corbat

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by corbat-tech

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Server Quality Checklist

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Latest release: v3.0.0
Disambiguation5/5
Each tool has a unique and clearly defined purpose: get_context for standards, health for server status, init for configuration, profiles for listing profiles, search for documentation, validate for code analysis, and verify for final validation. No overlap.
Naming Consistency5/5
All tools follow a simple, consistent pattern of lowercase verbs or verb_noun (e.g., get_context, validate, verify). There is no mixing of conventions or ambiguous names.
Tool Count5/5
With 7 tools, the set is well-scoped for the server's purpose of coding standards and quality assistance. Each tool covers a distinct aspect of the workflow without being too few or excessive.
Completeness4/5
The tool surface covers the core workflow: acquiring standards, validating code, and final verification. A minor gap is the absence of an automated fix tool, but the iterative validate-and-fix workflow compensates.
Average 4.4/5 across 7 of 7 tools scored. Lowest: 3.7/5.
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
- No stable releases found
- 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.
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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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It transparently lists return values, implying a read-only operation without side effects. However, it does not mention auth needs or rate limits, which are minor for a health check.
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?
Extremely concise: one sentence defining the action followed by a bulleted list of returns. Every word is relevant, and the structure is front-loaded.
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 health check tool with no parameters or output schema, the description fully covers what it does and what it returns. It lacks sibling differentiation but otherwise is complete.
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?
No parameters exist, so the description inherently adds meaning beyond the schema. According to guidelines, 0 params baseline is 4.
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 it checks server status, loaded profiles, and usage metrics, providing a specific verb and resource. It lists return fields, distinguishing it from siblings like 'profiles' which likely focus on profile details, but does not explicitly differentiate.
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 like 'get_context' or 'profiles'. The description only explains what the tool does, not when it should be chosen.
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?
No annotations are provided, so the description carries the full burden. It describes the return format (list of profiles with ID and description) and gives examples, but does not disclose any potential side effects, authentication requirements, or data freshness. For a read-only list, this is adequate but could be more transparent.
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 fairly concise, front-loading the main purpose and then providing examples. It is structured with a clear sentence followed by a bulleted list. Could be slightly more streamlined, but overall 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?
The tool has no output schema, so the description must explain return values. It clearly states it returns a list of profiles with ID and description, and provides concrete examples. This is complete for a simple list tool with no parameters.
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 adds value by listing example profiles and explaining their practical use, which helps the agent understand the context beyond the schema.
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 'List all available coding standards profiles' with a specific verb and resource. It distinguishes from sibling tools like get_context by explaining how profiles are used in conjunction, and provides examples of available profiles.
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 explains when to use the tool (to list profiles) and how to use the results (via .corbat.json or get_context auto-detection). However, it does not explicitly state when not to use it or compare to other siblings like search or validate, but the context is sufficient for a simple listing tool.
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?
With no annotations, the description fully describes behavior: it analyzes a project directory and suggests configuration. It lists return values (stack info, suggested content, profiles, instructions). It doesn't mention side effects or permissions, but for a suggestion tool that is acceptable.
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 extremely concise: a single sentence header followed by bullet points. Every word contributes. It is front-loaded and structured with WHEN TO USE and RETURNS sections, making it easy to scan.
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?
For a simple tool with one parameter and no output schema, the description provides all necessary context: purpose, usage guidelines, and return values. No additional information is needed for an agent to correctly select and invoke this 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?
The only parameter 'project_dir' is already described in the schema as 'Project directory to analyze'. The description adds no extra semantics beyond that, so baseline 3 is appropriate given 100% schema 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 the tool's purpose: 'Suggest a .corbat.json configuration for a project.' The verb 'suggest' combined with the specific resource '.corbat.json configuration' makes it distinct from siblings like 'validate' or 'profiles', which do different things.
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 'WHEN TO USE' section explicitly lists scenarios: setting up Corbat, customizing coding standards, seeing available profiles. This provides clear context. Although it doesn't explicitly say when not to use it, the positive cases are well-covered.
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?
No annotations provided, so description carries full burden. It discloses that up to 5 matching results with excerpts are returned, but does not mention behavior for empty queries or other edge cases.
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 short paragraphs with clear headings (WHEN TO USE, EXAMPLE QUERIES, RETURNS) – every sentence adds value, 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?
Given no output schema and a single parameter, the description provides sufficient context (return limit and format). Could mention result ordering or ranking, but not critical.
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% and the description adds example queries, but the parameter description in the schema already adequately explains the 'query' field, so minimal added 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 the tool searches 'standards documentation for specific topics' and provides example queries (kafka, docker, etc.), distinguishing it from sibling tools like get_context or health.
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?
Includes explicit 'WHEN TO USE' section with scenarios and example queries, making it easy for the agent to decide when to invoke this tool versus alternatives.
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?
Despite no annotations, the description details what the tool analyzes (anti-patterns, lengths, interfaces, tests) and returns (score, issues, warnings, metrics, verdict). Could mention that it does not modify code, but overall covers behavioral traits well.
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?
Well-structured with clear sections (WHEN TO USE, PERFORMS ANALYSIS, RETURNS, EXAMPLE). Every sentence adds value, and the format is front-loaded and 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 no output schema, the description fully details the return structure and covers usage context, analysis scope, and intended workflow. No obvious 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 coverage is 100%, so baseline is 3. The description adds an example usage but does not significantly elaborate on parameter meanings beyond what the schema provides.
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 ('Analyze') and resource ('code against coding standards') and distinguishes from sibling 'verify' by noting it is used before final approval.
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?
Explicitly lists when to use (after writing code, during development) and when not to use (before verify for final approval), providing clear context and alternatives.
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?
With no annotations, the description fully discloses behavior: it returns a structured set of information (stack, task type, rules, thresholds, naming conventions, workflow) and includes an example. No side effects are implied, and the read-only nature is clear.
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 concise and well-structured with sections for purpose, usage, returns, and an example. Every sentence adds value, and critical information is front-loaded.
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 purpose (returning context for coding), the description covers all necessary details: when to use, what it returns (including detailed items like naming conventions and TDD workflow), and a usage example. No output schema exists, but the return structure is well described.
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%, so the baseline is 3. The description adds an example call but does not elaborate on parameter semantics beyond what the schema already provides. The example is helpful but not essential.
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 it returns coding standards, guardrails, and workflow, with a specific verb 'Returns' and resource 'context'. It uniquely distinguishes from sibling tools like init, search, and verify, 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.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The 'WHEN TO USE' section explicitly advises calling this tool FIRST before writing any code, for new features, bugfixes, or when unsure about conventions. This strong directive is highly actionable.
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?
The description fully discloses what the tool checks (TDD compliance, DI compliance, no critical issues, quality score >= 50) and what it returns (PASS/FAIL). With no annotations provided, the description carries the full burden and does so comprehensively.
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 well-structured with clear sections (WHEN TO USE, WHAT IT CHECKS, RETURNS, WORKFLOW, EXAMPLE). It is concise yet complete, with no redundant sentences.
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 4 parameters, no output schema, and no annotations, the description covers purpose, usage, checks, return values, and workflow comprehensively. It provides all necessary context for an agent to use the tool correctly.
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?
Schema coverage is 100%, baseline 3. The description adds context by explaining parameter roles in the workflow (e.g., tests required for TDD compliance) and includes an example. It goes beyond schema descriptions but is not highly additive.
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: 'Verify generated code before handoff.' It uses specific verb 'verify' and resource 'generated code', and distinguishes from siblings like 'validate' by focusing on code generation workflow.
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 'WHEN TO USE' section explicitly states when to use (after generating code, before review/handoff) and provides a step-by-step workflow. It effectively guides the agent on appropriate usage.
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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