Splunk Enterprise/Cloud を自然言語で操作するための FastMCP ベースのツールです。このツールは、直感的なインターフェースを通じて Splunk データの検索、KV ストアの管理、Splunk リソースへのアクセスを行うための一連の機能を提供します。
このツールは次の 3 つのモードで動作します。
SSEモード（デフォルト）
サーバー送信イベントベースの通信
リアルタイムの双方向インタラクション
ウェブベースのMCPクライアントに適しています
引数が指定されていない場合のデフォルトモード
/sseエンドポイント経由のアクセス
APIモード
RESTful APIエンドポイント
/api/v1エンドポイントプレフィックス経由でアクセス
python splunk_mcp.py apiから始める
STDIOモード
標準入出力ベースの通信
Claude Desktopおよびその他のMCPクライアントと互換性があります
AIアシスタントとの直接統合に最適
python splunk_mcp.py stdioから始める
Related MCP server: MCP Server for Splunk
Splunk Search : 自然言語クエリでSplunk検索を実行する
インデックス管理: Splunk インデックスの一覧表示と検査
ユーザー管理: Splunk ユーザーの表示と管理
KVストア操作: KVストアコレクションの作成、一覧表示、管理
非同期サポート: パフォーマンス向上のため、async/await パターンで構築されています
詳細なログ記録: 絵文字インジケーターによる包括的なログ記録で視認性が向上します
SSL 構成: さまざまなセキュリティ要件に対応する柔軟な SSL 検証オプション
強化されたデバッグ: トラブルシューティングのための詳細な接続とエラーのログ記録
包括的なテスト: すべての主要な機能をカバーするユニットテスト
エラー処理: 適切なステータスコードによる堅牢なエラー処理
SSE準拠:MCP SSE仕様に完全準拠
MCP インターフェース経由では次のツールが利用できます。
リストツール
利用可能なすべてのMCPツールとその説明およびパラメータを一覧表示します。
ヘルスチェック
接続を確認するために利用可能なSplunkアプリのリストを返します
ピン
MCP サーバーが稼働していることを確認するためのシンプルな ping エンドポイント
現在のユーザー
現在認証されているユーザーに関する情報を返します
リストユーザー
すべてのユーザーとその役割のリストを返します
リストインデックス
アクセス可能なすべてのSplunkインデックスのリストを返します
インデックス情報を取得する
特定のインデックスに関する詳細情報を返します
パラメータ: index_name (文字列)
インデックスとソースタイプ
インデックスとそのソースタイプの包括的なリストを返します
検索_splunk
Splunk検索クエリを実行する
パラメータ:
search_query (文字列): Splunk検索文字列
earliest_time（文字列、オプション）: 検索ウィンドウの開始時刻
Latest_time（文字列、オプション）: 検索ウィンドウの終了時刻
max_results (整数、オプション): 返される結果の最大数
保存した検索リスト
Splunkインスタンスに保存された検索のリストを返します
list_kvstore_collections
すべてのKVストアコレクションを一覧表示します
kvstoreコレクションを作成する
新しいKVストアコレクションを作成します
パラメータ: collection_name (文字列)
削除_kvstore_コレクション
既存のKVストアコレクションを削除します
パラメータ: collection_name (文字列)
SSE モードで実行する場合、次のエンドポイントが利用できます。
/sse : SSE接続情報をテキスト/イベントストリーム形式で返します
SSE接続に関するメタデータを提供します
メッセージエンドポイントのURLが含まれています
プロトコルと機能の情報を提供します
/sse/messages : メインのSSEストリームエンドポイント
ハートビートなどのシステムイベントをストリーミングする
永続的な接続を維持
適切にフォーマットされたSSEイベントを送信します
/sse/health : SSEモードのヘルスチェックエンドポイント
SSE形式でステータスとバージョン情報を返します
MCP 実装には一貫したエラー処理が含まれます。
無効な検索コマンドまたは不正なリクエスト
権限が不十分です
リソースが見つかりません
無効な入力検証
予期しないサーバーエラー
Splunk サーバーとの接続の問題
すべてのエラー応答には、エラーを説明する詳細なメッセージが含まれます。
Python 3.10以上
依存管理のための詩
Splunk Enterprise/Cloudインスタンス
必要な権限を持つ適切なSplunk認証情報
リポジトリをクローンします。
git clone <repository-url>
cd splunk-mcpPoetry を使用して依存関係をインストールします。
poetry installサンプル環境ファイルをコピーして設定を構成します。
cp .env.example .envSplunk の資格情報を使用して.envファイルを更新します。
SPLUNK_HOST=your_splunk_host
SPLUNK_PORT=8089
SPLUNK_USERNAME=your_username
SPLUNK_PASSWORD=your_password
SPLUNK_SCHEME=https
VERIFY_SSL=true
FASTMCP_LOG_LEVEL=INFO最新のイメージをプルします:
docker pull livehybrid/splunk-mcp:latest上記のように.envファイルを作成するか、環境変数を直接使用します。
Docker Compose を使用して実行します。
docker-compose up -dまたは Docker を直接使用します:
docker run -i \
--env-file .env \
livehybrid/splunk-mcpこのツールは 3 つのモードで実行できます。
SSE モード (MCP クライアントのデフォルト):
# Start in SSE mode (default)
poetry run python splunk_mcp.py
# or explicitly:
poetry run python splunk_mcp.py sse
# Use uvicorn directly:
SERVER_MODE=api poetry run uvicorn splunk_mcp:app --host 0.0.0.0 --port 8000 --reloadSTDIO モード:
poetry run python splunk_mcp.py stdioこのプロジェクトは、新しいdocker compose (V2) コマンドと従来のdocker-compose (V1) コマンドの両方をサポートしています。以下の例ではV2構文を使用していますが、どちらもサポートされています。
SSE モード (デフォルト):
docker compose up -d mcpAPI モード:
docker compose run --rm mcp python splunk_mcp.py apiSTDIO モード:
docker compose run -i --rm mcp python splunk_mcp.py stdioこのプロジェクトには、Docker の専用テスト環境が含まれています。
すべてのテストを実行します。
./run_tests.sh --docker特定のテスト コンポーネントを実行します。
# Run only the MCP server
docker compose up -d mcp
# Run only the test container
docker compose up test
# Run both with test results
docker compose up --abort-on-container-exitテスト結果は./test-resultsディレクトリに保存されます。
建物画像:
# Build both images
docker compose build
# Build specific service
docker compose build mcp
docker compose build testログの表示:
# View all logs
docker compose logs
# Follow specific service logs
docker compose logs -f mcpデバッグ:
# Run with debug mode
DEBUG=true docker compose up mcp
# Access container shell
docker compose exec mcp /bin/bash注意: Docker Compose V1 を使用している場合は、上記のコマンドでdocker compose docker-composeに置き換えてください。
環境変数:
.envファイルをコミットしない
.env.exampleテンプレートとして使用する
本番環境ではDocker secretsの使用を検討する
SSL検証:
本番環境ではVERIFY_SSL=trueが推奨されます
開発/テスト用に無効にすることができます
環境変数で設定する
ポート露出：
必要なポートのみを公開する
可能な場合は内部 Docker ネットワークを使用する
生産現場でのネットワークセキュリティを考慮する
次の環境変数を設定します。
SPLUNK_HOST : Splunkホストアドレス
SPLUNK_PORT : Splunk管理ポート（デフォルト: 8089）
SPLUNK_USERNAME : Splunkユーザー名
SPLUNK_PASSWORD : Splunkのパスワード
SPLUNK_SCHEME : 接続スキーム（デフォルト: https）
VERIFY_SSL : SSL検証を有効/無効にする（デフォルト: true）
FASTMCP_LOG_LEVEL : ログレベル（デフォルト: INFO）
SERVER_MODE : uvicorn 使用時のサーバーモード (sse、api、stdio)
このツールは柔軟な SSL 検証オプションを提供します。
デフォルト（セキュア）モード：
VERIFY_SSL=true完全なSSL証明書検証
ホスト名検証が有効
実稼働環境に推奨
リラックスモード：
VERIFY_SSL=falseSSL証明書の検証が無効になっています
ホスト名検証が無効
テストや自己署名証明書に便利
このプロジェクトには、pytest を使用した包括的なテスト カバレッジと、カスタム MCP クライアントを使用したエンドツーエンドのテストが含まれています。
基本的なテスト実行:
poetry run pytestカバレッジレポートの場合:
poetry run pytest --cov=splunk_mcp詳細出力の場合:
poetry run pytest -vこのプロジェクトには、ライブ SSE エンドポイントに接続してすべてのツールをテストするカスタム MCP クライアント テスト スクリプトが含まれています。
# Test all tools
python test_endpoints.py
# Test specific tools
python test_endpoints.py health_check list_indexes
# List all available tools
python test_endpoints.py --listこのスクリプトは、次の機能により MCP クライアントとして機能します。
/sseエンドポイントに接続してメッセージURLを取得する
ツール呼び出しをメッセージエンドポイントに送信する
SSEイベントを処理してツールの結果を抽出する
期待される形式に対する結果の検証
これにより、実際の MCP クライアントで使用される SSE インターフェイスの現実的なテストが提供されます。
このプロジェクトでは、3 つの補完的なテスト手法を使用します。
MCP 統合テスト( tests/test_api.py ) :
mcp.call_tool()を通じて MCP ツール インターフェースをテストします。
FastMCPで適切なツール登録を検証
正しい応答形式とデータ構造を保証する
MCPインターフェースレベルでのエラー処理を検証します
**注:**このファイルは、目的をよりよく反映するために、理想的にはtest_mcp.pyに名前を変更する必要があります。
直接関数テスト( tests/test_endpoints_pytest.py ) :
Splunk 機能を直接テストします (MCP レイヤーをバイパス)
関数の実装の詳細をより包括的にカバーします
エッジケース、パラメータのバリエーション、エラー処理をテストします
SSL構成、接続パラメータ、タイムアウトのテストが含まれています
効率的なテストカバレッジのためにパラメータ化されたテストを使用する
エンドツーエンドの MCP クライアント テスト( test_endpoints.py ) :
SSEエンドポイントに接続する実際のMCPクライアントのように動作します
接続からツールの呼び出し、応答の解析までの完全なフローをテストします
実際のSSEプロトコル実装を検証する
実際のパラメータを使用してライブサーバーでツールをテストします
構成テスト( tests/test_config.py ) :
環境変数の解析のテスト
SSL検証設定
接続パラメータの検証
テストは以下をサポートします:
pytest-asyncio による非同期テスト
pytest-cov によるカバレッジレポート
pytest-mock によるモック
パラメータ化テスト
接続タイムアウトテスト
基本的な接続:
このツールは基本的なTCP接続テストを実行します
ポート8089にアクセスできるかどうかを確認する
ネットワークルーティングとファイアウォールを確認する
SSLの問題:
SSLエラーが表示される場合は、 VERIFY_SSL=falseを設定してみてください。
証明書の有効性と信頼チェーンを確認する
ホスト名が証明書と一致することを確認する
認証の問題:
Splunkの資格情報を確認する
ユーザー権限を確認する
アカウントがロックされていないことを確認する
デバッグ:
詳細なログを取得するには、 FASTMCP_LOG_LEVEL=DEBUGを設定します。
特定のエラーメッセージについては接続ログを確認してください
SSL構成メッセージを確認する
SSE 接続の問題:
SSEエンドポイントが/sse経由でアクセス可能であることを確認する
適切なコンテンツタイプヘッダーを確認する
ブラウザ開発者ツールを使用してSSE接続を検査する
Splunk MCPをClaude Desktopと統合するには、SSEモードまたはSTDIOモードのいずれかを使用するように設定する必要があります。claude_desktop_config.jsonに以下の設定claude_desktop_config.json追加してください。
{
"mcpServers": {
"splunk": {
"command": "poetry",
"env": {
"SPLUNK_HOST": "your_splunk_host",
"SPLUNK_PORT": "8089",
"SPLUNK_USERNAME": "your_username",
"SPLUNK_PASSWORD": "your_password",
"SPLUNK_SCHEME": "https",
"VERIFY_SSL": "false"
},
"args": [
"--directory",
"/path/to/splunk-mcp",
"run",
"python",
"splunk_mcp.py",
"stdio"
]
}
}
}{
"mcpServers": {
"splunk": {
"command": "poetry",
"env": {
"SPLUNK_HOST": "your_splunk_host",
"SPLUNK_PORT": "8089",
"SPLUNK_USERNAME": "your_username",
"SPLUNK_PASSWORD": "your_password",
"SPLUNK_SCHEME": "https",
"VERIFY_SSL": "false",
"FASTMCP_PORT": "8001",
"DEBUG": "true"
},
"args": [
"--directory",
"/path/to/splunk-mcp",
"run",
"python",
"splunk_mcp.py",
"sse"
]
}
}
}設定が完了すると、Claude を介して自然言語で Splunk と対話できるようになります。例:
利用可能なインデックスを一覧表示します。
What Splunk indexes are available?Splunk データを検索:
Search Splunk for failed login attempts in the last 24 hoursシステムの健全性を取得します。
Check the health of the Splunk systemKV ストアを管理します。
List all KV store collectionsMCP ツールは自動的に Claude で使用可能になり、自然言語コマンドを通じてこれらの操作を実行できるようになります。
[ここにライセンスを入力してください]
FastMCPフレームワーク
Python 用 Splunk SDK
構成管理のためのPython分離
SSE実装のためのSSE Starlette
Get information about the currently authenticated user.
This endpoint retrieves:
- Basic user information (username, real name, email)
- Assigned roles
- Default app settings
- User type
Returns:
Dict[str, Any]: Dictionary containing user information
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly describes what information is retrieved (e.g., basic info, roles, settings) and the return type, but lacks details on authentication requirements, error handling, or rate limits. This is adequate but has gaps for a tool with no annotations.
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 and front-loaded, starting with the core purpose followed by bullet points for retrieved information and return details. Every sentence adds value without redundancy, making it efficient and easy to parse.
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 low complexity (0 parameters, no output schema, no annotations), the description is largely complete, covering purpose, retrieved data, and return type. However, it could benefit from more behavioral context, such as authentication notes, to fully compensate for the lack of annotations.
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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, focusing instead on the tool's function and output, which aligns with the schema's simplicity.
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 explicitly states the verb 'Get' and resource 'information about the currently authenticated user,' making the purpose specific and clear. It distinguishes this tool from siblings like 'list_users' by focusing on the current authenticated user rather than listing all users.
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 usage context by specifying 'currently authenticated user,' suggesting it should be used when user-specific data is needed. However, it does not explicitly state when to use alternatives like 'list_users' or provide exclusions, leaving some ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get a list of all indexes and their sourcetypes.
This endpoint performs a search to gather:
- All available indexes
- All sourcetypes within each index
- Event counts for each sourcetype
- Time range information
Returns:
Dict[str, Any]: Dictionary containing:
- indexes: List of all accessible indexes
- sourcetypes: Dictionary mapping indexes to their sourcetypes
- metadata: Additional information about the search
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes what the tool does (performs a search to gather specific data types) and outlines the return structure in detail. However, it doesn't mention potential limitations like performance impact, authentication requirements, or rate limits, which would be helpful for a search operation.
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 perfectly structured: a clear purpose statement followed by bullet points detailing what the search gathers and a formatted Returns section. Every sentence adds value with zero redundancy, making it easy to scan and understand.
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 zero-parameter tool with no annotations or output schema, the description provides excellent context about the operation and return values. It explains the search behavior and output structure thoroughly. The only minor gap is lack of information about potential side effects or performance characteristics.
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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately focuses on output semantics instead, detailing what data is returned. This exceeds the baseline expectation for a zero-parameter tool.
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 specific action ('Get a list') and resources ('all indexes and their sourcetypes'), distinguishing it from siblings like list_indexes (which likely just lists indexes) and get_index_info (which likely provides detailed info about a specific index). The verb+resource combination is precise and unambiguous.
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 usage for gathering comprehensive index and sourcetype data with event counts and time ranges, but doesn't explicitly state when to use this tool versus alternatives like list_indexes or get_index_info. There's no guidance on prerequisites, exclusions, or specific scenarios where this tool is preferred over siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get metadata for a specific Splunk index.
Args:
index_name: Name of the index to get metadata for
Returns:
Dictionary containing index metadata
| Name | Required | Description | Default |
|---|---|---|---|
| index_name | Yes |
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 states it 'gets metadata' which implies a read-only operation, but doesn't specify permissions required, rate limits, error conditions, or what specific metadata fields are returned. The return format is vaguely described as a 'dictionary', lacking detail on structure or content.
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 efficiently structured with a clear purpose statement followed by Args and Returns sections. Every sentence serves a purpose: the first states the tool's function, the second explains the parameter, and the third describes the return. No redundant or verbose language is present.
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 single-parameter read operation with no annotations and no output schema, the description provides basic purpose and parameter explanation but lacks sufficient behavioral context. It doesn't detail what metadata is included, error handling, or authentication requirements, leaving gaps for an agent to use the tool effectively without additional 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 0%, but the description compensates by explaining the single parameter 'index_name' as 'Name of the index to get metadata for', adding semantic meaning beyond the schema's title 'Index Name'. However, it doesn't provide format examples, constraints, or validation rules for the index name parameter.
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 'Get' and resource 'metadata for a specific Splunk index', making the purpose unambiguous. It distinguishes from siblings like 'list_indexes' (which lists multiple indexes) by focusing on metadata retrieval for a single specified index. However, it doesn't explicitly contrast with 'get_indexes_and_sourcetypes' which might overlap in functionality.
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 usage when you need metadata for a specific index (vs. listing indexes), but doesn't provide explicit guidance on when to choose this tool over alternatives like 'get_indexes_and_sourcetypes' or 'list_indexes'. No prerequisites, exclusions, or comparative context are mentioned, leaving usage decisions to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get basic Splunk connection information and list available apps (same as health_check but for endpoint consistency)
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does (get connection info and list apps) but lacks details on permissions required, rate limits, error conditions, or response format. For a tool with zero annotation coverage, this leaves significant behavioral gaps.
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 perfectly concise and front-loaded: the first clause states the core purpose, and the second provides important sibling context. Every sentence earns its place with zero wasted words or 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 the tool's simplicity (0 parameters, no annotations, no output schema), the description is reasonably complete for a basic health/status tool. However, without annotations or output schema, it should ideally mention what specific information is returned or any authentication requirements. The sibling comparison adds useful 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?
The tool has 0 parameters with 100% schema description coverage, so the schema already fully documents the parameter situation. The description appropriately doesn't discuss parameters since none exist, which is correct. Baseline for 0 parameters 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 the tool's purpose: 'Get basic Splunk connection information and list available apps'. It specifies both the verb ('Get') and the resources ('connection information', 'available apps'), making the function unambiguous. However, it doesn't explicitly differentiate from its sibling 'health_check' beyond noting they're functionally equivalent for endpoint consistency.
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 provides clear context by stating this tool is 'same as health_check but for endpoint consistency', which implicitly guides usage toward this tool when endpoint consistency is needed. It doesn't explicitly state when NOT to use it or name alternatives beyond the mentioned sibling, but the comparison offers practical guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get basic Splunk connection information and list available apps
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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. While it states what the tool does, it doesn't describe important behavioral aspects like authentication requirements, rate limits, error conditions, or what specific information constitutes 'basic Splunk connection information.' The description is functional but lacks operational context.
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 sentence that communicates the complete purpose without any wasted words. It's appropriately sized for a zero-parameter diagnostic tool and front-loads the essential information. Every word earns its place in the description.
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 zero parameters and no output schema, the description adequately covers what the tool does but leaves important questions unanswered about return format, error handling, and authentication requirements. For a diagnostic tool that likely returns structured data about system health, more detail about what constitutes 'basic connection information' would be helpful.
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 with 100% schema description coverage, so the schema already fully documents the parameter situation. The description appropriately doesn't discuss parameters since none exist, which is correct for this case. A baseline of 4 is appropriate when no parameters need documentation.
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 with specific verbs ('Get', 'list') and resources ('basic Splunk connection information', 'available apps'), making it immediately understandable. It distinguishes this tool from siblings like 'ping' (simple connectivity) or 'list_users' (specific resource listing) by combining connection verification with app inventory.
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 usage for checking Splunk connectivity and discovering apps, but doesn't explicitly state when to use this versus alternatives like 'ping' (simpler connectivity check) or 'list_saved_searches' (specific resource listing). No guidance is provided about when NOT to use this tool or what prerequisites might be needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get a list of all available Splunk indexes.
Returns:
Dictionary containing list of indexes
| 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 states the tool returns a dictionary with a list of indexes, which is basic behavioral information. However, it lacks details on permissions, rate limits, or whether the list is paginated or filtered, which are important for a read operation in a system like Splunk.
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 with two sentences that directly state the purpose and return value. It's front-loaded with the main action, though it could be slightly more structured by explicitly separating usage notes from return details.
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 simplicity (0 parameters, no output schema, no annotations), the description is adequate but minimal. It covers the basic purpose and return format, but for a tool in a complex system like Splunk, it could benefit from more context on limitations or typical use cases.
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 0 parameters, and schema description coverage is 100%, so no parameter information is needed. The description doesn't add parameter details, but this is acceptable given the lack of parameters, aligning with the baseline for 0 parameters.
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 with 'Get a list of all available Splunk indexes,' which is a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'get_indexes_and_sourcetypes' or 'get_index_info,' which might offer overlapping or related functionality.
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools or contexts where this tool is preferred, such as for a simple list versus more detailed index information available in other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all KV store collections across apps.
Returns:
List of KV store collections with metadata including app, fields, and accelerated fields
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool lists collections and describes the return format, which is helpful. However, it lacks details on potential side effects, authentication needs, rate limits, or error handling, leaving gaps in behavioral context for a tool that might involve data access across apps.
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 with only two sentences, front-loading the purpose and then detailing the return format. Every sentence earns its place by providing essential information without any wasted words or 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 the tool's complexity (listing collections across apps) and the absence of annotations and output schema, the description is minimally adequate. It covers the purpose and return format, but for a tool that might involve cross-app data access, more context on permissions, scope, or limitations would enhance completeness.
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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately focuses on the return values instead, adding value by specifying what metadata is included in the output, which compensates for the lack of an output 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 the action ('List all KV store collections') and the resource ('across apps'), which provides a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'list_indexes' or 'list_saved_searches', which would require mentioning what makes KV store collections distinct from those other resources.
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for usage, or compare it to sibling tools like 'get_indexes_and_sourcetypes' or 'list_indexes', leaving the agent without explicit direction on selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all saved searches in Splunk
Returns:
List of saved searches with their names, descriptions, and search queries
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 states the return format ('List of saved searches with their names, descriptions, and search queries'), which is helpful, but lacks details on permissions, rate limits, pagination, or error handling for a read operation in Splunk.
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 front-loaded with the core purpose in the first sentence and adds return details in a second sentence. It's efficient with zero waste, though slightly brief for a tool with no annotations, which keeps it from a perfect score.
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 0 parameters, 100% schema coverage, and no output schema, the description is minimally adequate. It explains what the tool does and the return format, but for a read operation in Splunk with no annotations, it could benefit from more behavioral context like access requirements or data scope.
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 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description appropriately omits parameter details, earning a high baseline score for not adding unnecessary information.
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 ('List') and resource ('all saved searches in Splunk'), making the purpose unambiguous. However, it doesn't differentiate from sibling tools like 'list_indexes' or 'list_users' beyond specifying the resource type, which prevents a perfect score.
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. The description doesn't mention prerequisites, limitations, or comparisons to sibling tools like 'search_splunk' or 'get_indexes_and_sourcetypes', leaving the agent without context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all available MCP tools.
Returns:
List of all available tools with their name, description, and parameters.
| 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 full burden. It mentions the return format ('List of all available tools with their name, description, and parameters'), which is helpful. However, it doesn't disclose important behavioral traits like whether this is a read-only operation, if it requires authentication, rate limits, or pagination behavior. The description provides some output information but misses key operational details.
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 and well-structured. Two sentences efficiently convey the tool's purpose and return format with zero wasted words. It's front-loaded with the core functionality and follows with return details.
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 simplicity (0 parameters, no output schema, no annotations), the description is adequate but has gaps. It explains what the tool does and what it returns, but doesn't provide context about when to use it or behavioral constraints. For a tool with no structured metadata, the description should do more to compensate, particularly around usage guidelines and operational behavior.
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 0 parameters with 100% schema description coverage. The description doesn't need to explain parameters since none exist. It appropriately focuses on the tool's function and return value rather than parameter details. The baseline for 0 parameters 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 the tool's purpose: 'List all available MCP tools' with a specific verb ('List') and resource ('MCP tools'). It distinguishes from siblings by focusing on tools rather than users, indexes, searches, etc. However, it doesn't explicitly differentiate from similar listing tools like list_indexes or list_users beyond the resource type.
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. The description doesn't mention prerequisites, timing considerations, or compare it to other listing tools. It simply states what it does without context about when it's appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all Splunk users (requires admin privileges)
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the admin privilege requirement, which is useful behavioral context. However, it doesn't mention other traits like pagination, rate limits, or return format, leaving gaps in behavioral understanding.
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 sentence with zero wasted words. It's front-loaded with the core purpose and includes essential context (admin privileges) without unnecessary elaboration.
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 0 parameters and no output schema, the description covers the basic purpose and permission requirement adequately. However, without annotations or output schema, it lacks details on return format (e.g., list structure, fields) and other behavioral aspects, making it minimally 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?
The tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, earning a baseline 4 for this context.
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 ('List') and resource ('all Splunk users'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'current_user' or 'list_indexes', which would require a 5.
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 provides clear context with 'requires admin privileges', indicating when this tool is appropriate. It doesn't explicitly state when not to use it or name alternatives like 'current_user' for non-admin scenarios, preventing a score of 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Simple ping endpoint to check server availability and get basic server information.
This endpoint provides a lightweight way to:
- Verify the server is running and responsive
- Get basic server information including version and server time
- Check connectivity without making complex API calls
Returns:
Dict[str, Any]: Dictionary containing status and basic server information
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does well by disclosing behavioral traits: it's a read-only operation that returns server information, doesn't modify anything, and provides version and server time. It could mention response format details or error behavior, but covers the essential safety profile adequately.
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 perfectly structured: opening statement of purpose, bulleted list of specific capabilities, and clear return value description. Every sentence earns its place with zero wasted words, and information is front-loaded effectively.
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 zero-parameter diagnostic tool with no annotations and no output schema, the description provides complete context: purpose, when to use, what it returns, and how it differs from other tools. The return format description ('Dict[str, Any]') compensates for the missing output schema.
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 0 parameters with 100% schema coverage, so the baseline would be 4. The description appropriately doesn't discuss parameters since none exist, maintaining focus on the tool's purpose and behavior.
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 with specific verbs ('check server availability', 'get basic server information') and distinguishes it from siblings by emphasizing it's a 'lightweight way' for connectivity verification rather than complex API operations like search_splunk or list_users.
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 explicitly states when to use this tool: 'to check server availability', 'verify the server is running and responsive', and 'check connectivity without making complex API calls'. It clearly distinguishes this from more complex operations available in sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Execute a Splunk search query and return the results.
Args:
search_query: The search query to execute
earliest_time: Start time for the search (default: 24 hours ago)
latest_time: End time for the search (default: now)
max_results: Maximum number of results to return (default: 100)
Returns:
List of search results
| Name | Required | Description | Default |
|---|---|---|---|
| search_query | Yes | ||
| earliest_time | No | -24h | |
| latest_time | No | now | |
| max_results | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool executes a search and returns results, but lacks details on permissions required, rate limits, whether it's read-only or has side effects, response format, or error handling. For a tool with 4 parameters and no annotation coverage, this is a significant gap in transparency.
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 and appropriately sized. It starts with a clear purpose statement, followed by a bulleted list of args with explanations and defaults, and ends with return information. Every sentence earns its place by adding value, with no redundant or verbose content, making it easy to parse and understand.
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 complexity (4 parameters, no annotations, no output schema), the description is partially complete. It covers parameter semantics well but lacks behavioral context like permissions or rate limits, and without an output schema, it doesn't detail the structure of the 'List of search results'. This leaves gaps for an AI agent to fully understand tool behavior and outputs.
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 description adds substantial meaning beyond the input schema, which has 0% description coverage. It explains each parameter's purpose: 'search_query' as the query to execute, 'earliest_time' and 'latest_time' as time bounds with defaults, and 'max_results' as a limit. This compensates fully for the schema's lack of descriptions, providing clear semantics for all parameters.
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: 'Execute a Splunk search query and return the results.' This specifies the verb ('execute') and resource ('Splunk search query'), making it understandable. However, it doesn't explicitly differentiate this tool from potential siblings like 'list_saved_searches' or 'get_indexes_and_sourcetypes', which might also involve search-related operations, so it falls short of a perfect score.
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'list_saved_searches' for retrieving pre-defined searches or 'get_indexes_and_sourcetypes' for exploring data sources, nor does it specify prerequisites or contexts for executing searches. This lack of comparative information limits its utility for an AI agent in selecting the right tool.
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.
current_userget_index_infoget_indexes_and_sourcetypeshealthhealth_checklist_indexeslist_kvstore_collectionslist_saved_searcheslist_toolslist_userspingsearch_splunkScored across 12 tools
There is significant overlap between several tools, which could cause confusion. For example, 'health' and 'health_check' appear to be duplicates, and 'list_indexes', 'get_indexes_and_sourcetypes', and 'get_index_info' all relate to indexes with unclear boundaries. However, descriptions help differentiate some tools, such as 'search_splunk' being distinct for query execution.
Naming is inconsistent with mixed conventions. Some tools use verb_noun patterns like 'list_indexes' and 'search_splunk', while others use noun-only forms like 'health' and 'ping'. There are also deviations like 'get_indexes_and_sourcetypes' using 'and' in the name, and 'list_tools' is an outlier as it's meta to the server itself.
With 12 tools, the count is reasonable for a Splunk MCP server, covering user management, indexing, searches, and health checks. It's slightly heavy due to redundant tools like 'health' and 'health_check', but overall well-scoped for the domain without being overwhelming.
The toolset covers core Splunk operations like searching, indexing, and user management, but has notable gaps. For example, there are tools to list saved searches but no way to create, update, or delete them, and similar gaps exist for KV store collections and indexes. This could lead to dead ends for agents trying to perform full lifecycle management.
Official Microsoft MCP Server to query Microsoft Entra data using natural language
Query your org's data in natural language — read-only MCP access to SQL, NoSQL, files & warehouses.
Your org's AI agents, tasks, runs, search, and brain files as MCP tools and resources.
Governed data discovery, exact queries, decisions, simulations, and runtime utilities over MCP.
