Microsoft Fabric 中 Azure 数据资源管理器/Eventhouse 的模型上下文协议(MCP) 服务器。
这可以通过标准化的 MCP 接口访问您的 Azure 数据资源管理器/Eventhouse 集群和数据库，从而允许 AI 助手执行 KQL 查询并探索您的数据。
[x] 针对 Azure 数据资源管理器执行 KQL 查询
[x] 发现和探索数据库资源
[x] 列出已配置数据库中的表
[x] 查看表模式
[x] 表格中的示例数据
[x] 获取表统计信息/详细信息
[x] 身份验证支持
[x] 令牌凭据支持（Azure CLI、MSI 等）
[x] AKS 的 Workload Identity 凭据支持
[x] Docker 容器化支持
[x] 为AI助手提供交互工具
工具列表是可配置的，因此您可以选择要向 MCP 客户端提供的工具。如果您不使用某些功能，或者不想占用太多上下文窗口空间，此功能非常有用。
Related MCP server: EdgeLake MCP Server
使用 Azure CLI 登录到具有 ADX 群集权限的 Azure 帐户。
通过.env文件或系统环境变量配置 ADX 集群的环境变量：
# Required: Azure Data Explorer configuration
ADX_CLUSTER_URL=https://yourcluster.region.kusto.windows.net
ADX_DATABASE=your_database
# Optional: Azure Workload Identity credentials
# AZURE_TENANT_ID=your-tenant-id
# AZURE_CLIENT_ID=your-client-id
# ADX_TOKEN_FILE_PATH=/var/run/secrets/azure/tokens/azure-identity-token现在，在配置了工作负载标识的 Azure Kubernetes 服务 (AKS) 环境中运行时，服务器默认使用 WorkloadIdentityCredential。只要存在必要的环境变量，它就会优先使用 WorkloadIdentityCredential。
对于具有 Azure Workload Identity 的 AKS，只需：For AKS with Azure Workload Identity, you only need to:
确保 Pod 已设置AZURE_TENANT_ID和AZURE_CLIENT_ID环境变量
确保令牌文件安装在默认路径或使用ADX_TOKEN_FILE_PATH指定自定义路径
如果这些环境变量不存在，服务器将自动回退到 DefaultAzureCredential，它会按顺序尝试多种身份验证方法。
将服务器配置添加到客户端配置文件中。例如，对于 Claude Desktop：
{
"mcpServers": {
"adx": {
"command": "uv",
"args": [
"--directory",
"<full path to adx-mcp-server directory>",
"run",
"src/adx_mcp_server/main.py"
],
"env": {
"ADX_CLUSTER_URL": "https://yourcluster.region.kusto.windows.net",
"ADX_DATABASE": "your_database"
}
}
}
}注意：如果您在 Claude Desktop 中看到
Error: spawn uv ENOENT，则可能需要指定uv的完整路径或在配置中设置环境变量NO_UV=1。
该项目包括 Docker 支持，以便于部署和隔离。
使用以下方式构建 Docker 镜像：
docker build -t adx-mcp-server .您可以通过多种方式使用 Docker 运行服务器：
docker run -it --rm \
-e ADX_CLUSTER_URL=https://yourcluster.region.kusto.windows.net \
-e ADX_DATABASE=your_database \
-e AZURE_TENANT_ID=your_tenant_id \
-e AZURE_CLIENT_ID=your_client_id \
adx-mcp-server使用您的 Azure 数据资源管理器凭据创建一个.env文件，然后运行：
docker-compose up要将容器化服务器与 Claude Desktop 一起使用，请更新配置以使用带有环境变量的 Docker：
{
"mcpServers": {
"adx": {
"command": "docker",
"args": [
"run",
"--rm",
"-i",
"-e", "ADX_CLUSTER_URL",
"-e", "ADX_DATABASE",
"-e", "AZURE_TENANT_ID",
"-e", "AZURE_CLIENT_ID",
"-e", "ADX_TOKEN_FILE_PATH",
"adx-mcp-server"
],
"env": {
"ADX_CLUSTER_URL": "https://yourcluster.region.kusto.windows.net",
"ADX_DATABASE": "your_database",
"AZURE_TENANT_ID": "your_tenant_id",
"AZURE_CLIENT_ID": "your_client_id",
"ADX_TOKEN_FILE_PATH": "/var/run/secrets/azure/tokens/azure-identity-token"
}
}
}
}此配置通过使用仅带有变量名的-e标志，并在env对象中提供实际值，将环境变量从 Claude Desktop 传递到 Docker 容器。
此存储库还可用作开发容器，以实现无缝的开发体验。开发容器的安装程序位于devcontainer-feature/adx-mcp-server文件夹中。
有关更多详细信息，请查看devcontainer README 。
欢迎贡献代码！如果您有任何建议或改进，请创建 issue 或提交 pull request。
本项目使用uv来管理依赖项。请按照您平台的说明安装uv ：
curl -LsSf https://astral.sh/uv/install.sh | sh然后，您可以创建一个虚拟环境并使用以下命令安装依赖项：
uv venv
source .venv/bin/activate # On Unix/macOS
.venv\Scripts\activate # On Windows
uv pip install -e .该项目已采用src目录结构进行组织：
adx-mcp-server/
├── src/
│ └── adx_mcp_server/
│ ├── __init__.py # Package initialization
│ ├── server.py # MCP server implementation
│ ├── main.py # Main application logic
├── Dockerfile # Docker configuration
├── docker-compose.yml # Docker Compose configuration
├── .dockerignore # Docker ignore file
├── pyproject.toml # Project configuration
└── README.md # This file该项目包括一个全面的测试套件，可确保功能并有助于防止回归。
使用 pytest 运行测试：
# Install development dependencies
uv pip install -e ".[dev]"
# Run the tests
pytest
# Run with coverage report
pytest --cov=src --cov-report=term-missing测试分为：
配置验证测试
服务器功能测试
错误处理测试
主要应用测试
当添加新功能时，请同时添加相应的测试。
工具 | 类别 | 描述 |
| 询问 | 对 Azure 数据资源管理器执行 KQL 查询 |
| 发现 | 列出配置数据库中的所有表 |
| 发现 | 获取特定表的架构 |
| 发现 | 从具有可选样本大小的表中获取样本数据 |
麻省理工学院
Executes a Kusto Query Language (KQL) query against the configured Azure Data Explorer database and returns the results as a list of dictionaries.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must disclose behavioral traits. It only states that the tool returns a list of dictionaries but does not mention whether queries can modify data, rate limits, or pagination behavior. The lack of safety disclaimers is a gap.
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 that immediately conveys the core action and result format. No unnecessary words or repetition.
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 simplicity of the tool (one parameter) and the presence of an output schema (not shown but indicated), the description covers the essential action. However, it lacks usage guidance and behavioral transparency, making it only minimally adequate.
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 single parameter 'query' has no schema description (0% coverage). The description adds the phrase 'Kusto Query Language (KQL)' which clarifies the language but does not explain expected syntax, format, or examples. The value added beyond the schema is minimal.
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 identifies the tool as executing a KQL query against Azure Data Explorer, with a specific verb ('executes') and resource ('KQL query'). It distinguishes itself from siblings like get_table_details by being the only tool that runs arbitrary queries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like list_tables or sample_table_data. There is no mention of prerequisites, safety considerations, or typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retrieves table details including TotalRowCount, HotExtentSize
| Name | Required | Description | Default |
|---|---|---|---|
| table_name | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It only states 'retrieves', hinting at read-only, but doesn't disclose auth needs, performance impact, or other behavioral traits.
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?
A single sentence is concise, but it is too brief to be fully informative. It could include more detail without being verbose, such as stating it's a read operation.
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?
Output schema exists, so return values are covered. However, the description lacks context on when to use, parameter details, and behavioral traits, making it incomplete for an agent's decision.
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 has one parameter (table_name) with 0% description coverage. The description does not explain the parameter format, constraints, or valid values, adding no value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves table details and lists specific metrics (TotalRowCount, HotExtentSize). It distinguishes from sibling tools like get_table_schema (schema) and sample_table_data (samples).
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 explicit guidance on when to use this tool vs alternatives. Usage is implied by the description of retrieving row count and extent size, but no conditions or exclusions are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retrieves the schema information for a specified table in the Azure Data Explorer database, including column names, data types, and other schema-related metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| table_name | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must fully disclose behavior. It states the action but does not mention read-only safety, error handling (e.g., if table does not exist), or any prerequisites. For a read operation, minimal but still insufficient given zero annotation coverage.
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 that is concise and directly states the purpose. No wasted words, but could be slightly more structured (e.g., separating purpose and details). Still effective and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given presence of output schema, description need not explain return values. For a simple one-parameter tool, the description covers the core function but misses usage guidelines and parameter details. Minimal viable but with clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has one parameter (table_name) with 0% description coverage. The tool description does not elaborate on table_name format, expected values, or constraints. With low schema coverage, the description should compensate but fails to add meaning beyond the parameter name.
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?
Description clearly states the verb 'retrieves', the resource 'schema information for a specified table', and includes specifics like 'column names, data types, and other schema-related metadata'. It easily distinguishes from siblings like 'execute_query' (runs queries) and 'list_tables' (lists tables).
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. For example, it does not explain that this is for schema metadata only, while 'get_table_details' might include more. Lacks any explicit context or exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retrieves a list of all tables available in the configured Azure Data Explorer database, including their names, folders, and database associations.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It indicates a read operation (retrieves) without mentioning side effects, auth needs, or rate limits. For a simple list operation, this is minimally adequate but lacks explicit disclosure of read-only 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, front-loaded with verb 'Retrieves'. Every word adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Complexity is low (empty schema, 0 parameters). Description covers what the tool returns (table names, folders, database associations). Presence of an output schema reduces burden, and the description aligns well.
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 no parameters (100% coverage), so baseline is 3. The description adds no parameter details, which is acceptable since there are none.
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?
Description clearly states the tool retrieves a list of all tables in the Azure Data Explorer database, specifying the returned fields (names, folders, database associations). This distinguishes it from sibling tools like get_table_details or get_table_schema which focus on individual tables or schemas.
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 mention when to use list_tables instead of execute_query, get_table_details, etc. Context about use cases or exclusions is absent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retrieves a random sample of rows from the specified table in the Azure Data Explorer database. The sample_size parameter controls how many rows to return (default: 10).
| Name | Required | Description | Default |
|---|---|---|---|
| table_name | Yes | ||
| sample_size | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior but only mentions sampling and sample_size default. It does not state whether the operation is read-only, the nature of randomness, or implications for large tables. Minimal 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?
Two sentences, front-loaded with purpose, no redundant words. Efficiently communicates core function and key parameter.
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 output schema exists, the description need not detail return values, but lacks context like the source database name (implied in description), column selection, or ordering. Sufficient for a simple sampling tool but minimal.
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 0% so description must compensate. It explains sample_size (default 10) but provides no meaning for table_name beyond its existence. Half the parameters are undocumented in meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it retrieves a random sample of rows from a specified table in Azure Data Explorer. It uses a specific verb and resource, and implicitly distinguishes from sibling tools like list_tables or execute_query by specifying sampling behavior.
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 use when a random sample is needed, but provides no explicit guidance on when to use this tool versus alternatives (e.g., execute_query for custom queries). No when-not or context about prerequisites 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.
v1.1.0execute_query1 field changedInput schema / additionalPropertiesAdded value: +falseget_table_details1 field changedInput schema / additionalPropertiesAdded value: +falseget_table_schema1 field changedInput schema / additionalPropertiesAdded value: +falselist_tables1 field changedInput schema / additionalPropertiesAdded value: +falsesample_table_data1 field changedInput schema / additionalPropertiesAdded value: +falsev1.0.0execute_query3 fields changedInput schema / properties / query / titleRemoved value: -"Query"Input schema / titleRemoved value: -"execute_queryArguments"Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "result": {
+ "items": {
+ "additionalProperties": true,
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "result"
+ ],
+ "type": "object",
+ "x-fastmcp-wrap-result": true
+}get_table_detailsget_table_schema3 fields changedInput schema / properties / table_name / titleRemoved value: -"Table Name"Input schema / titleRemoved value: -"get_table_schemaArguments"Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "result": {
+ "items": {
+ "additionalProperties": true,
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "result"
+ ],
+ "type": "object",
+ "x-fastmcp-wrap-result": true
+}list_tables2 fields changedInput schema / titleRemoved value: -"list_tablesArguments"Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "result": {
+ "items": {
+ "additionalProperties": true,
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "result"
+ ],
+ "type": "object",
+ "x-fastmcp-wrap-result": true
+}sample_table_data4 fields changedInput schema / properties / sample_size / titleRemoved value: -"Sample Size"Input schema / properties / table_name / titleRemoved value: -"Table Name"Input schema / titleRemoved value: -"sample_table_dataArguments"Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "result": {
+ "items": {
+ "additionalProperties": true,
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "result"
+ ],
+ "type": "object",
+ "x-fastmcp-wrap-result": true
+}execute_queryget_table_schemalist_tablessample_table_dataScored across 5 tools
Each tool targets a distinct purpose: listing tables, retrieving schema, retrieving details, sampling data, and executing arbitrary queries. No overlap and clear boundaries between tools.
All tool names follow a consistent verb_noun pattern (e.g., list_tables, get_table_schema, execute_query) using snake_case, making them predictable and easy to interpret.
Five tools is well-scoped for a read-only Azure Data Explorer query server, covering essential operations like listing, schema retrieval, details, sampling, and querying without unnecessary clutter.
The tool set provides a solid foundation for querying and metadata retrieval. Missing are data modification or ingestion tools, but for a query-focused server this is acceptable, though a bit more (e.g., table statistics) could enhance completeness.
Ask data questions in natural language. Get SQL, insights, and charts from your databases.
Connect your AI assistants to Keboola and expose your data, transformations, SQL queries, ...
Explore and query your data warehouse through Myriade's AI data analyst agent.
Query your team's drift, vulnerability, and upgrade data from any AI assistant. OAuth 2.1, 51 tools.
