Provides coding standards and quality validation for AI coding agents, configurable in JetBrains IDEs via the AI Assistant MCP integration.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@corbat Check this code for coding standards violations"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Shared standards, context, and verification gates for agent-assisted software delivery.
Designed for MCP-compatible coding tools and multi-agent workflows. See compatibility notes.
⚡ Try it in 30 seconds — just add the config below and start coding.
AI-generated code often works locally but misses team-specific review expectations:
Without Corbat | With Corbat |
Methods with 50+ lines | Max 20 lines per method |
No dependency injection | Proper DI with interfaces |
| Custom exceptions with context |
Missing or minimal tests | Tests included, TDD approach |
God classes, mixed concerns | SOLID principles, clean layers |
Works locally | Reviewable implementation with explicit checks |
Corbat helps by giving agents your team's standards before implementation and by providing validation tools before review or handoff.
Related MCP server: Hivelore
1. Add to your MCP config:
{
"mcpServers": {
"corbat": {
"command": "npx",
"args": ["-y", "@corbat-tech/coding-standards-mcp"]
}
}
}2. Config file location:
Tool | Location |
Cursor |
|
VS Code |
|
Windsurf |
|
JetBrains | Settings → AI Assistant → MCP |
Claude Desktop |
|
Claude Code |
|
Complete setup guide for common MCP-compatible tools
3. Done. Corbat can auto-detect your stack and return matching standards.
Zero overhead. Corbat runs locally and adds ~50ms to detect your stack. After that, it's just context for the AI.
Fully customizable. Don't like a rule? Override it in
.corbat.json. Jump to customization →
You: "Create a user registration service"Without Corbat, you get:
class UserService {
private users: Map<string, User> = new Map();
register(name: string, email: string, password: string) {
if (!name || !email || !password) throw new Error('Invalid input');
const user = { id: crypto.randomUUID(), name, email, password };
this.users.set(user.id, user);
return user;
}
}
// ✗ Hardcoded storage ✗ Password in plain text ✗ Generic error ✗ No testsWith Corbat, you get:
// Domain
interface UserRepository {
save(user: User): Promise<void>;
findByEmail(email: string): Promise<User | null>;
}
class EmailAlreadyExistsError extends Error {
constructor(email: string) {
super(`Email already registered: ${email}`);
}
}
// Application
class RegisterUserUseCase {
constructor(
private readonly repository: UserRepository,
private readonly hasher: PasswordHasher
) {}
async execute(input: RegisterUserInput): Promise<User> {
const existing = await this.repository.findByEmail(input.email);
if (existing) throw new EmailAlreadyExistsError(input.email);
const user = User.create({
...input,
password: await this.hasher.hash(input.password)
});
await this.repository.save(user);
return user;
}
}
// ✓ Repository interface ✓ Password hashing ✓ Custom error ✓ Testable// Test included
describe('RegisterUserUseCase', () => {
const repository = { save: vi.fn(), findByEmail: vi.fn() };
const hasher = { hash: vi.fn() };
const useCase = new RegisterUserUseCase(repository, hasher);
beforeEach(() => vi.clearAllMocks());
it('should hash password before saving', async () => {
repository.findByEmail.mockResolvedValue(null);
hasher.hash.mockResolvedValue('hashed_password');
await useCase.execute({ name: 'John', email: 'john@test.com', password: 'secret' });
expect(hasher.hash).toHaveBeenCalledWith('secret');
expect(repository.save).toHaveBeenCalledWith(
expect.objectContaining({ password: 'hashed_password' })
);
});
it('should reject duplicate emails', async () => {
repository.findByEmail.mockResolvedValue({ id: '1', email: 'john@test.com' });
await expect(
useCase.execute({ name: 'John', email: 'john@test.com', password: 'secret' })
).rejects.toThrow(EmailAlreadyExistsError);
});
});This is the kind of structure Corbat asks agents to consider before handoff.
Corbat provides these guardrails before generation and validation checks during iteration:
Rule | Why It Matters |
Max 20 lines per method | Readable, testable, single-purpose functions |
Max 200 lines per class | Single Responsibility Principle |
Meaningful names | No |
No magic numbers | Constants with descriptive names |
Rule | Why It Matters |
Interfaces for dependencies | Testable code, easy mocking |
Layer separation | Domain logic isolated from infrastructure |
Hexagonal/Clean patterns | Framework-agnostic business rules |
Rule | Why It Matters |
Custom exceptions |
|
Error context | Include IDs, values, state in errors |
No empty catches | Every error handled or propagated |
Rule | Why It Matters |
Input validation | Reject bad data at boundaries |
No hardcoded secrets | Environment variables only |
Parameterized queries | Prevent SQL injection |
Output encoding | Prevent XSS |
We evaluated Corbat across 15 scenarios in 6 languages. The primary benchmark report is mixed and should be read honestly: Corbat won 1/15 scenarios by the original aggregate score, while an alternative value analysis found stronger results for code compactness and maintainability.
The strongest observed signal is that Corbat-guided outputs are often smaller and more focused:
Scenario | With Corbat | Without Corbat | What This Means |
Kotlin Coroutines | 236 lines | 1,923 lines | Same functionality, 8x less to maintain |
Java Hexagonal | 623 lines | 2,740 lines | Clean architecture without the bloat |
Go Clean Arch | 459 lines | 2,012 lines | Idiomatic Go, not Java-in-Go |
TypeScript NestJS | 395 lines | 1,554 lines | Right patterns, right size |
This is not enough to claim universal quality improvement. It is evidence that standards context can reduce over-generation in some workflows.
The value report reweights the same dataset toward efficiency and maintainability:
Metric | Result | What It Means |
Code Reduction | 67% | Less to maintain, review, and debug |
Security checks | 100% | No issues detected by benchmark pattern checks |
Maintainability | 93% win | Easier to understand and modify |
Architecture Efficiency | 87% win | Better patterns per line of code |
Cognitive Load | -59% | Faster onboarding for new developers |
Every scenario was analyzed using pattern checks inspired by OWASP Top 10 categories. This is not a replacement for SAST, DAST, dependency scanning, manual review, or threat modeling.
No SQL/NoSQL injection patterns detected
No XSS patterns detected
No hardcoded credentials detected
Input validation patterns present at boundaries
Error messages did not expose stack traces in the benchmark samples
Language | Scenarios | Patterns |
☕ Java | 5 | Spring Boot, DDD Aggregates, Hexagonal, Kafka Events, Saga |
📘 TypeScript | 4 | Express REST, NestJS Clean, React Components, Next.js Full-Stack |
🐍 Python | 2 | FastAPI CRUD, Repository Pattern |
🐹 Go | 2 | HTTP Handlers, Clean Architecture |
🦀 Rust | 1 | Axum with Repository Trait |
🟣 Kotlin | 1 | Coroutines + Strategy Pattern |
📖 Full benchmark methodology · Value analysis
Corbat auto-detects your stack and applies the right standards:
Profile | Stack | What You Get |
| Java 21 + Spring Boot 3 | Hexagonal + DDD, TDD with 80%+ coverage |
| Kotlin + Spring Boot 3 | Coroutines, Kotest + MockK |
| Node.js + TypeScript | Clean Architecture, Vitest |
| Next.js 14+ | App Router patterns, Server Components |
| React 18+ | Hooks, Testing Library, accessible components |
| Vue 3.5+ | Composition API, Vitest |
| Angular 19+ | Standalone components, Jest |
| Python + FastAPI | Async patterns, pytest |
| Go 1.22+ | Idiomatic Go, table-driven tests |
| Rust + Axum | Ownership patterns, proptest |
| C# 12 + ASP.NET Core 8 | Clean + CQRS, xUnit |
| Dart 3 + Flutter | BLoC/Riverpod, widget tests |
Auto-detection: Corbat reads pom.xml, package.json, go.mod, Cargo.toml, etc.
Use Case | Why Corbat Helps |
Starting a new project | Correct architecture from day one |
Multi-agent delivery | Planner, implementation, review, and security agents share the same policy context |
Teams with mixed experience | Standards become explicit and repeatable |
Strict code review standards | Agents can validate against the review bar before handoff |
Regulated industries | Consistent security and documentation |
Legacy modernization | New code follows modern patterns |
Quick prototypes where quality doesn't matter
One-off scripts you'll throw away
Learning projects where you want to make mistakes
npx corbat-initDetects your stack and generates a .corbat.json with sensible defaults.
Create .corbat.json in your project root:
{
"profile": "java-spring-backend",
"architecture": {
"pattern": "hexagonal",
"layers": ["domain", "application", "infrastructure", "api"]
},
"quality": {
"maxMethodLines": 20,
"maxClassLines": 200,
"minCoverage": 80
},
"rules": {
"always": [
"Use records for DTOs",
"Prefer Optional over null"
],
"never": [
"Use field injection",
"Catch generic Exception"
]
}
}Browse 14 ready-to-use templates for Java, Python, Node.js, React, Go, Rust, and more.
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Your Prompt │────▶│ Corbat MCP │────▶│ AI + Rules │
└─────────────┘ └──────┬──────┘ └─────────────┘
│
┌──────────────┼──────────────┐
▼ ▼ ▼
┌────────────┐ ┌────────────┐ ┌────────────┐
│ 1. Detect │ │ 2. Load │ │ 3. Inject │
│ Stack │ │ Profile │ │ Guardrails │
└────────────┘ └────────────┘ └────────────┘
pom.xml hexagonal max 20 lines
package.json + DDD + interfaces
go.mod + SOLID + custom errorsCorbat does not modify AI output. It supplies context, standards, profiles, and validation feedback so agents can align with your review expectations.
Important: Actual code quality depends on the model, host tool, prompt, repository context, tests, and human review. Treat Corbat as a policy and verification layer, not as a guarantee of production readiness.
Resource | Description |
Installation notes for common MCP-compatible tools | |
Ready-to-use | |
Full list of supported AI tools | |
Detailed results from 15 scenarios | |
How Corbat fits planner/reviewer/security/release agent workflows | |
Local execution model, threat model, and reporting | |
Tools, prompts, and configuration options |
Add to your MCP config and you're done:
{ "mcpServers": { "corbat": { "command": "npx", "args": ["-y", "@corbat-tech/coding-standards-mcp"] }}}Use Corbat as shared standards context plus a local quality gate.
Developed by corbat-tech
Returns coding standards, guardrails, and workflow for implementing a task.
WHEN TO USE:
ALWAYS call this FIRST before writing any code
When starting a new feature, bugfix, or refactor
When unsure about project conventions
RETURNS:
Detected stack (Java/Python/TypeScript/Go/Rust/etc)
Task type classification (feature/bugfix/refactor/test/security/performance)
MUST rules (mandatory guidelines)
AVOID rules (anti-patterns to prevent)
Code quality thresholds (max lines, coverage %)
Naming conventions (classes, methods, files)
Recommended TDD workflow
EXAMPLE: get_context({ task: "Create payment service", project_dir: "/path/to/project" })
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | What you're implementing (e.g., "Create payment service", "Fix login bug") | |
| project_dir | No | Project directory for auto-detection of stack and .corbat.json config (optional) |
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.
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.
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.
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.
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.
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.
Check server status, loaded profiles, and usage metrics.
RETURNS:
Server status (OK/ERROR)
Version
Load time
Profiles loaded
Standards documents count
Usage metrics (tool calls, most used profile)
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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.
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.
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.
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.
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.
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.
Suggest a .corbat.json configuration for a project.
WHEN TO USE:
Setting up Corbat for a new project
Want to customize coding standards for a project
Need to see available profiles and options
Analyzes the project directory and suggests optimal configuration based on detected stack.
RETURNS:
Detected stack information
Suggested .corbat.json content
Available profiles list
Setup instructions
| Name | Required | Description | Default |
|---|---|---|---|
| project_dir | Yes | Project directory to analyze |
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.
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.
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.
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.
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.
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.
List all available coding standards profiles.
RETURNS: List of profiles with ID and description. Profiles include:
java-spring-backend: Enterprise Java with Hexagonal Architecture
nodejs: Node.js/TypeScript with Clean Architecture
react, vue, angular: Frontend frameworks
python: FastAPI/Django
go, rust: Systems programming
And more...
Use profile ID in .corbat.json or get_context will auto-detect.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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.
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.
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.
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.
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.
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.
Search standards documentation for specific topics.
WHEN TO USE:
Looking for specific technology guidance (kafka, docker, kubernetes)
Need detailed information on a pattern or practice
Exploring available standards
EXAMPLE QUERIES: "kafka", "testing", "docker", "logging", "metrics", "archunit", "flyway"
RETURNS: Up to 5 matching results with excerpts from documentation.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query (e.g., "kafka", "testing", "docker") |
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.
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.
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.
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.
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.
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.
Analyze code against coding standards with language-aware checks and heuristic fallback.
WHEN TO USE:
After writing code, to check for issues
During iterative development
Before calling verify for final approval
PERFORMS ANALYSIS:
Detects anti-patterns (empty catch, hardcoded secrets, etc.)
Measures method/class lengths where supported
Checks for interfaces and tests
Calculates quality score
RETURNS:
Score (0-100)
CRITICAL issues (must fix)
WARNINGS (should fix)
Metrics (lines, methods, tests, etc.)
PASSED/NEEDS WORK verdict
EXAMPLE: validate({ code: "public class UserService { ... }", task_type: "feature" })
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | The code to validate | |
| task_type | No | Type of task for context-aware validation (optional) |
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.
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.
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.
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.
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.
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.
Verify generated code before handoff.
WHEN TO USE:
After generating code
Before review, handoff, or final response
As a final quality gate in agent workflows
WHAT IT CHECKS:
Tests are provided (TDD compliance)
Interfaces exist (DI compliance)
No critical code issues
Quality score >= 50
RETURNS:
PASS: No blocking issues detected by configured checks
FAIL: Issues to fix, iterate and verify again
WORKFLOW:
Generate code following get_context guidelines
Call verify({ code, tests, interfaces })
If FAIL: fix issues and call verify again
If PASS: proceed to the next workflow step
EXAMPLE: verify({ code: "class UserServiceImpl...", tests: "describe('UserService')...", interfaces: "interface UserService..." })
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | All implementation code | |
| tests | No | All test code (REQUIRED for TDD compliance) | |
| task_type | No | Type of task for context-aware verification | |
| interfaces | No | All interfaces and type definitions |
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.
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.
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.
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.
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.
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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v3.0.0get_contexthealthinitprofilessearchvalidateverifyEach 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.
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.
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.
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.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Security reviews for coding agents: diffs checked against your org policy and live infrastructure.
Production-readiness for your AI coding agents.
Security gateway for AI agents: policy, approval, and audited execution, no secrets shared.
Pre-execution governance for AI agents. Deterministic PASS/FAIL/REVIEW verdicts, replayable proof.
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