What can you do with this server?

The IoTDB MCP Server provides an interface for interacting with IoTDB databases with the following capabilities: * SQL Query Execution: Execute SELECT queries using both Tree and Table SQL dialects for data retrieval * Metadata Operations: Run SHOW and COUNT queries for metadata (databases, timeseries, devices) * Schema Management: List all tables and describe table schemas (column names and types) * Data Export: Export query results to CSV or Excel files * Performance Optimizations: Includes session pooling, connection retry mechanisms, and timeout management * Connectivity: Configure connections using host, port, username, password, and database name * SQL Dialect Support: Switch between "Tree" and "Table" SQL dialects as needed

Which integrations are available for this server?

Provides database interaction with Apache IoTDB, enabling execution of SQL queries to read data, list tables, and describe table schemas.

How do I use IoTDB MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@IoTDB MCP Server show me the list of all tables in the database" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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IoTDB MCP Server

Official

by apache

Overview Schema Related Servers Score Discussions

Python

Hybrid

IoTDB MCP Server

English | 中文

Overview

A Model Context Protocol (MCP) server implementation that provides database interaction and business intelligence capabilities through IoTDB. This server enables running SQL queries and interacting with IoTDB using different SQL dialects (Tree Model and Table Model).

Related MCP server: XiYan MCP Server

Components

Resources

The server doesn't expose any resources.

Prompts

The server doesn't provide any prompts.

Tools

The server offers different tools for IoTDB Tree Model and Table Model. You can choose between them by setting the "IOTDB_SQL_DIALECT" configuration to either "tree" or "table".

Tree Model

metadata_query
- Execute SHOW/COUNT queries to read metadata from the database
- Input:
  - query_sql (string): The SHOW/COUNT SQL query to execute
- Supported query types:
  - SHOW DATABASES [path]
  - SHOW TIMESERIES [path]
  - SHOW CHILD PATHS [path]
  - SHOW CHILD NODES [path]
  - SHOW DEVICES [path]
  - COUNT TIMESERIES [path]
  - COUNT NODES [path]
  - COUNT DEVICES [path]
- Returns: Query results as array of objects
select_query
- Execute SELECT queries to read data from the database
- Input:
  - query_sql (string): The SELECT SQL query to execute (using TREE dialect, time using ISO 8601 format, e.g. 2017-11-01T00:08:00.000)
- Supported functions:
  - SUM, COUNT, MAX_VALUE, MIN_VALUE, AVG, VARIANCE, MAX_TIME, MIN_TIME, etc.
- Returns: Query results as array of objects
export_query
- Execute a query and export the results to a CSV or Excel file
- Input:
  - query_sql (string): The SQL query to execute (using TREE dialect)
  - format (string): Export format, either "csv" or "excel" (default: "csv")
  - filename (string): Optional filename for the exported file. If not provided, a unique filename will be generated.
- Returns: Information about the exported file and a preview of the data (max 10 rows)

Table Model

Query Tools

read_query
- Execute SELECT queries to read data from the database
- Input:
  - query_sql (string): The SELECT SQL query to execute (using TABLE dialect, time using ISO 8601 format, e.g. 2017-11-01T00:08:00.000)
- Returns: Query results as array of objects

Schema Tools

list_tables
- Get a list of all tables in the database
- No input required
- Returns: Array of table names
describe_table
- View schema information for a specific table
- Input:
  - table_name (string): Name of table to describe
- Returns: Array of column definitions with names and types
export_table_query
- Execute a query and export the results to a CSV or Excel file
- Input:
  - query_sql (string): The SQL query to execute (using TABLE dialect)
  - format (string): Export format, either "csv" or "excel" (default: "csv")
  - filename (string): Optional filename for the exported file. If not provided, a unique filename will be generated.
- Returns: Information about the exported file and a preview of the data (max 10 rows)

Configuration Options

IoTDB MCP Server supports the following configuration options, which can be set via environment variables or command-line arguments:

Option	Environment Variable	Default Value	Description
--host	IOTDB_HOST	127.0.0.1	IoTDB host address
--port	IOTDB_PORT	6667	IoTDB port
--user	IOTDB_USER	root	IoTDB username
--password	IOTDB_PASSWORD	root	IoTDB password
--database	IOTDB_DATABASE	test	IoTDB database name
--sql-dialect	IOTDB_SQL_DIALECT	table	SQL dialect: tree or table
--export-path	IOTDB_EXPORT_PATH	/tmp	Path for exporting query results

Performance Optimizations

IoTDB MCP Server includes the following performance optimization features:

Session Pool Management: Uses optimized session pool configurations, supporting up to 100 concurrent sessions
Optimized Fetch Size: For queries, a fetch size of 1024 is set
Connection Retry: Configured automatic retry mechanism for connection failures
Timeout Management: Session wait timeout set to 5000 milliseconds for improved reliability
Export Functionality: Support for exporting query results to CSV or Excel formats

Prerequisites

Python environment
uv package manager
IoTDB installation
MCP server dependencies

Development

# 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

Claude Desktop Integration

Configure the MCP server in Claude Desktop's configuration file:

macOS

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

Windows

Location: %APPDATA%/Claude/claude_desktop_config.json

You may need to put the full path to the uv executable in the command field. You can get this by running which uv on MacOS/Linux or where uv on Windows.

Claude Desktop Configuration Example

Add the following configuration to Claude Desktop's configuration file:

{
  "mcpServers": {
    "iotdb": {
      "command": "uv",
      "args": [
        "--directory",
        "/Users/your_username/iotdb-mcp-server/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",
        "IOTDB_EXPORT_PATH": "/path/to/export/folder"
      }
    }
  }
}

Note: Make sure to replace the --directory parameter's path with your actual repository clone path.

Error Handling and Logging

IoTDB MCP Server includes comprehensive error handling and logging capabilities:

Log Level: Logging level is set to INFO, allowing you to view server status in the console
Exception Handling: All database operations include exception handling to ensure graceful handling and meaningful error messages when errors occur
Session Management: Automatic closure of used sessions to prevent resource leaks
Parameter Validation: Basic validation of user-input SQL queries to ensure only allowed query types are executed

Docker Support

You can build a container image for the IoTDB MCP Server using the Dockerfile in the project root:

# Build Docker image
docker build -t iotdb-mcp-server .

# Run container
docker run -e IOTDB_HOST=<your-iotdb-host> -e IOTDB_PORT=<your-iotdb-port> -e IOTDB_USER=<your-iotdb-user> -e IOTDB_PASSWORD=<your-iotdb-password> iotdb-mcp-server

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

Name	Required	Description	Default
`table_name`	Yes

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

Name	Required	Default
`query_sql`	Yes
`format`	No	csv
`filename`	No

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

Name	Required	Description	Default
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

Name	Required	Description	Default
`query_sql`	Yes

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.

3 tool updatesv1.0.0Jan 3, 2026
- 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"
```
4 tool updatesAug 15, 2025
- First observeddescribe_table
- First observedexport_table_query
- First observedlist_tables
- First observedread_query

Install Server

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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

GitHub Repository

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