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apache

IoTDB MCP Server

Official
by apache
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Python
Hybrid

Servidor MCP de IoTDB

insignia de herrería

Descripción general

Una implementación de servidor del Protocolo de Contexto de Modelo (MCP) que proporciona interacción con bases de datos y capacidades de inteligencia empresarial a través de IoTDB. Este servidor permite ejecutar consultas SQL.

Related MCP server: XiYan MCP Server

Componentes

Recursos

El servidor no expone ningún recurso.

Indicaciones

El servidor no proporciona ningún mensaje.

Herramientas

El servidor ofrece diferentes herramientas para el modelo de árbol y el modelo de tabla de IoTDB. Puede elegir entre ellas configurando "IOTDB_SQL_DIALECT" en "árbol" o "tabla".

Modelo de árbol

  • metadata_query

    • Ejecute consultas SHOW/COUNT para leer metadatos de la base de datos

    • Aporte:

      • query_sql (cadena): La consulta SQL SHOW/COUNT que se ejecutará

    • Devuelve: Resultados de la consulta como una matriz de objetos

  • select_query

    • Ejecutar consultas SELECT para leer datos de la base de datos

    • Aporte:

      • query_sql (cadena): La consulta SQL SELECT que se ejecutará

    • Devuelve: Resultados de la consulta como una matriz de objetos

Modelo de tabla

Herramientas de consulta

  • read_query

    • Ejecutar consultas SELECT para leer datos de la base de datos

    • Aporte:

      • query (cadena): La consulta SQL SELECT que se ejecutará

    • Devuelve: Resultados de la consulta como una matriz de objetos

Herramientas de esquema

  • list_tables

    • Obtener una lista de todas las tablas en la base de datos

    • No se requiere entrada

    • Devuelve: Matriz de nombres de tablas

  • describe-table

    • Ver información del esquema para una tabla específica

    • Aporte:

      • table_name (cadena): Nombre de la tabla a describir

    • Devuelve: Matriz de definiciones de columnas con nombres y tipos

Integración de escritorio de Claude

Prerrequisitos

  • Python con el gestor de paquetes uv

  • Instalación de IoTDB

  • Dependencias del servidor MCP

Desarrollo

# Clone the repository
git clone https://github.com/apache/iotdb-mcp-server.git
cd iotdb_mcp_server

# Create virtual environment
uv venv
source venv/bin/activate  # or `venv\Scripts\activate` on Windows

# Install development dependencies
uv sync

Configure el servidor MCP en el archivo de configuración de Claude Desktop:

Sistema operativo Mac

Ubicación: ~/Library/Application Support/Claude/claude_desktop_config.json

Ventanas

Ubicación: %APPDATA%/Claude/claude_desktop_config.json

Es posible que necesites introducir la ruta completa del ejecutable uv en el campo de comandos. Puedes obtenerla ejecutando which uv en macOS/Linux o where uv en Windows.

{
  "mcpServers": {
    "iotdb": {
      "command": "uv",
      "args": [
        "--directory",
        "YOUR_REPO_PATH/src/iotdb_mcp_server",
        "run",
        "server.py"
      ],
      "env": {
        "IOTDB_HOST": "127.0.0.1",
        "IOTDB_PORT": "6667",
        "IOTDB_USER": "root",
        "IOTDB_PASSWORD": "root",
        "IOTDB_DATABASE": "test",
        "IOTDB_SQL_DIALECT": "table"
      }
    }
  }
}

Available Tools

4 tools
describe_tableC

Get the schema information for a specific table Args: table_name: name of the table to describe

ParametersJSON Schema
NameRequiredDescriptionDefault
table_nameYes

TDQS

C2.6/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves schema information but doesn't describe what that includes (e.g., column names, types, constraints), whether it's a read-only operation, potential errors (e.g., if the table doesn't exist), or any rate limits. This leaves significant gaps in understanding the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with two sentences, but the structure could be improved. The first sentence states the purpose clearly, but the second sentence is formatted as an 'Args:' section, which might be redundant with the input schema. It's front-loaded but includes unnecessary formatting that doesn't add value beyond the schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a schema retrieval tool with no annotations, no output schema, and low parameter documentation, the description is incomplete. It doesn't explain what the output includes (e.g., JSON structure, error handling), making it hard for an agent to use effectively without additional context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, so the description must compensate for the lack of parameter documentation. It adds minimal meaning by specifying that 'table_name' is the 'name of the table to describe', but this is basic and doesn't provide details like format, constraints, or examples. For a single parameter with no schema documentation, this is inadequate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

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 a specific verb ('Get') and resource ('schema information for a specific table'), making it easy to understand what it does. However, it doesn't explicitly differentiate from sibling tools like 'list_tables' (which might list table names without schema details) or 'read_query' (which might execute queries rather than describe structure), missing full sibling differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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_tables' or 'export_table_query', nor does it specify prerequisites or contexts for use, leaving the agent to infer usage based on the purpose alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

export_table_queryA

Execute a query and export the results to a CSV or Excel file.

Args: query_sql: The SQL query to execute (using TABLE dialect, time using ISO 8601 format, e.g. 2017-11-01T00:08:00.000) format: Export format, either "csv" or "excel" (default: "csv") filename: Optional filename for the exported file. If not provided, a unique filename will be generated.

SQL Syntax: SELECT ⟨select_list⟩ FROM ⟨tables⟩ [WHERE ⟨condition⟩] [GROUP BY ⟨groups⟩] [HAVING ⟨group_filter⟩] [FILL ⟨fill_methods⟩] [ORDER BY ⟨order_expression⟩] [OFFSET ⟨n⟩] [LIMIT ⟨n⟩];

Returns: Information about the exported file and a preview of the data (max 10 rows)

ParametersJSON Schema
NameRequiredDescriptionDefault
query_sqlYes
formatNocsv
filenameNo

TDQS

A4.2/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden and discloses key behavioral traits: it describes the export process, file format options, filename generation behavior, and preview limitations (max 10 rows). However, it doesn't mention potential side effects like file creation impacts, authentication needs, rate limits, or error handling for invalid SQL.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (Args, SQL Syntax, Returns) and front-loaded purpose statement. While comprehensive, some details like the full SQL syntax example might be excessive if the agent already understands SQL, but overall it's efficiently organized with minimal waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (query execution + file export), no annotations, no output schema, and 3 parameters, the description provides good coverage: purpose, parameters, SQL syntax, and return information. However, it could better address behavioral aspects like error conditions or file system impacts to be fully complete for this mutation tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by providing detailed semantic information for all 3 parameters: query_sql (SQL query with dialect and time format specifics), format (export format options with default), and filename (optional with auto-generation behavior). It adds substantial value beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

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 ('execute a query and export the results') and resources ('to a CSV or Excel file'), distinguishing it from sibling tools like describe_table, list_tables, and read_query which don't mention export functionality. It explicitly combines query execution with file export, making its unique role evident.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool (to execute queries and export results to files), but doesn't explicitly state when not to use it or name alternatives among sibling tools. It implies usage for file exports but lacks explicit guidance on choosing between this and read_query for non-export scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_tablesB

List all tables in the IoTDB database.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

B3.1/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden but only states the basic action. It doesn't disclose behavioral traits like whether it's read-only, pagination, rate limits, or error handling, leaving significant gaps for a tool with 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without any unnecessary words or fluff. It is appropriately sized and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations, no output schema, and a simple but incomplete description, the tool lacks context on behavior, output format, or usage scenarios. This is inadequate for a tool that might return a list of tables without further details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

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 appropriately doesn't discuss parameters, earning a high baseline score for this dimension.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('List') and resource ('all tables in the IoTDB database'), providing a specific purpose. However, it doesn't explicitly differentiate from sibling tools like 'describe_table' or 'export_table_query', 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.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives such as 'describe_table' or 'export_table_query'. The description only states what it does, without context for selection among siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

read_queryB

Execute a SELECT query on the IoTDB. Please use table sql_dialect when generating SQL queries.

Args: query_sql: The SQL query to execute (using TABLE dialect, time using ISO 8601 format, e.g. 2017-11-01T00:08:00.000)

ParametersJSON Schema
NameRequiredDescriptionDefault
query_sqlYes

TDQS

B3.3/5.0
Behavior2/5

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 mentions the SQL dialect and time format, which is useful context. However, it doesn't disclose critical behavioral traits: whether this is read-only (though implied by SELECT), authentication requirements, rate limits, error handling, result format, or pagination. For a query execution tool with zero annotation coverage, this leaves significant gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded: the first sentence states the core purpose, followed by specific guidance and parameter details. The 'Args:' section is clear and adds necessary information. While efficient, it could be slightly more structured by separating usage guidelines from parameter documentation.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (query execution with SQL), no annotations, no output schema, and 0% schema description coverage, the description is moderately complete. It covers the purpose, basic usage guidelines, and parameter semantics adequately. However, it lacks information about return values, error conditions, and behavioral constraints that would be needed for robust agent usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

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 that 'query_sql' is 'The SQL query to execute' and provides crucial context about the TABLE dialect and ISO 8601 time format. With only one parameter and the schema providing no descriptions, the description effectively compensates by explaining the parameter's purpose and constraints.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

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 SELECT query on the IoTDB' - a specific verb (execute) and resource (SELECT query on IoTDB). It distinguishes itself from siblings like 'describe_table', 'export_table_query', and 'list_tables' by focusing on query execution rather than metadata or export operations. However, it doesn't explicitly differentiate itself from potential write operations or other query types beyond SELECT.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides some usage context: 'Please use table sql_dialect when generating SQL queries' and mentions the TABLE dialect and ISO 8601 time format. However, it doesn't explicitly state when to use this tool versus alternatives like 'export_table_query' (which might handle results differently) or provide clear exclusion criteria for non-SELECT queries. The guidance is helpful but incomplete.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 3 tool updatesv1.0.0
    • Changeddescribe_table1 field changed
      • removedInput schema / properties / table_name / title
        Removed value: -"Table Name"
    • Changedexport_table_query3 fields changed
      • removedInput schema / properties / filename / title
        Removed value: -"Filename"
      • removedInput schema / properties / format / title
        Removed value: -"Format"
      • removedInput schema / properties / query_sql / title
        Removed value: -"Query Sql"
    • Changedread_query1 field changed
      • removedInput schema / properties / query_sql / title
        Removed value: -"Query Sql"
  2. 4 tool updates
    • First observeddescribe_table
    • First observedexport_table_query
    • First observedlist_tables
    • First observedread_query
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TDQS

B3.1/5.0
Disambiguation3/5

The tools have distinct primary functions (describe, export, list, read), but there's significant overlap between export_table_query and read_query as both execute SQL queries. The descriptions clarify that export_table_query focuses on file export while read_query returns query results directly, but an agent might still confuse when to use each for data retrieval.

Naming Consistency4/5

Three tools follow a consistent verb_noun pattern (describe_table, list_tables, export_table_query), while read_query deviates slightly by using 'read' instead of a more specific verb like 'execute' or 'run'. The naming is mostly predictable and readable, with only minor inconsistency in the verb choice for one tool.

Tool Count4/5

Four tools is a reasonable count for a database server, allowing core operations without being overwhelming. However, it feels slightly thin for full IoTDB coverage, as it lacks tools for table creation, deletion, or data insertion, which might be expected in a complete database interface.

Completeness2/5

The toolset is severely incomplete for a database server. It covers read operations (list, describe, query) and export, but lacks any write capabilities (create, insert, update, delete) or administrative functions. This creates significant gaps that will cause agent failures when trying to perform basic database management tasks.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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