Un servidor de Protocolo de contexto de modelo (MCP) para Azure Data Explorer/Eventhouse en Microsoft Fabric.
Esto proporciona acceso a sus clústeres y bases de datos de Azure Data Explorer/Eventhouse a través de interfaces MCP estandarizadas, lo que permite que los asistentes de IA ejecuten consultas KQL y exploren sus datos.
[x] Ejecutar consultas KQL en Azure Data Explorer
[x] Descubra y explore recursos de bases de datos
[x] Listar tablas en la base de datos configurada
[x] Ver esquemas de tablas
[x] Datos de muestra de tablas
[x] Obtener estadísticas/detalles de la tabla
[x] Soporte de autenticación
[x] Compatibilidad con credenciales de token (Azure CLI, MSI, etc.)
[x] Compatibilidad de credenciales de identidad de carga de trabajo para AKS
[x] Compatibilidad con contenedores Docker
[x] Proporcionar herramientas interactivas para asistentes de IA
La lista de herramientas es configurable, por lo que puede elegir qué herramientas quiere que estén disponibles para el cliente MCP. Esto resulta útil si no utiliza ciertas funciones o si no desea ocupar demasiado espacio en la ventana de contexto.
Related MCP server: EdgeLake MCP Server
Inicie sesión en su cuenta de Azure que tenga permiso para el clúster ADX mediante la CLI de Azure.
Configure las variables de entorno para su clúster ADX, ya sea a través de un archivo .env o variables de entorno del sistema:
# 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-tokenEl servidor ahora usa WorkloadIdentityCredential de forma predeterminada al ejecutarse en entornos de Azure Kubernetes Service (AKS) con la identidad de la carga de trabajo configurada. Prioriza el uso de WorkloadIdentityCredential siempre que estén presentes las variables de entorno necesarias.
Para AKS con Azure Workload Identity, solo necesita:
Asegúrese de que el pod tenga configuradas las variables de entorno AZURE_TENANT_ID y AZURE_CLIENT_ID
Asegúrese de que el archivo de token esté montado en la ruta predeterminada o especifique una ruta personalizada con ADX_TOKEN_FILE_PATH
Si estas variables de entorno no están presentes, el servidor recurrirá automáticamente a DefaultAzureCredential, que prueba varios métodos de autenticación en secuencia.
Agregue la configuración del servidor al archivo de configuración del cliente. Por ejemplo, para 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"
}
}
}
}Nota: si ve
Error: spawn uv ENOENTen Claude Desktop, es posible que deba especificar la ruta completa auvo establecer la variable de entornoNO_UV=1en la configuración.
Este proyecto incluye soporte para Docker para una fácil implementación y aislamiento.
Construya la imagen de Docker usando:
docker build -t adx-mcp-server .Puedes ejecutar el servidor usando Docker de varias maneras:
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-serverCree un archivo .env con sus credenciales de Azure Data Explorer y luego ejecute:
docker-compose upPara utilizar el servidor en contenedores con Claude Desktop, actualice la configuración para usar Docker con las variables de entorno:
{
"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"
}
}
}
}Esta configuración pasa las variables de entorno de Claude Desktop al contenedor Docker utilizando el indicador -e con solo el nombre de la variable y proporcionando los valores reales en el objeto env .
Este repositorio también puede utilizarse como contenedor de desarrollo para una experiencia de desarrollo fluida. La configuración del contenedor de desarrollo se encuentra en la carpeta devcontainer-feature/adx-mcp-server .
Para obtener más detalles, consulte el archivo README de devcontainer .
¡Agradecemos sus contribuciones! Abra un problema o envíe una solicitud de incorporación de cambios si tiene alguna sugerencia o mejora.
Este proyecto utiliza uv para gestionar las dependencias. Instale uv siguiendo las instrucciones para su plataforma:
curl -LsSf https://astral.sh/uv/install.sh | shLuego puede crear un entorno virtual e instalar las dependencias con:
uv venv
source .venv/bin/activate # On Unix/macOS
.venv\Scripts\activate # On Windows
uv pip install -e .El proyecto se ha organizado con una estructura de directorio src :
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 fileEl proyecto incluye un conjunto de pruebas integral que garantiza la funcionalidad y ayuda a prevenir regresiones.
Ejecute las pruebas con pytest:
# Install development dependencies
uv pip install -e ".[dev]"
# Run the tests
pytest
# Run with coverage report
pytest --cov=src --cov-report=term-missingLas pruebas se organizan en:
Pruebas de validación de configuración
Pruebas de funcionalidad del servidor
Pruebas de manejo de errores
Pruebas de aplicación principales
Al agregar nuevas funciones, agregue también las pruebas correspondientes.
Herramienta | Categoría | Descripción |
| Consulta | Ejecutar una consulta KQL en Azure Data Explorer |
| Descubrimiento | Listar todas las tablas en la base de datos configurada |
| Descubrimiento | Obtener el esquema para una tabla específica |
| Descubrimiento | Obtener datos de muestra de una tabla con tamaño de muestra opcional |
Instituto Tecnológico de Massachusetts (MIT)
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.
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