このプロジェクトは、Semgrepツール、The Replit TeamとそのAgent V2、およびstefanskiasan/semgrep-mcp-serverによる実装の堅牢性に触発されましたが、インストールとメンテナンスをより簡単かつ強化するために大幅なアーキテクチャの変更を加えて進化させたものです。
MCP Server Semgrepは、強力なSemgrep静的解析ツールをAnthropic ClaudeのようなAIアシスタントと統合する、Model Context Protocol準拠のサーバーです。これにより、会話型インターフェースを通じて直接、高度なコード解析、セキュリティ脆弱性の検出、コード品質の向上を実現します。
Related MCP server: AWS Security MCP
包括的なソースコード解析 - 個別のファイルだけでなく、プロジェクト全体の問題を検出
プロアクティブなエラー検出 - 致命的なバグになる前に潜在的な問題を特定
継続的なコード品質向上 - 定期的なスキャンとリファクタリングにより、コードベースを段階的に改善
スタイルの整合性 - 以下のようなコード内の不整合を特定・修正:
CSSにおける任意のz-indexレイヤー
一貫性のない命名規則
コードの重複
名前付き定数ではなく「マジックナンバー」の使用
既知の脆弱性に対する自動コード検証 - 既知のセキュリティ問題パターンをスキャン
カスタマイズされたセキュリティルール - プロジェクト固有のルールを作成
チームの教育 - 潜在的な問題を検出することで、安全なプログラミング手法を学習
「ライブ」ドキュメント - AIがコードの断片がなぜ問題なのか、どう修正すべきかを説明
技術的負債の削減 - 問題のある領域を体系的に検出して修正
コードレビューの改善 - 一般的な問題を自動検出することで、より複雑な問題に集中可能
公式MCP SDKとの直接統合
ハンドラーを統合した簡素化されたアーキテクチャ
クリーンなES Modules実装
セキュリティのための効率的なエラーハンドリングとパス検証
英語とポーランド語の両方に対応したインターフェースとドキュメント
包括的なユニットテスト
充実したドキュメント
クロスプラットフォーム対応 (Windows, macOS, Linux)
柔軟なSemgrepインストール検出と管理
Semgrep MCP Serverは以下のツールを提供します:
scan_directory: ソースコードをスキャンして潜在的な問題を検出
list_rules: 利用可能なルールとSemgrepがサポートする言語を表示
analyze_results: スキャン結果の詳細な分析
create_rule: カスタムSemgrepルールの作成
filter_results: さまざまな基準で結果をフィルタリング
export_results: さまざまな形式で結果をエクスポート
compare_results: 2つの結果セットを比較（例: 変更前と変更後）
デプロイ前のコードセキュリティ解析
一般的なプログラミングエラーの検出
チーム内でのコーディング標準の強制
既存コードのリファクタリングと品質向上
スタイルやコード構造の不整合の特定（例: CSS、コンポーネント構成）
ベストプラクティスに関する開発者教育
修正の正確性の検証（スキャン結果の比較）
Node.js v18+
TypeScript (開発用)
MCP Server Semgrepをインストールして使用する最も簡単な方法は、Smithery.ai経由です:
インストール手順に従って、MCP互換クライアントに追加
Semgrep APIトークンや許可されたワークスペースルートなどのオプション設定を構成
これは、すべての依存関係と構成を自動的に処理するため、Claude Desktopおよびその他のMCPクライアントに推奨される方法です。
# Using npm
npm install -g mcp-server-semgrep
# Using pnpm
pnpm add -g mcp-server-semgrep
# Using yarn
yarn global add mcp-server-semgrepこのパッケージは他のレジストリでも利用可能です:
# Using npm
npm install -g git+https://github.com/VetCoders/mcp-server-semgrep.git
# Using pnpm
pnpm add -g git+https://github.com/VetCoders/mcp-server-semgrep.git
# Using yarn
yarn global add git+https://github.com/VetCoders/mcp-server-semgrep.gitリポジトリをクローン:
git clone https://github.com/VetCoders/mcp-server-semgrep.git
cd mcp-server-semgrep依存関係をインストール (すべての主要なパッケージマネージャーをサポート):
# Using pnpm (recommended)
pnpm install
# Using npm
npm install
# Using yarn
yarn installプロジェクトをビルド:
# Using pnpm
pnpm run build
# Using npm
npm run build
# Using yarn
yarn build注: インストールプロセス中にSemgrepが利用可能か自動的にチェックされます。Semgrepが見つからない場合は、インストール方法の指示が表示されます。
このサーバーは、明示的に許可されたワークスペースルート内のファイルのみを読み書きします。
デフォルトでは、許可されるルートはプロセス作業ディレクトリ (process.cwd()) です。
Claude Desktop、Smithery、またはプロジェクトルート以外でサーバーを起動するランチャーの場合、MCP_SERVER_SEMGREP_ALLOWED_ROOTSに1つ以上の絶対パスを設定してください。
複数のルートを指定する場合は、プラットフォームのパス区切り文字を使用してください（macOS/Linuxは :、Windowsは ;）。
このサーバーは独自のSemgrepアカウント管理を実装していません。インストール済みのsemgrep CLIを呼び出し、Semgrepの通常の認証動作に依存します。
ローカルターミナルやローカル開発環境では、現在のOSアカウントの既存のsemgrep loginセッションを使用できる場合があります。
Claude Desktop、Smithery、コンテナ、CIなどの管理された起動環境では、決定論的な動作のために明示的なSEMGREP_APP_TOKENの使用を推奨します。
マシンやランナー間でポータブルな構成が必要な場合、SEMGREP_APP_TOKENが最も安全なオプションです。
Semgrepはいくつかの方法でインストールできます:
パッケージマネージャー経由:
# Using pnpm
pnpm add -g semgrep
# Using npm
npm install -g semgrep
# Using yarn
yarn global add semgrepPython pip:
pip install semgrepHomebrew (macOS):
brew install semgrepLinux:
sudo apt-get install semgrep
# or
curl -sSL https://install.semgrep.dev | shWindows:
pip install semgrepMCP Server SemgrepをClaude Desktopと統合するには2つの方法があります:
「Install in Claude Desktop」をクリック
画面の指示に従う
Claude Desktopをインストール
Claude Desktop構成ファイル (claude_desktop_config.json) を更新し、serversセクションに追加します。
semgrep loginで既に認証されているユーザーアカウントで起動するローカル環境では、Semgrep CLIがそのログイン情報を再利用できる場合があります。デスクトップ管理環境や共有環境では、引き続きSEMGREP_APP_TOKENを明示的に設定することを推奨します:
{
"mcpServers": {
"semgrep": {
"command": "node",
"args": [
"/your_path/mcp-server-semgrep/build/index.js"
],
"env": {
"SEMGREP_APP_TOKEN": "your_semgrep_app_token",
"MCP_SERVER_SEMGREP_ALLOWED_ROOTS": "/Users/you/projects"
}
}
}
}Claude Desktopを起動し、コード解析に関する質問を開始します。
複数のワークスペースをスキャンしたい場合は、MCP_SERVER_SEMGREP_ALLOWED_ROOTSに絶対パスをプラットフォーム区切り文字でリストして設定してください。
Could you scan my source code in the /projects/my-application directory for potential security issues? That directory is already included in MCP_SERVER_SEMGREP_ALLOWED_ROOTS.Analyze the z-index values in the project's CSS files and identify inconsistencies and potential layer conflicts.Create a Semgrep rule that detects improper use of input sanitization functions.Show me only scan results related to SQL injection vulnerabilities.Find all "magic numbers" in the code and suggest replacing them with named constants.プロジェクトの特定のニーズに合わせてカスタムルールを作成できます。作成可能なルールの例を以下に示します:
rules:
- id: inconsistent-z-index
pattern: z-index: $Z
message: "Z-index $Z may not comply with the project's layering system"
languages: [css, scss]
severity: WARNINGrules:
- id: deprecated-import
pattern: import $X from 'old-library'
message: "You're using a deprecated library. Consider using 'new-library'"
languages: [javascript, typescript]
severity: WARNINGpnpm test├── src/
│ └── index.ts # Main entry point and all handler implementations
├── scripts/
│ └── check-semgrep.js # Semgrep detection and installation helper
├── build/ # Compiled JavaScript (after build)
└── tests/ # Unit testsツールの使用に関する詳細情報は以下を参照してください:
このプロジェクトはMITライセンスの下でライセンスされています。詳細はLICENSEファイルを参照してください。
Maciej Gad - 半年前までbashを見つけられなかった獣医
Klaudiusz - 個別のエーテル的存在であり、米国カリフォルニア州のGPUループのどこかに住むAnthropicのClaude Sonnet 3.5-3.7の別インスタンス
CLI初心者からMCPツール開発者への旅
🤖 Claude CodeとMCP Toolsの究極の助けを借りて開発されました
元のインスピレーションを与えてくれたstefanskiasan
ClaudeとMCPプロトコルを提供してくれたAnthropic
優れた静的解析ツールを提供してくれたSemgrep
Analyzes scan results
| Name | Required | Description | Default |
|---|---|---|---|
| results_file | Yes | Absolute path to JSON results file (must be within an allowed workspace root) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility for behavioral disclosure. It only says 'Analyzes', implying a read operation, but does not state if results are modified, returned, or stored. No information about side effects, authorization needs, or output format is given.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise but lacks structuring. It does not provide additional sections or details to aid understanding. The brevity is acceptable but not optimally informative.
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 absence of an output schema and the presence of sibling tools, the description is incomplete. It does not explain what the analysis returns or how it differs from compare_results or filter_results. The tool's functionality remains unclear.
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 schema provides a complete description for the single parameter (results_file) with context about allowed paths. Since schema coverage is 100%, the description's lack of parameter information is acceptable per guidelines. However, it adds no extra meaning 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 states 'Analyzes scan results', which is a verb+resource, but it is vague. It does not specify what kind of analysis is performed (e.g., statistical, pattern detection, summary) and fails to distinguish from sibling tools like compare_results, filter_results, and export_results.
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?
There is no guidance on when to use this tool versus alternatives. No context, prerequisites, or exclusions are provided, leaving the agent without criteria for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compares two scan results
| Name | Required | Description | Default |
|---|---|---|---|
| old_results | Yes | Absolute path to older JSON results file | |
| new_results | Yes | Absolute path to newer JSON results file |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description is minimal ('Compares two scan results') and provides no behavioral details beyond the name. With no annotations, it fails to disclose whether the tool is read-only, its side effects, return behavior, or required permissions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no extra words, making it concise. However, it could be restructured to front-load more critical information without increasing length significantly.
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 tool with no output schema and only two string parameters, the description does not explain what the comparison produces (e.g., diff output, boolean, list of changes). This leaves the agent unsure of the return value and behavior, making it incomplete.
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?
Both parameters are described in the input schema ('Absolute path to older JSON results file' and 'Absolute path to newer JSON results file'), achieving 100% schema coverage. The description adds no additional meaning beyond the schema, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Compares two scan results' uses a verb ('compares') and resource ('scan results'), clearly indicating the tool's function. It is distinct from siblings like 'analyze_results' and 'filter_results', but lacks specificity on what the comparison entails (e.g., differences, similarities).
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 such as 'analyze_results' or 'filter_results'. There is no mention of prerequisites, when-not-to-use, or explicit context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Creates a new Semgrep rule
| Name | Required | Description | Default |
|---|---|---|---|
| output_path | Yes | Absolute path for output rule file | |
| pattern | Yes | Search pattern for the rule | |
| language | Yes | Target language for the rule | |
| message | Yes | Message to display when rule matches | |
| severity | No | Rule severity (ERROR, WARNING, INFO) | WARNING |
| id | No | Rule identifier | custom_rule |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It only states 'Creates a new Semgrep rule' with no information about side effects (e.g., overwriting existing files), permissions, or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise but lacks structure. It front-loads the action but provides no additional detail, making it barely adequate.
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 creates a file (output_path required) and has no output schema, the description should explain return behavior (e.g., success indication) or file naming. It does not, leaving significant gaps for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 100% coverage with clear parameter descriptions. The tool description adds no additional meaning beyond the schema, meeting the baseline for high 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 uses a specific verb 'Creates' and resource 'a new Semgrep rule', making the core action clear. It naturally distinguishes from siblings which focus on analysis, comparison, and listing, not creation.
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 on when to use this tool versus alternatives. The description does not indicate prerequisites (e.g., rule syntax knowledge) or situations where other tools might be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Exports scan results in various formats
| Name | Required | Description | Default |
|---|---|---|---|
| results_file | Yes | Absolute path to JSON results file | |
| output_file | Yes | Absolute path to output file | |
| format | No | Output format (json, sarif, text) | text |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It fails to mention whether the tool overwrites existing files, requires network access, or produces any side effects. The agent cannot infer safety or error conditions from the description alone.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise but lacks structure. It does not front-load critical information like required parameters or output behavior. The brevity is acceptable but not optimal for usability.
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 should indicate what the tool returns (e.g., success message, file path). It also does not mention error handling or performance implications. The tool is simple, but the description remains incomplete for fully autonomous invocation.
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?
All 3 parameters are described in the schema with high coverage (100%). The description adds no extra context beyond 'exports scan results in various formats'—it does not elaborate on parameter constraints like valid file paths or format specifics. Baseline 3 is appropriate since schema does the work.
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 'Exports scan results in various formats' clearly indicates the action (export) and resource (scan results) and mentions format variability. However, it does not differentiate from sibling tools like analyze_results or compare_results, which might also output results. The description could be more specific about the exact nature of the export.
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, such as analyze_results or filter_results. There are no mentions of prerequisites or context in which export is appropriate. The agent is left without decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Filters scan results by various criteria
| Name | Required | Description | Default |
|---|---|---|---|
| results_file | Yes | Absolute path to JSON results file | |
| severity | No | Filter by severity (ERROR, WARNING, INFO) | |
| rule_id | No | Filter by rule ID | |
| path_pattern | No | Filter by file path pattern (regex) | |
| language | No | Filter by programming language | |
| message_pattern | No | Filter by message content (regex) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries the full burden. It does not disclose whether the tool modifies the original file, requires authentication, or has side effects. The filtering behavior (e.g., AND vs OR logic) is not explained.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Very short single sentence, efficient but lacking critical details. It is concise but not optimally informative for a 6-parameter tool.
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 6 parameters, no output schema, and no annotations, the description is incomplete. It does not explain return format, behavior when no matches, or how it differs from sibling tools like export_results.
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 parameter descriptions, so the description adds minimal value beyond the schema. It does not clarify how multiple filters interact, which leaves ambiguity for the agent.
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 it filters scan results, which is clear but lacks specificity about the resource (e.g., scan results file) and does not differentiate from sibling tools like analyze_results or compare_results.
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 on when to use this tool versus alternatives (e.g., analyze_results for aggregation, compare_results for comparison). No when-not-to-use or prerequisites mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Lists available Semgrep rules
| Name | Required | Description | Default |
|---|---|---|---|
| language | No | Programming language for rules (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. Description lacks any behavioral details such as authentication needs, rate limits, or whether it returns full rule details or just names.
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?
Single sentence, concise and front-loaded with essential information. 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 one optional parameter, the description provides minimal context. It doesn't clarify what information is returned (e.g., rule names only or full definitions).
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 no extra meaning beyond the schema's parameter description. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'lists' and resource 'Semgrep rules', distinguishing it from siblings like 'create_rule' and 'scan_directory'.
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 on when to use this tool versus alternatives like 'filter_results' or 'analyze_results'. Does not specify when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Performs a Semgrep scan on a directory
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Absolute path to the directory to scan (must be within an allowed workspace root) | |
| config | No | Semgrep configuration (e.g. "auto" or absolute path to rule file) | auto |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description only states the action without disclosing side effects, permissions, or output behavior.
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?
Single sentence is concise but lacks structure or front-loading of key details. Could be expanded to include usage context.
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?
No output schema and no annotations; description does not explain return values, side effects, or prerequisites, making it incomplete for a scan tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%; both 'path' and 'config' are described in the schema. Description adds no extra meaning 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?
Clear verb+resource: 'Performs a Semgrep scan on a directory' distinguishes from siblings like analyze_results or list_rules.
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 on when to use this tool vs alternatives (e.g., analyze_results) or any exclusions.
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.
analyze_resultscompare_resultscreate_ruleexport_resultsfilter_resultslist_rulesscan_directoryScored across 7 tools
Each tool targets a distinct aspect of Semgrep workflow: scanning, rule management, result analysis, filtering, export, and comparison. No overlapping purposes that would confuse an agent.
All tools follow the consistent verb_noun pattern (scan_directory, list_rules, create_rule, etc.), making the API predictable and easy to navigate.
Seven tools is a well-scoped set for a Semgrep server, covering core operations without bloat or excessive granularity.
The surface covers scanning, rule listing/creation, and result handling (analyze, filter, export, compare). Missing update/delete for rules and detailed rule inspection, but core workflows are complete.
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Enable secure connectivity between Sentry issues and debugging data, and LLM clients, using a Model Context Protocol (MCP) server.
A Model Context Protocol server for Wix AI tools
The OpenZeppelin Solidity Contracts MCP server integrates OpenZeppelin's security and style rules into AI-driven development workflows, enabling AI assistants to generate safe, correct, and production-ready smart contracts. It automatically validates generated code against OpenZeppelin standards (including imports, modifiers, naming conventions, and security checks) and supports various contract types including ERC-20, ERC-721, ERC-1155, Stablecoins, RWA, Governor, and Account contracts through prompt-driven workflows.
