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

Annotations already declare readOnlyHint true, destructiveHint false, idempotentHint true. Description adds return value details (classes, methods, etc.) which is helpful but does not disclose behavior beyond what annotations provide. No contradiction.

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?

Description is concise at 2 sentences plus a bullet list of return values. Essential information is front-loaded. Could potentially be more concise by removing the bullet list if not needed, but overall efficient.

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

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

Given the tool has 1 parameter with full schema coverage, no output schema, and rich annotations, the description adequately states the analysis scope and return value. However, it lacks details on error conditions (e.g., file not found) and implicit dependencies like Python installed. Lacking that, it's minimally 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 single parameter 'path' is fully described in the input schema (100% coverage). Description repeats the parameter description but adds no new meaning beyond the schema. Baseline 3 applies.

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?

Description clearly states it analyzes a Python file with specific elements (classes, functions, etc.) and metrics. Differentiates from sibling by mentioning 'Python file' and specific analysis outputs, but could be more distinct from cc_analyze_methods.

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?

Description implies use for investigating Python code structure and metrics. No mention of when to use alternatives like cc_analyze_methods or cc_diagnose_imports, but the scope is clear.

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?

Annotations already indicate this is read-only and idempotent. The description adds value by detailing what analysis is performed (parameters, decorators, complexity, data flow), which goes beyond annotations. No contradictions.

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: a one-line summary followed by organized Args and Returns sections. Every sentence is informative and 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?

Given the tool's complexity (method analysis), the description is fairly complete. It covers inputs, optional filter, and output. However, it does not mention performance considerations for large files, which would be helpful.

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 100%, so the description adds minimal extra beyond the schema. It repeats parameter descriptions but does not provide deeper semantics like file size limits or encoding requirements.

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 detailed method analysis of a Python file, listing what is returned (methods with parameters, decorators, complexity, data flow). It is distinct from sibling tools like cc_analyze_code and cc_extract_classes.

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 includes explicit parameters (path and optional class_name) and the return format, but lacks guidance on when to use this tool versus alternatives like cc_analyze_code or cc_extract_classes. It is clear but does not exclude 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?

Annotations already indicate the tool is read-only, idempotent, and non-destructive. The description adds the specific checks performed, which is useful context but does not disclose return format, error behavior, or how results are reported.

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 sentence that efficiently communicates the tool's purpose with no redundant information. However, being in German may reduce clarity for English-speaking agents, but conciseness is otherwise excellent.

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 no output schema, so the description should explain what the tool returns (e.g., list of issues, file paths). It does not, leaving the agent uncertain about the result format. With moderate schema richness, this is a significant gap.

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 100% with good descriptions for path, recursive, and max_issues. The description does not add additional parameter meaning beyond the schema, so baseline score of 3 is appropriate.

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 checks Python files for specific indentation issues like missing colons, unindented return/yield, and mixed tabs/spaces. The verb 'Prüft' and resource 'Python-Dateien' are specific, and the listed issues distinguish it from general code analysis siblings like cc_analyze_code.

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

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

The description provides no guidance on when to use this tool versus alternatives (e.g., cc_analyze_code). It merely states what it does, leaving the agent without context for selection.

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?

Annotations indicate idempotency (idempotentHint=true) and non-destructive (destructiveHint=false). Description adds that dry_run is available for preview, making it safe to test. However, it doesn't mention file backup or undo behavior, which is acceptable given idempotency annotation.

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?

Description is concise and front-loaded with the purpose, followed by parameter list. The list format is clear. Minor waste: some descriptions are truncated (e.g., 'Trailing whitespace' could be more descriptive).

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 good schema coverage and annotations, the description is complete enough for a cleanup tool. It explains all operations and includes dry_run. No output schema, but the tool is simple and output is likely a success message or diff, which may be inferred.

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 covers all 6 parameters with descriptions (100% coverage). The description repeats parameter info from schema, adding no new semantics beyond what schema provides. Baseline 3 is appropriate.

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 cleans up source code files with specific operations (BOM, NUL bytes, trailing whitespace, line endings). It distinguishes from siblings like cc_fix_encoding and cc_organize_imports by focusing on whitespace and byte-level cleanup.

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 operations but does not provide guidance on when to use this tool vs alternatives (e.g., cc_fix_encoding for encoding issues). No explicit when-not-to-use or prerequisites mentioned.

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?

Annotations already provide idempotentHint=true, indicating repeated calls are safe. Description adds that it converts formats, but doesn't disclose whether input/output files are overwritten, if temporary files are created, or error handling. No contradiction with annotations (idempotentHint=true is consistent with conversion). Annotation contradiction: false.

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?

Description is concise (3 lines) with bullet-like listing of args. However, the Args section is essentially a duplication of the schema. Could be trimmed to just the first sentence plus a note about optional json_indent. Still, no wasted sentences.

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

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

Given no output schema, description lacks info on return value (e.g., success message, error handling). However, conversion tools typically return success/failure. Parameter coverage is complete. Suggestions for additional context: output file overwrite behavior or format conversion limitations.

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 100%, so baseline is 3. The description adds value by listing all parameters and their types, which is redundant but helpful in a quick look. It also mentions a non-schema item (json_indent with default) and provides format enums inline. Slight extra value beyond 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 that the tool converts between multiple specific formats (JSON, CSV, INI, YAML, TOML, XML, TOON). The verb 'converts' and the list of formats provide high specificity. Sibling tools like cc_fix_json or cc_validate_json are related but distinct, and the description differentiates by covering many formats.

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 does not explicitly state when to use this tool versus alternatives. For example, if the goal is only to validate JSON, cc_validate_json might be better. However, the tool's broad format support implies it is the go-to for general conversion. No exclusion criteria or alternatives mentioned.

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?

Annotations already mark the tool as readOnly and non-destructive, so the description need not restate that. It adds value by detailing the types of issues detected (missing modules, circular imports, etc.), which goes beyond the annotations.

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 short and front-loaded with purpose. The second sentence repeats 'path' information from schema, minor 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?

Given the low complexity (one parameter, no nested objects), the description adequately covers purpose and detection. No output schema exists, but the tool's output (diagnosis list) is implicit and not critical.

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 100% for the single parameter 'path', so baseline is 3. The description restates the parameter's purpose but adds no extra detail beyond what the schema already provides.

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 diagnoses import issues like missing modules, circular imports, and unused imports. It distinguishes itself from siblings like cc_analyze_code and cc_organize_imports by focusing on diagnosing problems rather than general analysis or organization.

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 encountering import problems, but does not explicitly state when to use this tool versus others. No when-not-to-use or exclusion criteria are given, leaving the agent to infer 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?

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false, so the tool is clearly a safe, read-only, idempotent operation. The description adds the specific output format (Unified-Diff-Format), which provides useful behavioral context beyond annotations.

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, well-formed sentence that efficiently conveys the tool's purpose and output format. No unnecessary words or redundant information.

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

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

Given that the tool has simple inputs (two file paths and an optional context lines parameter), the description covers the core functionality. However, it does not mention any potential constraints like file existence or formats, but given the schema's completeness and the tool's simplicity, it is largely adequate.

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

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

Schema description coverage is 100%, so the schema already documents the three parameters with descriptions. The description does not add any further semantic context beyond what the schema provides, but it also does not need to. Baseline 3 is appropriate here.

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 compares two files and shows differences in unified diff format. It is a specific verb ('vergleicht') plus resource ('zwei Dateien') with an explicit output format, which distinguishes it from siblings like cc_analyze_code or cc_cleanup_file.

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 comparing two files, but it does not provide any guidance on when to use this tool versus other tools that might also handle file comparisons (none directly, but siblings like cc_analyze_code could be related). No alternatives or exclusions are mentioned.

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?

Discloses that output_dir is optional and defaults to display only, which is behavioral. Annotations already mark it as idempotent and non-destructive, and description does not contradict. Adds value beyond annotations.

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?

Exceptionally concise: a single-sentence purpose, brief parameter list, and one-line usage note. No redundant or vague language, well-structured with clear front-loading.

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?

Covers purpose and parameter basics but lacks explanation of output format (e.g., what 'separate text blocks' look like) and what 'pycutter-style code blocks' means. With no output schema, more detail would be helpful.

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

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

Schema description coverage is 100%, so baseline is 3. The description's 'Args' section largely repeats schema descriptions without adding new semantics or clarifying edge cases.

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-resource pair 'Extracts Python classes and functions' and clearly distinguishes it from sibling tools like cc_analyze_code by emphasizing extraction of separate text blocks.

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?

States 'Useful for code review and documentation', providing clear context. However, it does not explicitly mention when to avoid using this tool or how it differs from similar siblings (e.g., cc_analyze_methods).

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?

Annotations declare idempotentHint=true, destructiveHint=false, and readOnlyHint=false. The description adds context that it repairs 27+ patterns for specific languages, which goes beyond annotations. No contradictions.

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: one sentence for purpose, a compact list of arguments, and a one-liner about patterns. Every word adds value; no 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 low complexity (3 parameters, no output schema), the description is adequate: it explains the tool's purpose, parameters, and supported patterns. Could mention that it modifies files in-place (destructiveHint=false but still writes), but completeness is high for the scope.

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

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

Schema description coverage is 100%, so the schema already documents parameters. The description briefly mentions the three params (path, dry_run, create_backup) but adds no extra context beyond what is in 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 states 'Repairs encoding errors (Mojibake, double UTF-8)' which is a specific verb-resource pair. It clearly distinguishes from siblings like cc_fix_json or cc_fix_umlauts by focusing on encoding issues.

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 implicitly suggests use for encoding errors but gives no explicit guidance on when to use vs alternatives like cc_fix_umlauts or cc_scan_emoji. No when-not or exclusion criteria 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?

Annotations indicate idempotentHint=true and destructiveHint=false, which the description does not contradict. The description adds behavioral context by listing specific error types repaired, and mentions backup creation, which is useful. However, it doesn't disclose if repairs are lossy or if output is overwritten.

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 with bullet-listed parameter descriptions and a clear summary of repairs. It could be slightly more structured, but it's effective and 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?

Given the tool's simplicity (3 params, no output schema), the description covers the main use case and parameters. It lacks detail on return values and edge cases, but for a repair tool this is adequate. The annotations provide additional context like idempotency.

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 100%, so the schema already documents all parameters. The description adds minor clarity by listing the repairs and summarizing each parameter's purpose, but does not add further semantics beyond what the schema's descriptions provide.

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 repairs common JSON errors, listing specific issues (BOM, trailing commas, etc.). This distinguishes it from siblings like cc_validate_json and cc_convert_format by focusing on fixing rather than validating or converting.

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 implicitly suggests use when JSON files have common errors, but it does not explicitly state when to use this vs alternatives like cc_validate_json. No when-not-to-use or exclusionary conditions 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?

Annotations indicate idempotentHint=true and destructiveHint=false, so the description aligns. It adds value by disclosing the 70+ patterns detected and replacement behavior, which is beyond annotations.

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?

Description is concise with three sentences, front-loading the core purpose. The Args list could be integrated into the first sentence to reduce redundancy, but overall efficient.

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

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

The description is complete given no output schema and 100% schema coverage. It explains the tool's unique capability (70+ patterns) and parameter functions sufficiently for an agent.

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

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

Schema has 100% description coverage, so parameters are fully described in schema. The description only lists parameter names, adding no extra meaning beyond what schema provides.

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 fixes broken German umlauts in source code files, specifying the resource type (source code) and the scope (German umlauts). It distinguishes from sibling tools like cc_fix_encoding by targeting a specific encoding issue.

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 does not explicitly state when to use this tool versus alternatives like cc_fix_encoding. It implies usage for German umlaut problems but lacks guidance on prerequisites or conditions.

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?

Annotations already show idempotentHint=true (no side effects beyond output file) and destructiveHint=false. Description adds context: reads package.json or pip packages, so agent knows it doesn't modify project. No contradictions.

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?

Very concise: 4 lines with clear bullet for args and a short sentence on behavior. No fluff, front-loaded 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 no output schema, description doesn't detail return value, but that's acceptable. Enumerates supported project types and formats. Could mention if output file is overwritten, but idempotent hint implies content generation. Complete for a simple license generator.

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 100% with basic descriptions. Description adds meaning beyond schema: lists allowed format values (text/json/csv) and explains project_dir is for npm or Python projects. Slightly improves on 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?

Clearly states it generates a third-party license file for npm or Python projects. Specifies input (package.json or pip packages) and outputs (license info). Differentiates from siblings focusing on code analysis, conversion, or cleanup.

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?

Implies usage for generating license files, but no explicit guidance on when to use this vs alternatives. No mention of prerequisites (e.g., presence of package.json) or when not to use.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds that it does not write files, which aligns with readOnly. However, it does not disclose other behavioral traits such as error handling, performance implications, or dependencies on external templates. With annotations providing the safety profile, the description adds minimal but useful 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 a single concise sentence in German with no wasted words. However, it lacks structure (e.g., bullet points, front-loading of key information). For its brevity, it is appropriate, but for a tool with 19 parameters, more structure would improve 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?

Given the tool's complexity (19 parameters, no output schema), the description is insufficient. It does not explain how parameters interact (e.g., the relationship between kind and other fields), the purpose of spec_json merging, or expected output format. The agent would need to infer from parameter names and descriptions alone.

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

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

Schema description coverage is 100%, with each parameter having a description. The tool's description adds no additional meaning beyond what the schema already provides. Baseline 3 is appropriate since the schema does the heavy lifting.

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

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

The description clearly states it generates Python code snippets from BACH-derived templates without writing files. The name and title are self-explanatory, and the description adds the key constraint of not writing files. It does not explicitly distinguish from sibling tools, but no sibling directly competes with code generation, so purpose is sufficiently 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 provides no guidance on when to use this tool versus alternatives. It mentions 'BACH-abgeleiteten Templates' but does not explain what BACH is or in what scenarios this tool is preferable. There is no exclusion criteria or recommended context. The agent is left without usage direction.

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. 'Get' implies a read-only operation with no side effects, and the description clarifies the data scope. However, it does not explicitly state that no configuration is modified, nor does it describe response format or error behavior. This is a minor gap for a zero-parameter getter.

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, front-loaded sentence with no redundant words. It directly states what the tool does and what it returns.

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 zero-parameter tool with no output schema, the description covers the essential information: it returns the currently configured output language and the list of supported languages. It could mention the exact return format, but that is not critical for such a simple getter.

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 parameters, so the baseline is 4. The description does not need to explain parameter semantics because there are none to explain.

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 ('Get') and a clear resource: the currently configured output language and supported languages for CodeCommander tools. This clearly distinguishes it from the sibling tool cc_set_language, which would be for changing the language.

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: call this when you need to know the current output language or supported languages. However, it does not explicitly mention the alternative cc_set_language or any conditions for when to use one over the other. For a simple getter, the implied usage is adequate 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?

Annotations already indicate idempotentHint=true and no destructive/readOnly concerns. Description adds value by specifying 'produces standalone HTML with CSS styling' and 'printable as PDF via browser', which are behavioral traits not captured by annotations. No contradictions.

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?

Description is very concise with a single line plus an argument list. Front-loaded with main action and key output detail. Every sentence adds value: conversion purpose, arguments, output format and use case.

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 3 parameters, no output schema, and moderate complexity. Description adequately covers inputs and output format, but lacks details on error handling, encoding, or behavior for invalid inputs. Given the simplicity, it is mostly sufficient but could be more 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 coverage is 100% but descriptions are brief ('Markdown file', 'HTML output', 'Document title'). Description adds context by grouping parameters and noting the optional title. Provides additional meaning beyond schema for input_path and output_path by linking to the conversion process.

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?

Description clearly states verb 'Converts' and resource 'Markdown to formatted HTML'. Explicitly distinguishes from sibling 'cc_md_to_pdf' by noting HTML can be printed as PDF via browser, implying this tool is for HTML conversion while the sibling directly produces PDF.

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?

Description provides clear context: converts markdown to HTML with CSS styling for PDF printing. However, it does not explicitly state when to use this over cc_md_to_pdf or when not to use it, missing a clear alternative exclusion.

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?

Annotations already indicate idempotentHint=true and no destructiveness; description adds value by disclosing the browser dependency, fallback to HTML, and relation to cc_md_to_html. No contradictions found.

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, with meaningful sections. It front-loads the main action and includes args list. Minor verbosity in the fallback line could be trimmed.

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 simplicity (3 params, no output schema, idempotent), the description covers the core behavior, browser requirement, and fallback. However, it could mention possible errors or output location clarity.

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

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

Schema description coverage is 100% (all params described generically), but the description adds details like the optional title doc title and the browser dependency, which are useful but not essential. Score reduced because the description largely repeats parameter names without deeper semantics.

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

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

The description clearly states the verb 'converts' and the resource 'Markdown to PDF', specifying the browser-based approach and distinguishing it from sibling cc_md_to_html by mentioning the same parser and fallback behavior.

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 indicates when to use this tool (for PDF conversion), mentions a prerequisite (Edge/Chrome), and a fallback (to HTML). However, it does not explicitly state when NOT to use it or compare with siblings like cc_convert_format.

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?

Annotations include idempotentHint: true, destructiveHint: false, readOnlyHint: false. Description mentions preview via dry_run, groups. No contradiction.

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?

Brief, includes parameters and groups. Well-structured with bullet points.

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

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

Given no output schema, description explains output implied (organized file). Could mention that modifying file side-effect, but annotations cover idempotency.

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 100%, so schema describes parameters well. Description adds grouping info and clarifies dry_run behavior, but baseline is 3.

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?

Description clearly states it organizes Python imports per PEP 8, sorted, deduplicated, grouped. Distinct from siblings like cc_analyze_code or cc_cleanup_file which have different purposes.

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?

Gives a general usage context (organizing imports) but does not explicitly say when to use vs alternatives or when not to use. No mention of prerequisites.

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

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

The description discloses key behaviors: it applies edits with preview, test file modes, and backup protection. Annotations provide no destructive hint, but the description clarifies the tool can modify files with safeguards. No contradiction with annotations.

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 sentence that captures the core functionality. It is concise and front-loaded with the key idea, though it could be slightly more structured.

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

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

Given 15 parameters and no output schema, the description is relatively brief. While the schema covers parameter details, the description lacks guidance on how the different operations (inspect, insert, etc.) relate to each other or the overall workflow.

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 100% with all parameters having descriptions. The description does not add extra meaning beyond the schema, but the schema itself is thorough, so baseline 3 is elevated to 4.

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 handles structural Python edits with preview, test file, and backup protection. The name also indicates Python structural editing. However, the phrase 'BACH-abgeleitete' is obscure and may not be understood by all agents.

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 implicitly suggests use for structural edits but provides no explicit guidance on when to use this tool versus sibling tools like cc_generate_python_code or cc_analyze_methods. No when-not-to-use or alternative comparisons are given.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the tool's safety is clear. The description adds that it tests against text or file content but does not elaborate on return format or behavior beyond annotations. With annotations covering much, the description provides marginal extra transparency.

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

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

The description is a single sentence, front-loaded, and to the point. It wastes no words, though it is in German, which may reduce clarity for English-speaking agents.

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 5 parameters and no output schema, the description should indicate return format or provide usage hints. It lacks any mention of matches, results, or replacement functionality. Annotations are helpful but don't fill the completeness gap.

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

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

Schema description coverage is 100%, so the parameters are well-documented in the schema. The description adds no new parameter semantics beyond what the schema provides; it merely restates 'text or file content'. Thus, baseline score of 3 is appropriate.

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 tests regular expressions against text or file content, which is specific and distinguishes it from sibling tools that focus on code analysis, JSON validation, or formatting. However, it could be more precise by mentioning pattern and flags.

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

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

The description provides no guidance on when to use this tool versus alternatives, nor does it mention any prerequisites or when not to use it. Sibling tools are unrelated, so context is missing.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, covering safety. The description adds that execution occurs in isolated subprocesses, which is useful behavioral context. However, it does not explain BACH or any potential side effects beyond the annotations.

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 concise sentence that immediately states the core function. There is no redundant information, and it is efficiently 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 5 parameters and no output schema, the description lacks explanation of return values or diagnostic results. The term 'BACH-abgeleitete' is undefined, and the tool's behavior beyond isolation is unclear. More context is needed for a diagnostic 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 100%, so baseline is 3. The description does not add additional meaning beyond the schema; it merely states parameters implicitly through the task. No enrichment of parameter semantics is provided.

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 specifies a concrete action: performing Python runtime import diagnostics in isolated subprocesses, derived from BACH. It distinguishes the tool from static analysis siblings like cc_diagnose_imports by emphasizing 'runtime' and 'subprocess', but does not explicitly compare with 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?

No guidance is provided on when to use this tool versus siblings (e.g., cc_diagnose_imports). There is no mention of prerequisites, scenarios, or exclusions, leaving the agent to infer usage context entirely.

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?

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, so the agent knows it is safe and non-destructive. The description adds that it 'shows ASCII alternatives' and that scanning can be recursive or filtered by extensions, providing behavioral context beyond the annotations.

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—two short sentences plus a bullet list of args. Every sentence adds value, and the structure (one line summary, then args) is clear. 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?

Given the tool's simplicity (3 parameters, no output schema, single purpose), the description covers the essential aspects. The absence of output schema is fine; the result is likely a list of found emojis with alternatives, which the description implies. Could mention what 'shows ASCII alternatives' means (e.g., output format) but not critical.

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

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

Schema description coverage is 100%, but the descriptions in the schema are minimal (e.g., 'Path', 'Recursive'). The tool description adds context: path is a file or directory, recursive is for recursive scanning, and extensions specifies which file types. This adds meaning 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 scans files for emojis and shows ASCII alternatives, which is a specific verb+resource combination. This distinguishes it from siblings like cc_fix_encoding or cc_fix_umlauts which handle encoding/umlaut issues, not emoji detection.

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

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

The description mentions usefulness for systems that don't support Unicode/Emoji, giving a clear use case. It does not explicitly state when NOT to use it or mention alternatives, but the context is 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 exist, so the description bears full responsibility. It only states a state change ('set') without disclosing persistence, scope, reversibility, or side effects, leaving key behavioral traits 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 a single sentence that is front-loaded with the verb. It is concise, though it sacrifices some informative detail.

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 low complexity, the description should clearly explain the effect (e.g., affects all subsequent tool outputs) but fails to do so, leaving ambiguity about the tool's role.

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 100% with a clear enum description. The description adds no extra meaning beyond the schema, so it meets the baseline for high-coverage cases.

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 specifies the action ('Set') and the resource ('output language for CodeCommander tools'), distinguishing it from siblings that perform other tasks like diffing files or scanning emoji.

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 given on when to use this tool (e.g., before other tools) or alternatives, but its purpose is self-contained and differs from all listed siblings, reducing the need for explicit 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?

Annotations declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, which already signal safe behavior. The description adds that it provides 'detailed error information and position', offering some behavioral context. However, it does not specify output format or behavior on non-existent files.

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 short and front-loaded, stating the core purpose in the first sentence. The Args section is concise but could be integrated. Overall efficient.

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

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

The tool is simple (one parameter, no output schema, clear annotations). The description covers the purpose and parameter but lacks details on return values or error handling, which is acceptable given the simplicity.

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 100% with one parameter. The description restates the parameter 'path' as 'Path to the JSON file', adding minimal value beyond the schema description. Baseline 3 is appropriate.

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 validates JSON and provides detailed error information with position. The verb 'validates' and resource 'JSON' are specific. While it does not explicitly differentiate from siblings like 'cc_fix_json' (which likely fixes JSON), the distinct name and title help distinguish it.

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 'cc_fix_json'. The description does not mention prerequisites (e.g., file existence) or scenarios where this tool is appropriate.

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