mcp-zen-of-languages

Server Quality Checklist

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Latest release: v0.8.1
Disambiguation2/5
The repository analysis tools form a confusing cluster: analyze_repository, analyze_batch, analyze_batch_summary, and analyze_batch_auto all analyze a repository but differ in output format, pagination, and token budget. Additionally, detect_languages and get_supported_languages both return language lists with only subtle differences. This overlap creates real ambiguity for an agent despite the descriptive text.
Naming Consistency5/5
All tool names consistently use a verb_noun structure with snake_case, such as analyze_repository, set_config_override, and onboard_project. The pattern is predictable and easy to navigate.
Tool Count4/5
At 16 tools, the server is slightly over the ideal 3-15 range, but the tools cover distinct aspects of the analysis workflow: snippet analysis, repository analysis, batch processing, reporting, configuration, and onboarding. The count feels justified rather than bloated.
Completeness4/5
The tool set covers the core workflow of language detection, code and repository analysis, paginated scanning, summarization, remediation prompt generation, agent task creation, and configuration management. Minor gaps exist, such as no explicit rule-listing tool, but the export_rule_detector_mapping and get_supported_languages partially compensate.
Average 3.9/5 across 16 of 16 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 7 commits in the last 12 weeks
- Last stable release on June 22, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows it's a safe read. The description's phrase 'stricter request-quality guardrails' hints at validation but is ambiguous; 'richer telemetry metadata' relates to output not behavior. It adds no concrete 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the core action. It is not excessively verbose, but the trailing qualifiers 'stricter request-quality guardrails and richer telemetry metadata' are vague and could be considered wasted words, preventing a perfect score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The presence of annotations and an output schema reduces the need to explain basic behavior and return format. However, the description lacks any usage guidance, and the vague qualifiers do not clarify when to use this tool or what distinguishes it in practice. It is adequate but has gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so all parameters are already documented. The description does not add any parameter-level meaning or clarify the 'project_as' field or 'perspective' enum. Baseline 3 is appropriate since the schema carries the full burden.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action and resource: 'Analyze a code snippet against zen rules.' This distinguishes it from sibling tools like analyze_repository and analyze_batch by focusing on a single snippet. The added qualifiers 'stricter request-quality guardrails and richer telemetry metadata' are vague and not directly about purpose, but they don't obscure it.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or when-not-to-use guidance is given. It does not mention alternatives like analyze_repository for broader scope, nor does it provide context for choosing this tool over siblings. The usage is only implied by the verb and the resource.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds that prompts include 'MCP-first guidance metadata' and 'v2 versioned prompt semantics,' which gives a hint about output structure but lacks concrete details about behavior such as return format or side effects. 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence and is efficient in length. However, the specialized terms 'MCP-first' and 'v2 versioned' may not be universally understood and reduce clarity, though they do not add unnecessary bulk.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The presence of an output schema and fully documented parameters lowers the burden on the description. Yet the description does not explain the broader context of remediation prompts, when to invoke this tool, or how the various parameters (e.g., perspective, project_as) interact. Given the large sibling set, additional context would be valuable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All six parameters are fully described in the schema (100% coverage), so the description does not need to elaborate on parameter semantics. The description itself mentions no parameters, and the schema already handles meaning, so a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action ('Generate') and resource ('remediation prompts'), which is specific and matches the name. However, it does not explicitly differentiate from sibling tools like 'generate_agent_tasks' or the analysis tools, and the added jargon ('MCP-first guidance metadata', 'v2 versioned prompt semantics') may confuse rather than clarify.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 the many sibling tools (e.g., analyze_zen_violations, generate_agent_tasks). It does not mention prerequisites, typical use cases, or exclusions, leaving the agent to infer usage solely from the name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, so the safety profile is known. The description adds the behavior of returning per-file results, but does not disclose the potential execution of external tools or file caps, which are only visible in the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that conveys the core purpose without wasted words. It is appropriately concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is minimal but the presence of a detailed schema and annotations helps. However, it lacks context about the type of analysis performed, the meaning of per-file results, and when to apply this tool, making it only minimally complete for a tool with five parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so all parameters have detailed descriptions. The tool description adds no additional parameter semantics, though it does identify repo_path as the main input. This aligns with the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool analyzes a repository path and returns per-file analysis results, using a specific verb and resource. It is distinguishable from siblings like detect_languages, though it does not specify the exact nature of the analysis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidance is provided. The description does not indicate when to use this tool versus alternatives such as analyze_zen_violations or generate_prompts, nor does it mention any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds minimal context ('for analysis') but does not disclose additional behavioral traits. It is consistent with annotations, so 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no wasted words. It is front-loaded and clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only tool with full schema coverage and an output schema, the description is adequate. It could benefit from noting the distinction from get_supported_languages, but the current context covers the essential information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%; the repo_path parameter is fully described in the schema including its 'currently unused' status. The tool description adds no parameter information beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns a supported language list with the verb 'Return' and resource 'supported language list'. However, it does not distinguish from the similarly named sibling tool 'get_supported_languages', which appears to serve a similar purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. There is no mention of context, exclusions, or alternative tools like 'get_supported_languages'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety and idempotency. The description adds no additional behavioral traits (e.g., side effects, permissions, performance). It does not contradict annotations, but also provides minimal behavioral context beyond what annotations already convey.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no wasted words. It efficiently conveys the core purpose and output formats without redundancy. This is appropriate for the tool's relative simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
An output schema exists, so return values are covered. However, with 7 parameters and a wide range of perspectives and include/exclude options, the description is quite sparse. It does not explain the meaning of 'perspective' or the distinction between analysis, gaps, and prompts. The schema carries the burden, but the description could provide additional context for effective tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description does not add any parameter-specific information beyond the schema; it merely repeats the concept of 'gap analysis' and 'prompts' which are already reflected in include_gaps and include_prompts parameters. No extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb ('Generate'), a specific resource ('report'), and the report's contents ('gap analysis and prompts'). It also specifies output formats ('markdown/json'), which distinguishes it from sibling tools like generate_prompts or analyze_zen_violations that focus on narrower outputs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 does not mention scenarios, prerequisites, or exclusions. With many sibling analysis tools, the lack of any usage context leaves the agent to infer when this aggregator is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds little beyond confirming a read-like operation; it does not disclose any additional behavioral traits like output size or error conditions. This is consistent with annotations, so 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that encodes purpose and scope without any fluff. It is concise and appropriately structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter tool with complete schema descriptions and an output schema, the description is largely sufficient. It lacks usage guidance but does not need to explain return values since an output schema exists. The gap in usage guidance prevents a perfect score.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, as both 'code' and 'language' have descriptive text. The description itself adds no parameter-specific information beyond what the schema already provides, so it meets the baseline for schema-driven clarity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function with a specific verb ('Return'), a specific resource ('detected architectural patterns'), and scope ('for a code snippet'). It distinguishes itself from sibling tools like detect_languages and analyze_zen_violations by focusing on architectural patterns.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance is provided about when to use this tool versus alternatives. The description lacks prerequisites, exclusions, or any mention of better-suited sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds no extra behavioral context (e.g., prerequisites, side effects) beyond the core conversion purpose, so it meets the lowered bar but does not enrich beyond it.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that front-loads the verb and purpose. Every word earns its place, with no unnecessary fluff or repetition of schema details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's read-only, idempotent nature and the presence of an output schema, the short description is largely sufficient. It misses some workflow context (e.g., that it likely consumes results from analyze_zen_violations), but this is not critical for selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so all five parameters are already documented in the input schema. The description does not add any additional parameter meaning or usage context, matching the baseline for full coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Convert') and clearly states the resource ('zen violations') and output ('structured agent task lists'). This distinguishes it from sibling tools like analyze_zen_violations (which likely analyzes violations) and generate_prompts (which generates prompts), making its unique role clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is used after obtaining zen violations, but it does not explicitly state when to use it versus alternatives or provide workflow context. No exclusions are mentioned, and the positioning relative to sibling tools is left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already flag the operation as destructive, and the description adds that overrides persist for the session, which is valuable context beyond the annotations. However, it does not disclose whether overrides affect downstream analysis tools or what happens on repeated calls.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core action, and contains no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (7 parameters, output schema), the description is minimal. It does not explain how overrides interact with other tools or how to reset them, though the schema covers parameters and the output schema presumably covers return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All parameters are fully described in the schema (100% coverage), and the description provides no additional parameter-level detail beyond the schema, so the baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the action (override), the resource (configuration values), and the scope (specific language at runtime). It distinguishes the tool from siblings like get_config and clear_config_overrides.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for temporary runtime changes and notes session persistence, but it does not explicitly compare with alternatives or state when not to use it (e.g., when permanent changes are needed). It could mention clear_config_overrides for resetting.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering safety. The description adds meaningful behavioral context by revealing token budgeting, severity ordering, and cursor-based pagination, which are not visible from annotations alone. This goes beyond schema parameter descriptions and helps the agent anticipate the tool's output characteristics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact: two sentences capture the main purpose and the crucial pagination mechanism. It is front-loaded with the core action and outcome, and no unnecessary details or repetition inflate the length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema present, the description need not explain return values. It adequately conveys the primary function, token budget, ordering, and resume behavior, which are the key operational aspects. The only notable gap is the lack of guidance on choosing this tool over sibling analysis tools, but given the schema and annotation coverage, the description remains sufficiently complete for a 7-parameter tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description does not add extra parameter-level insight beyond what the schema already provides; it simply reflects the overall token-budget and pagination behavior. No additional syntax, format, or edge-case details are given, so it meets but does not exceed the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action (analyse a repository path) and its specific output (token-budgeted, paginated violations) with a memorable detail (highest-severity first). The title adds 'batch / LLM-safe,' which helps distinguish it from sibling tools like analyze_repository without needing to name them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the intended use case ('designed for LLM context windows') but does not explicitly say when to use this tool over alternatives such as analyze_repository or analyze_batch_summary. It lacks any exclusion criteria or direct mention of alternative tools, so the guidance is implicit rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds the context 'from the live registry', indicating the data source, but doesn't elaborate on other behavioral aspects. With annotations present, this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence that conveys the purpose and source without waste. It earns its place with no redundancy or extraneous detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (one optional param, no required params) and has an output schema plus thorough annotations. The description, while brief, sufficiently covers the core purpose and source. It could mention the output shape, but the output schema exists, so this is acceptable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%. The schema already thoroughly explains the 'languages' parameter, including behavior when omitted and default semantics. The description adds no additional parameter information, which is fine since the baseline is 3 for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb ('Generate') and resource ('rule-detector mapping JSON') with a source ('from the live registry'). This uniquely distinguishes it from sibling tools like detect_languages or generate_prompts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies a clear use case: obtaining the current rule-detector mapping. While it doesn't explicitly state when not to use alternatives, the context is clear and no exclusions are needed given the distinct purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds the detail that the return includes 'zen rules and their detector coverage', which is content-specific but does not disclose additional behavioral traits such as ordering, pagination, or potential error modes. With annotations present, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that front-loads the primary action ('Return list') and specifies what the list contains. There is no redundant information or padding, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that the tool has no parameters, an output schema exists, and the description clearly states the purpose and content, the description is sufficient. It does not need to explain return values because the output schema is provided. The description covers the essential context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4. The description does not need to explain parameters, and the input schema already confirms that no parameters are required. No additional semantic information is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Return') and identifies the exact resource ('list of all languages with zen rules and their detector coverage'). This clearly distinguishes it from sibling tools like detect_languages, which likely performs language detection rather than listing supported languages.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage (e.g., when you need to know which languages have zen rules), but it does not explicitly state when to use this tool versus alternatives or provide any exclusion criteria. No mention of specific use cases or scenarios is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds 'interactive' and 'based on project characteristics', giving insight into the tool's behavior beyond what annotations capture. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the primary action and outcome. Every word earns its place; no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only onboarding tool with a full input schema and output schema, the description is adequate. It could add slightly more detail about what 'interactive' entails or when to use it, but overall it is complete enough given the strong annotations and schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema provides 100% coverage with detailed descriptions for all four parameters. The description only refers generically to 'project characteristics', not adding specific meaning beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Get interactive onboarding guidance') and resource ('setting up zen analysis on a project'), clearly distinguishing it from sibling analysis tools like analyze_zen_violations or analyze_repository. It also states the return value: recommended configuration.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies the tool is for initial onboarding/setup of zen analysis, which distinguishes it from analysis/reporting tools. However, it does not explicitly name alternatives or state when not to use it, so it lacks explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare destructiveHint=true. The description adds context beyond that by specifying that ALL overrides are cleared and that the config reverts to zen-config.yaml defaults, clarifying the scope and target of the destructive action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no wasted words. It clearly states the action and outcome in a compact format.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity (0 params, destructive behavior) and presence of annotations (destructiveHint) plus an output schema, the description is fully complete. It specifies what is cleared and the resulting state, with no gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so baseline is 4. The description adds meaning by clarifying that 'all' overrides are cleared, which is not represented in the empty schema. No parameter-level detail is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'clear' and identifies the resource as 'runtime configuration overrides', with the effect 'reverting to zen-config.yaml defaults'. This clearly distinguishes it from siblings like set_config_override (which adds a specific override) and get_config (which reads config).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you need to reset all overrides, but does not explicitly mention when to use this tool versus set_config_override or get_config. No exclusions or alternative tools are named, 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds useful context that the returned configuration includes runtime overrides, which tells the agent that changes from set_config_override are reflected. This goes beyond the annotations without contradicting them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence with no unnecessary words. It front-loads the action and resource immediately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no parameters, comprehensive annotations, and an output schema present, the description covers all necessary context. It mentions runtime overrides, which is important for a configuration read, and there is no need to explain return values given the output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool takes zero parameters (empty schema), so the baseline of 4 applies. The description does not need to explain parameter semantics because none exist.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Return' and names the resource 'current server configuration', further clarifying scope with 'including any runtime overrides'. This clearly distinguishes it from sibling tools like set_config_override and clear_config_overrides, which are write operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The usage context is implied by the read-only nature of the tool, but the description does not explicitly state when to use it versus alternatives like set_config_override. It gives no exclusions or alternative tool names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, covering safety. The description adds valuable behavioral context by guaranteeing the output 'Always fits within a single LLM context window' and indicating the compact, top-5 nature of the result. This goes beyond annotations and helps the agent understand the tool's practical impact.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with purpose and output, followed by usage guidance. There is zero filler or redundancy; every word contributes to understanding the tool's role.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema present, return values are already specified. The description covers purpose, usage context, and a key behavioral constraint (context window fit). It is complete enough for an agent to decide on invocation, though it could briefly mention what constitutes a 'hotspot' but that is not essential.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (all 5 parameters have descriptions). The description does not add meaning beyond the schema, but the schema itself fully documents all parameters, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Return') and a clear resource/output: a compact project health score and top-5 hotspot files. It explicitly distinguishes from sibling 'analyze_batch' by positioning itself as a pre-pagination summary, making its purpose unmistakable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides an explicit when-to-use directive: 'Use this before analyze_batch to decide whether full pagination is needed.' This clearly positions it relative to a specific alternative and implies when the full version would be needed, giving actionable usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint, idempotentHint, destructiveHint=false), the description discloses the auto-routing behavior between small and large repos, and the cursor-based pagination mechanism. This adds valuable context about how the tool behaves at runtime, which annotations alone do not provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core purpose ('Smart entry point'), and every phrase earns its place. It efficiently conveys the auto-routing logic, pagination guidance, and sibling preference without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (7 params, 2 required), the description sufficiently covers the essential behavior and usage. Since an output schema exists, return values like has_more are already defined; the description adds the necessary context about auto-routing and pagination, making it complete for an agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so all parameters are already described. The description adds minimal parameter-level meaning; it only reinforces the cursor's role in pagination ('Pass the returned cursor back') without introducing new details beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'automatically decides between returning all violations at once (small repos) or paginating (large repos).' It distinguishes from siblings by explicitly recommending it over analyze_repository and analyze_batch, making it a distinct and purposeful entry point.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'Prefer this over manually choosing between analyze_repository and analyze_batch.' It also explains the pagination flow with 'Pass the returned cursor back to continue pagination if has_more is true,' giving clear step-by-step instructions for using the tool correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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