Click on "Deploy 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., "@abac-validation-mcp-server Review security of my ABAC policy code."
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.
An MCP (Model Context Protocol) server that provides AI-assisted ABAC (Attribute-Based Access Control) code validation and review tools. This server integrates with Claude CLI or GitHub Copilot CLI to perform long-running validation operations with progress notifications.
Business Logic Validation: Validate code against business requirements using AI
AI-Assisted Code Review: Comprehensive code review covering quality, security, and performance
Batch Processing: Validate multiple files with progress updates
Progress Notifications: Long-running operations report progress to the client
CLI Integration: Calls Claude/Copilot CLI for AI analysis (ready for integration)
Related MCP server: Argus MCP
npm install
npm run buildnpm run devAdd to your VS Code settings.json (.vscode/settings.json or user settings):
{
"mcp.servers": {
"abac-validation": {
"type": "stdio",
"command": "node",
"args": [
"/absolute/path/to/abac-validation-mcp-server/dist/index.js"
]
}
}
}Or use npx for easier distribution:
{
"mcp.servers": {
"abac-validation": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"abac-validation-mcp-server"
]
}
}
}Add to claude_desktop_config.json:
{
"mcpServers": {
"abac-validation": {
"command": "node",
"args": [
"/absolute/path/to/abac-validation-mcp-server/dist/index.js"
]
}
}
}Validate code against business requirements using AI.
Parameters:
code (string, required): The code to validate
requirements (string, required): Business requirements to validate against
Example:
{
"code": "function calculateDiscount(price, customerType) { ... }",
"requirements": "Premium customers get 20% discount, regular customers get 10%"
}Perform comprehensive AI-assisted code review.
Parameters:
code (string, required): The code to review
context (string, optional): Additional context about the code
review_type (string, optional): Type of review - "comprehensive", "security", "performance", or "style"
Example:
{
"code": "async function fetchUserData(userId) { ... }",
"context": "This is part of a user authentication system",
"review_type": "security"
}Validate multiple files in batch with progress updates.
Parameters:
files (array, required): Array of file objects with path and optional requirements
Example:
{
"files": [
{ "path": "src/auth.ts", "requirements": "Must use OAuth 2.0" },
{ "path": "src/payment.ts", "requirements": "PCI DSS compliant" }
]
}The server skeleton is ready with placeholder implementations. To integrate with actual CLIs:
Replace the TODO sections in src/index.ts with:
// Install: npm install -g @anthropic-ai/claude-cli
const result = await executeWithProgress('claude', [
'analyze',
'--prompt', prompt
], (msg) => {
if (progressToken) {
// Send progress notification
}
});// Requires GitHub Copilot CLI access
const result = await executeWithProgress('gh', [
'copilot',
'explain',
code
], (msg) => {
if (progressToken) {
// Send progress notification
}
});For more control, use the Anthropic API directly:
npm install @anthropic-ai/sdkimport Anthropic from '@anthropic-ai/sdk';
const client = new Anthropic({
apiKey: process.env.ANTHROPIC_API_KEY
});
// Use streaming for progress
const stream = await client.messages.create({
model: 'claude-sonnet-4-5-20250929',
messages: [{ role: 'user', content: prompt }],
stream: true,
max_tokens: 4096
});
for await (const event of stream) {
// Send progress updates
}npm run buildnpm run watchTest the server with MCP Inspector:
npx @modelcontextprotocol/inspector node dist/index.jsIntegrate CLI: Choose your preferred AI CLI (Claude/Copilot) and implement in the TODO sections
Add Progress Notifications: Use MCP progress notification protocol for real-time updates
File System Access: Add file reading capabilities for batch validation
Configuration: Add config file for API keys, CLI paths, etc.
Error Handling: Improve error handling and retry logic
Caching: Add caching for repeated validations
Custom Validators: Add domain-specific validation rules
User (VS Code/Copilot/Claude)
↓
MCP Client
↓ (stdio)
Validation MCP Server
↓
Claude CLI / Copilot CLI / API
↓
AI Model (validation/review)
↓
Results with ProgressMIT
Analyze cross-component impact of code changes on RMS and MIP. This is a detailed analysis tool that runs Claude CLI against RMS and MIP codebases to identify dependencies, breaking changes, and integration issues.
| Name | Required | Description | Default |
|---|---|---|---|
| codeChanges | Yes | The code changes to analyze | |
| analysisType | No | Type of analysis to perform (default: comprehensive) | |
| targetComponents | Yes | Components to check for impact |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. The description does mention that this runs Claude CLI against RMS and MIP codebases, which is a nontrivial behavioral detail (external process execution) that an agent would want to know. However, it doesn't disclose side effects (if any), cost/time implications of running CLI analysis, or permission requirements. For a non-mutating analysis tool, the disclosure is reasonable but could be richer.
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 sentences, reasonably front-loaded with the purpose stated clearly. The first sentence is concise and informative. The second sentence adds context about methodology. Slight redundancy between 'detailed analysis' and 'runs Claude CLI' but no wasted words.
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 no annotations, the description should compensate more. It doesn't describe what the analysis output looks like (report format, findings structure), whether results are returned directly or saved somewhere, or how long execution may take given it runs CLI commands. For a tool that triggers external processes, these operational details are meaningful. The 3-parameter schema is fully covered in the schema itself, which helps, but the tool's execution model remains underspecified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema documents all 3 parameters well. The description adds context about what the tool does with these inputs (runs analysis against RMS/MIP codebases), but doesn't add semantic detail beyond the schema, such as how codeChanges should be formatted or how analysisType affects output. Baseline 3 is appropriate when the schema carries the documentation weight.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb (analyze) + resource (cross-component impact on RMS and MIP) and describes what it produces (dependencies, breaking changes, integration issues). The purpose is clear and specific. However, it doesn't explicitly distinguish this from sibling tools like 'validate_abac_changes', though the distinct focus on component impact analysis provides implicit differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use it (when analyzing code changes across RMS/MIP components), but provides no explicit when-not-to-use guidance or alternatives. The sibling tools (validate_abac_changes, get_current_changes, generate_validation_questions) are not referenced to help disambiguate use cases. The description states methodology (runs Claude CLI), which provides context, but no exclusions or alternative guidance means the agent must infer fit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate pre-defined validation questions based on code changes. Returns a list of relevant questions that should be answered for ABAC compliance and cross-component compatibility.
| Name | Required | Description | Default |
|---|---|---|---|
| filePath | Yes | Path of the file being changed | |
| codeChanges | Yes | The code changes to analyze |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral disclosure burden. It indicates the tool is a read-only generation operation returning 'a list of relevant questions,' which implies non-destructive behavior. However, it doesn't disclose whether it depends on upstream data (like get_current_changes), whether results vary deterministically, or any preconditions on the codeChanges format. Some value is added but key behavioral aspects are unaddressed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient two-sentence block with no wasted words. It front-loads the core purpose and ends with the domain context. Slightly more structured detail (e.g., listing the two compliance domains more explicitly) could be added without padding, but it's appropriately concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 2 parameters, no output schema, and no annotations, the description must carry substantial information. It explains the purpose and domain context but is thin on behavioral specifics: it doesn't describe the return format, the nature of the generated questions, whether filePath affects question selection, or how this integrates with the sibling validation tools. Adequate but with clear gaps given the zero-annotation context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters (filePath and codeChanges). The description adds minimal parameter meaning beyond noting that codeChanges are 'the code changes to analyze' — it clarifies the relationship between the parameters and the output but provides no format guidance. This meets the baseline 3 but doesn't exceed it.
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 'Generate pre-defined validation questions based on code changes' with a specific verb (generate), resource (validation questions), and scope (based on code changes, for ABAC compliance and cross-component compatibility). It's clear but doesn't explicitly differentiate it from its siblings like validate_abac_changes or analyze_cross_component_impact, which appear related to similar validation/analysis concerns.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions the purpose ('for ABAC compliance and cross-component compatibility') but provides no explicit guidance on when to use this tool vs alternatives. It doesn't mention alternatives, exclusions, or sequencing (e.g., should validate_abac_changes be called after this, or instead of it). The context for use is only implied through the purpose statement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get current uncommitted changes from the M365ABAC repository using git diff. Useful for validating work-in-progress code.
| Name | Required | Description | Default |
|---|---|---|---|
| repository | No | Which repository to get changes from (default: M365ABAC) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses the read-only nature via 'git diff' (uncommitted changes), implying a non-destructive operation. However, it doesn't describe output format, which are safe assumptions for a read-only diff operation. Adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences, front-loaded with the core action then a use-case clause. Efficient and no wasted words, though it doesn't enumerate any nuances like git diff specifics.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (1 param, enums, no output schema), and the description covers the purpose and read-only nature. Sibling tools handle validation/analysis distinctly. Adequate and complete enough for this low-complexity tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with the repository parameter documented with enum values (M365ABAC, RMS, MIP) and default. The description reinforces that M365ABAC is the default repo. With full coverage and one simple enum param, the parameter is well-specified; description adds marginal but useful context about the default.
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?
Clear description: 'Get current uncommitted changes from the M365ABAC repository using git diff. Useful for validating work-in-progress code.' Specific verb (get), resource (uncommitted changes from repo), and method (git diff). It distinguishes somewhat from siblings which are about validation/analysis/generation, though it doesn't explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context ('validating work-in-progress code') and the repository param is documented with enum values in the schema. It implies the use case (WIP code validation) but doesn't explicitly contrast with validate_abac_changes or analyze_cross_component_impact, though the verbs naturally differ.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Validate ABAC code changes using Q&A analysis. Accepts Q&A format where Copilot has already analyzed git diff and answered 8 predefined ABAC questions. Validates the answers against M365ABAC requirements and performs cross-component impact analysis.
| Name | Required | Description | Default |
|---|---|---|---|
| filePath | Yes | Path of the main file being changed (relative to repo root) | |
| fileType | No | Type of file (e.g., 'typescript', 'csharp', 'python') | |
| codeChanges | Yes | Q&A analysis text from Copilot. Format: 'Q&A Analysis:\n1. [Category] Question?\nA: Answer...\n2. [Category] Question?\nA: Answer...' Contains Copilot's analysis of git diff against 8 ABAC questions. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full transparency burden. It conveys that the tool reads Q&A text and validates against M365ABAC requirements plus cross-component impact, but does not disclose side effects, whether it modifies state, what happens on validation failure, or how the output is structured. The dual-duty mention of cross-component impact analysis is vague about behavioral outcomes.
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 two sentences and reasonably efficient. However, the second sentence partially repeats the first ('validates the answers' restates 'Validate ABAC code changes'), and the cross-component impact sentence adds info already implied by a sibling tool name. It's adequate but slightly redundant.
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 validation tool with 100% schema coverage and no output schema, the description covers the input format (Q&A with 8 questions) and validation scope (M365ABAC requirements, cross-component impact). However, given three related sibling tools, the description should clarify how this validation tool fits into the overall workflow — it doesn't explain return value/outcome shape, which matters for a tool whose output likely guides next steps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the structured fields already document all three parameters. The description adds context about the codeChanges format ('Q&A Analysis:...' with category brackets) which complements the schema. However, it does not explain fileType's role in validation or how it maps to the filePath parameter beyond what the schema states.
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+resource ('Validate ABAC code changes') and explains the mechanism (Q&A analysis with 8 predefined questions), distinguishing it from siblings like generate_validation_questions and analyze_cross_component_impact. It's clear what the tool does, though 'performs cross-component impact analysis' slightly overlaps with the sibling analyze_cross_component_impact, creating mild ambiguity about boundaries.
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 the input mechanism (Q&A format where Copilot has already analyzed the git diff against 8 ABAC questions) which implies a prerequisite state, but does not explicitly state when not to use this tool or name alternatives. Sibling tools like analyze_cross_component_impact and generate_validation_questions suggest related workflows, but no explicit guidance on choosing between them is given.
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.
v1.0.0analyze_cross_component_impactgenerate_validation_questionsget_current_changesvalidate_abac_changesScored across 4 tools
The tools are mostly distinct: validate_abac_changes covers full validation, analyze_cross_component_impact focuses specifically on cross-component work, get_current_changes retrieves diffs, and generate_validation_questions produces questions. The main overlap is between validate_abac_changes and analyze_cross_component_impact, since validation already includes cross-component impact analysis, which could cause an agent to pick the wrong one.
Tools generally follow verb_object pattern: validate_abac_changes, analyze_cross_component_impact, get_current_changes, generate_validation_questions. All use snake_case with consistent verb-first naming, though the objects vary in form (abac_changes vs cross_component_impact vs current_changes vs validation_questions) making them slightly less uniform.
Four tools is a well-scoped set for a validation workflow. Each tool represents a distinct stage: get diffs, generate questions, analyze cross-component impact, and validate. This is an appropriate size that feels lean but sufficient for the stated purpose of ABAC change validation.
The workflow covers retrieving changes, generating questions, analyzing cross-component impact, and validating against requirements. However, there's no dedicated tool for applying validation recommendations or generating a final report/summary, and the coupling between generate_validation_questions and validate_abac_changes (which accepts answers to 8 predefined questions) may leave a gap if questions change. The core loop is covered but reporting and remediation are absent.
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