a basic understanding but lacks 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?

With no annotations, the description carries the behavioral transparency burden. It discloses that the search is case-insensitive and that num_context_lines defaults to 2, and mentions 'wrapper around ripgrep,' implying standard ripgrep behavior. However, it does not specify details like recursion behavior, .gitignore handling, or how multiple matches are returned, which would be important for an agent to anticipate results.

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-line summary followed by clearly formatted Args and Returns sections. Each line serves a purpose, with no fluff or redundancy. The organization makes it easy for an agent to parse quickly.

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

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

The description covers the core purpose and parameters, but completeness is limited by the lack of output schema and annotations. The return value is only described vaguely as 'file path and lines of context,' with no indication of formatting, multiple match handling, or error behavior. Given the tool's simplicity, this is adequate but leaves room for clarification.

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?

Despite 0% schema coverage, the description fully compensates by explaining every parameter: keyword (case insensitive), working_directory (use full absolute path), and num_context_lines (default 2, context before/after). This adds crucial meaning beyond the bare schema property names and 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 function: 'Search for a keyword in the current directory (wrapper around ripgrep)' and specifies it returns contextual lines. This distinguishes it from sibling tools like smart_code_search or get_repo_symbols, which focus on different search or analysis tasks. The verb+resource structure is specific and unambiguous.

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

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

No guidance is provided on when to use this tool versus alternatives like smart_code_search or get_repo_symbols. The description only explains what the tool does and its parameters, leaving the agent to infer usage context. There is no mention of when not to use it or how it compares to sibling tools.

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

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

With no annotations, the description carries the transparency burden. 'Analyze' implies read-only behavior, and the return type is stated ('Contributor impact analysis with expertise areas'), but it does not explicitly confirm that no modifications are made, nor does it mention potential side effects like repository access costs or permissions. This is adequate but not thorough.

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: a clear purpose statement, an Args section with one-line explanations, and a Returns section. No redundant or filler content; every sentence adds value.

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 4 parameters, no output schema, and no annotations, the description covers the essential aspects: what it does, each parameter's meaning, and the type of return value. It lacks examples or edge-case guidance, but is sufficiently complete for an agent to understand and invoke the 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, forcing the description to explain each parameter. The 'Args:' section provides concise, meaningful descriptions for all four parameters (repo_path, days_back, min_commits, focus_on_important_modules), fully compensating for the schema's lack of detail.

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

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

The description clearly states the tool's purpose with a specific verb ('Analyze') and resource ('contributor patterns and their impact on important modules'), and additionally identifies key contributors and areas of expertise. This distinguishes it from siblings like coderank_analysis or get_commit_hotspots, which focus on code metrics rather than contributor attribution.

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 contributor patterns and impact, providing clear context. However, it does not explicitly state when to prefer this tool over alternatives or mention exclusions, so it misses the top score but remains clearly contextual.

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 transparency burden. It explains the analysis method (regex pattern search, tracing function calls, git history) and what will be returned (risk assessment, mitigation suggestions). While it does not explicitly state it is read-only, the wording strongly implies non-mutating analysis, and the process details are sufficient for an agent to understand the behavior.

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: purpose, use cases, arguments, and return value. It is somewhat lengthy but each section serves a purpose. The bulleted list of use cases is helpful, though a few lines (e.g., 'Critical for understanding...') are somewhat redundant with the opening 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?

Given the tool's complexity (6 params, nested objects, no output schema), the description covers the input semantics and high-level return behavior. However, it does not detail the exact structure of the returned analysis or mention error conditions, which could leave an agent uncertain about how to consume the result. Still, it is sufficiently complete for 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 0%, so the Args section is essential. It explains each parameter, provides a detailed example for the complex 'side_effect_patterns' parameter, and clarifies the behavior of 'trace_depth' and 'include_historical_bugs'. This fully compensates for the lack of 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 opens with a specific verb and resource: 'Discover all potential side effects of calling a function or method.' This clearly distinguishes it from sibling analysis tools by focusing on unintended consequences rather than general code search or dependency tracing.

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 a clear bulleted list of when to use the tool ('when you need to understand... what else might happen, affected systems, unintended consequences, what to test'). It does not explicitly state when not to use it or name alternatives, so it stops short of a 5.

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 format (categorized examples with context and patterns), but doesn't mention any limitations, side effects, performance considerations, or explicitly state it's read-only. The read-only nature is implied but not guaranteed.

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 clear sections: purpose, when-to-use bullets, Args, and Returns. However, there's some redundancy (e.g., 'Perfect for learning unfamiliar APIs...' repeats the bullet points), making it slightly verbose but still clear and organized.

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 tool with 7 parameters and no output schema, the description provides adequate context: clear use cases, full parameter explanations, and return type. It doesn't address edge cases or limitations, but it's sufficient for an agent to select and invoke the tool correctly without external documentation.

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 descriptions are completely absent (0% coverage), but the description's Args section thoroughly explains all 7 parameters, including semantics like 'absolute path', maximum examples, and context line count. This fully compensates for the missing 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 finds real usage examples of APIs/functions/classes in the codebase, using a specific verb and resource. It differentiates itself from siblings like get_symbol_usages by emphasizing real usage examples and patterns, though it doesn't explicitly name 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 clear when-to-use guidance with bullet points like 'Learn how to properly use an existing API' and 'Perfect for learning unfamiliar APIs'. However, it does not specify when not to use the tool or mention alternative sibling tools.

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

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

With no annotations, the description must disclose behavior itself. It explains the algorithmic approach and output type, but does not state whether the tool is read-only, performance expectations, or prerequisites (e.g., whether CodeRank data must exist). This is adequate but not rich.

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: an intro statement, a clear Args list, and a Returns line. No filler or redundant information, and every sentence adds value.

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 tool with no annotations and no output schema, the description covers purpose, parameters, and output sufficiently for initial understanding. It lacks edge-case behavior and dependencies, but the core functionality is well explained.

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 list provides concise explanations for all five parameters, which is crucial given 0% schema description coverage. Each parameter's role is clear (e.g., 'min_connections: Minimum import connections to consider'). However, explanations lack format constraints (e.g., path types) beyond what the schema defaults imply.

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 'Identify code hotspots by combining CodeRank with symbol usage frequency,' clearly specifying the verb, resource, and method. This differentiates it from sibling tools like coderank_analysis (likely CodeRank-only) and get_commit_hotspots (hotspots from 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 the intended use (combined connectivity and usage) but does not explicitly state when to use this tool over alternatives or provide any exclusions. No direct comparison to sibling tools is given, so guidance is inferred 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the tool 'Identifies coupled modules that might have hidden dependencies' and describes the return value, but does not disclose whether it is read-only, what git history it reads, performance implications, or any limitations. This is a significant gap for an analysis 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 reasonably structured with an Args section and Returns line, but the first two sentences are redundant ('frequently changed together' and 'coupled modules' essentially repeat the same idea). The Returns line also restates the purpose. It is not overly long, but the redundancy makes it less concise than ideal.

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 only partially complete. It covers the basic purpose and parameters, but the Returns section is vague ('Analysis of modules that frequently change together') without specifying the format or how to interpret results. It also omits caveats or additional context needed for an agent to use 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?

The schema has 0% description coverage, but the description compensates with an explicit Args block explaining each parameter: repo_path, days_back, min_cochange_frequency, and top_n. It adds plain-language meaning such as 'Days to analyze' and 'Minimum times modules must change together,' which goes beyond the schema's titles and defaults. However, it could still provide more detail (e.g., units, edge cases) to fully compensate.

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 verb and resource: 'Find modules that are frequently changed together in commits.' It also adds context about identifying coupled modules and hidden dependencies. However, it does not explicitly differentiate from sibling tools like find_code_hotspots or trace_dependency_impact, so it stops short of a 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?

No guidance is provided on when to use this tool versus alternatives. It does not mention any exclusions, prerequisites, or specific use cases beyond the basic purpose. The description implies it is for co-change analysis but never says when it is preferred over 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 the description carries the full burden for behavioral disclosure. It explains the process (running a command and filtering rows) but does not explicitly state whether the operation is read-only, mention side effects, or disclose potential errors or permissions needed. While it implies a safe read operation, that is not explicit.

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 brief summary line, a clear parameter list, and a returns section. Each parameter is explained concisely without redundancy. It is appropriately sized for a tool with five parameters and avoids unnecessary prose.

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 no output schema and no annotations, the description covers all essential aspects: the command executed, parameter meanings, return type, and intended output readiness for printing or writing. It provides enough context for an agent to use the tool effectively, though it omits error handling details, which are not typically required.

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 no descriptions, so the description fully compensates by explaining each parameter in detail, including exact semantics like 'Exact values allowed in the **Type** column' and 'post-filter'. It also clarifies the meaning of None for each optional filter, which is essential for correct usage.

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 specific verb and resource: 'Run `kit symbols <repo>`', clearly stating it retrieves and filters repository symbols. This distinguishes it from sibling tools like get_symbol_usages and smart_code_search, which focus on different aspects.

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 by explaining what the tool does and how filters work, but it does not explicitly state when to prefer this tool over alternatives or when not to use it. No comparison or exclusion criteria are provided for 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 carries the full burden. It discloses the command execution and post-filtering behavior, plus requirements like absolute paths. However, it does not discuss error conditions, whether the operation is read-only, or the exact format of returned rows, 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 a single clear sentence followed by a structured parameter list and returns section. Every element serves a purpose; no redundant or promotional 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?

For a tool with 6 parameters and no output schema, the description covers all parameters and the return type. It lacks explicit usage examples or error handling, but the operational details are sufficient for an agent to invoke the tool correctly. It could be slightly richer on edge cases, but overall it is well-rounded.

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 zero property descriptions, but the description compensates thoroughly: it explains repo path expectations, the substring flexibility for symbol_name, allowed symbol_type values, and the exact post-filter semantics for file_must_contain and file_must_not_contain. This goes far beyond the schema 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 that the tool runs `kit usages <repo> <symbol_name>` and applies filters, which is a specific verb+resource for inspecting symbol usages. This distinguishes it from sibling search tools by emphasizing usage inspection with optional type and file filtering.

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 sibling tools like smart_code_search or get_repo_symbols. It is purely operational, with no mention of alternatives, prerequisites, or exclusive use cases.

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 fails to explicitly state whether the tool is read-only, what data it accesses (e.g., git history, code files), or any side effects. Worse, the 'Args' section in the description lists parameters (focus_areas, custom_patterns) that do not match the actual input schema (which expects bottleneck_patterns), creating confusion about how the tool behaves and what it accepts.

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 uses a structured bullet-list format for usage and an 'Args' section, but the Args section is redundant (repeats parameter info) and inaccurate. The overall length is moderate, but the inaccuracies make some parts unhelpful. It could be tightened and made more coherent.

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

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

The tool has a nested object parameter (bottleneck_patterns) with no schema description and no output schema. The description provides a high-level 'Returns' statement but does not clarify the structure of the bottleneck_patterns object or explain how focus_areas/custom_patterns relate. This is a significant gap for correct invocation, especially given the parameter mismatch.

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 does attempt to explain parameters, but the names are wrong: focus_areas and custom_patterns do not exist in the schema, while bottleneck_patterns (the required object parameter) is completely omitted. This misalignment means the description actively misleads the agent about parameter names and meanings, failing to add any correct value beyond the 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 statement: 'Identify potential performance bottlenecks by analyzing code patterns and complexity.' This is a specific verb+resource and gives a good sense of the tool's function. However, it does not explicitly differentiate from sibling tools like find_code_hotspots or coderank_analysis, though the usage bullet points hint at a proactive optimization focus.

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 a 'Use this tool when you need to' list with concrete scenarios (finding likely performance issues, prioritizing efforts, understanding slow parts, planning improvements). It also states it's 'Perfect for performance optimization planning.' While it doesn't mention when not to use it or name alternatives, the usage context is clear and actionable.

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 ('Integration architecture map with dependency risks and patterns') but does not specify whether the tool is read-only, whether it requires network access, or any side effects/limitations. For a mapping/analysis tool, the lack of explicit behavioral guarantees 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 structured with bullet points, argument explanations, and a returns section, making it scannable. However, it includes promotional language ('Essential for...') and repeats parameter information that would be better placed in the schema. It is moderately verbose but not excessively so.

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 a complex schema (6 params, 3 required, nested object), no output schema, and no annotations, the description should do more. It provides usage scenarios and a high-level return description but fails to clarify the format of the integration map, how integration_patterns should be structured, or what 'risk' assessment entails. The parameter mismatch further reduces its completeness.

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

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

The schema description coverage is 0%, so the description must compensate. The 'Args' section explains several parameters and their meaning (e.g., include_error_handling, show_configuration, risk_assessment). However, it lists 'integration_types' and 'custom_patterns' which do not match the actual schema's 'integration_patterns' parameter. The required core parameter 'integration_patterns' is left entirely unexplained, creating confusion and potential misuse.

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 'Map all external integration points and their characteristics,' a specific verb+resource statement that clearly defines the tool's purpose. It enumerates the types of insights (services, implementation, error handling, failure points) which distinguishes it from sibling analysis tools like trace_dependency_impact or find_code_hotspots.

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 'Use this tool when you need to understand:' followed by concrete scenarios, and adds 'Essential for understanding system dependencies, planning for service outages, or designing resilient integration patterns.' This gives clear context for when to invoke the tool, though it does not mention when not to use it or name specific alternatives.

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 present, so the description must convey behavioral traits. It mentions combining ripgrep with CodeRank and that results are prioritized by code importance, but does not disclose details such as output format, error handling, performance implications, or whether it modifies any state (though search implies read-only). The transparency is minimal and leaves key behaviors unspecified.

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 very concise and well-structured: a one-sentence overview, an Args list with each parameter on its own line, and a Returns line. There is no fluff or redundancy, making it easy to scan for key information while remaining complete at a high level.

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 tool with six parameters, no output schema, and many similar siblings, the description is under-specified. It does not describe the structure of the returned results (e.g., file paths, line numbers, match snippets), how the working_directory relates to repo_path, default sorting behavior when rank_results is false, or behavior on invalid inputs. This leaves the agent with significant uncertainty about tool output and behaviors.

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 descriptions are empty (0% coverage), but the description adds meaningful information for most parameters, such as keyword 'supports regex', working_directory 'absolute path', and clear explanations for rank_results, context_lines, and max_results. However, repo_path remains vague (no clarity on relative vs absolute, or repo structure), so it does not fully compensate for the schema gap.

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

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

The description clearly states the tool performs an enhanced code search combining ripgrep with CodeRank to prioritize important modules. This specific technique (CodeRank prioritization) distinguishes it from sibling tools like contextual_keyword_search, making its unique purpose 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?

No explicit guidance is provided on when to use this tool versus alternatives. Given the many sibling search and analysis tools, the description does not mention when CodeRank prioritization is beneficial or when a simpler search would be preferred, leaving the selection decision to the agent.

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 tool identifies transformations, side effects, and data origins/destinations, and that it returns 'Comprehensive data flow analysis.' However, it does not disclose whether it is read-only, performance implications, scope limitations, or how the tracing is performed (e.g., static analysis via AST). This leaves gaps in behavioral 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 well-structured with a clear purpose statement, bullet points for usage, and distinct sections for Args and Returns. It is appropriately sized and front-loaded. The 'Perfect for debugging' sentence is somewhat redundant given the preceding bullets, but not distractingly so.

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 (6 parameters, no output schema, no annotations), the description provides a solid foundation: purpose, usage guidance, parameter explanations, and a Returns summary. However, it omits practical details like whether a git repository is required, performance considerations, or the exact structure of the returned analysis, which would help an agent invoke it more confidently.

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 coverage is 0%, so the description must compensate. The Args section provides descriptions for all six parameters, adding meaning beyond the schema's bare titles and types. For example, data_identifier gives examples and max_depth clarifies its purpose, though some descriptions remain vague (e.g., 'how deep' lacks units, working_directory vs repo_path distinction is subtle).

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 specific verb+resource: 'Trace how specific data flows through the system from source to destination.' It also lists concrete examples of data identifiers (user_id, email, order_data) and clearly differentiates from sibling tools like trace_dependency_impact by focusing on the flow of a specific data element rather than dependency impact.

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 when-to-use conditions via 'Use this tool when you need to understand:' followed by four concrete scenarios. However, it does not include when-not-to-use conditions or mention alternative sibling tools, which prevents a perfect score.

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

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

With no annotations provided, the description carries the full burden of disclosing side effects and safety. It does not explicitly state that the tool is read-only or non-mutating, nor does it describe any limitations or prerequisites. The return description is also vague, leaving the agent uncertain about output structure.

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 brief summary, an Args list, and a Returns line. The Args section is necessary given the schema's lack of descriptions, though the Returns line is under-specified.

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?

This is a moderately complex tool with no output schema, no annotations, and low schema coverage. The description explains parameters but fails to describe the return structure or operational expectations, leaving significant gaps for an agent to invoke it correctly.

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 zero descriptive coverage, so the description's Args section is essential. It provides meaningful descriptions for all six parameters, including the allowed values for analysis_type and change_type, and clarifies the purpose of max_depth and working_directory.

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 traces dependency chains and analyzes refactoring impact for a module, explicitly combining both. This distinguishes it from sibling tools like trace_data_flow or change_propagation_analysis because it targets module-level combined analysis.

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 the tool—when dependency tracing, refactoring impact, or both are needed. It explains the analysis_type parameter to select modes. However, it does not explicitly mention alternatives or when not to use it relative to sibling tools.

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

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

With no annotations, the description carries the full burden. It explains file categorization behavior ('Files are categorized by checking if any pattern appears in the file path') and the overall mapping intent. However, it does not disclose whether the tool is read-only (no modifications), how trace_depth affects behavior, or what happens when no files match, leaving 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 structured with a clear intro, usage bullets, and an organized Args section with an example. It is longer than average but appropriate for 7 parameters including a nested object. Minor redundancy: 'Perfect for feature modification...' overlaps with the earlier bullet list, but overall every section earns its place.

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

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

For a tool with 7 parameters, no annotations, and no output schema, the description covers purpose, usage, and parameters well. The 'Returns' section is minimal ('Complete feature implementation map with all involved components by layer') and does not describe output structure, how trace_depth influences results, or edge cases. Completeness is adequate but not exhaustive.

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 coverage is 0%, so the description must fully explain parameters, and it does. Each argument has a meaningful explanation; file_categories includes a detailed format and example, and trace_depth/booleans are described. This compensates completely for the schema's lack of 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 opens with a specific verb and resource: 'Map all code involved in implementing a specific feature from UI to data layer.' It clearly defines the scope (feature implementation, multi-layer) and distinguishes itself from sibling tools like trace_data_flow or map_integration_points by focusing on feature implementation mapping.

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 an explicit 'Use this tool when you need to' list covering four common scenarios, and adds 'Perfect for feature modification, debugging feature issues, or understanding complex feature implementations.' It lacks explicit when-not-to-use guidance or named alternatives, but the use cases are clear and actionable.

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