Provides access to Turso-hosted LibSQL databases, allowing retrieval of table lists, database schemas, table schemas, and execution of SELECT queries.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP-Turso show me the schema for the users table"
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.
A Model Context Protocol (MCP) server that provides access to the Turso-hosted LibSQL databases. Currently, the server provides the following functionality:
Retrieving a list of tables in a database
Retrieving a database's schema
Retrieving the schema of a table
Performing SELECT queries
Add this to your claude_desktop_config.json:
{
"mcpServers": [
"turso": {
"command": "npx",
"args": ["-y", "mcp-turso"],
"env": {
"TURSO_DATABASE_URL": "your_url",
"TURSO_AUTH_TOKEN": "your_token"
}
}
]
}You will need an existing database to continue. If you don't have one, create one. To get the database URL via the Turso CLI, run:
turso db show --url <database-name>Then get the database authentication token:
turso db tokens create <database-name>Add those values to your configuration as shown above.
To configure the Turso MCP server with Cursor, add the following to your Cursor settings:
Open Cursor and go to Settings (⚙️) > Settings (JSON)
Add the following configuration to your settings JSON:
"mcpServers": {
"turso": {
"command": "npx",
"args": ["-y", "mcp-turso"],
"env": {
"TURSO_DATABASE_URL": "your_url",
"TURSO_AUTH_TOKEN": "your_token"
}
}
}Replace your_url and your_token with your Turso database URL and authentication token as described in the previous section.
The server includes a custom logger for debugging outside of Claude Desktop. By default, this logger writes to <parent-dir>/logs/mcp-turso.log, where <parent-dir> is the parent directory of directory containing the mcp-turso script. In other words, if the path to mcp-turso is ~/foo/bin/mcp-turso, the logs will be at ~/foo/logs/mcp-turso.log. If running with NPX as above, the default logs will be:
~/.npm/_npx/<npx-dir-name>/node_modules/mcp-turso/logs/mcp-turso.logIf you would like to specify a custom path, you can include a --logs flag with an absolute posix path in the server's configuration:
{
"mcpServers": [
"turso": {
"command": "npx",
"args": ["-y", "mcp-turso", "--logs", "/Users/<username>/path/to/dir/mcp-logs.log"],
"env": {
"TURSO_DATABASE_URL": "your_url",
"TURSO_AUTH_TOKEN": "your_token"
}
}
]
}The path to the log file (default or custom) is always logged to stderr when the server is created. For Claude desktop, this will show up in your server logs in ~/Library/Logs/Claude.
Note: Right now, I haven't implemented specifying a custom logging file for Windows, but this is coming.
Related MCP server: Supabase MCP Server
The server provides the following tools:
list_tables
Get a list of all the tables in the database
No input
Returns: an array of table names
get_db_schema
Get the schemas of all tables in the database
No input
Returns: an array of SQL creation statements
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
query_database
Execute a SELECT query to read data from the database
Input:
sql (string): The SELECT SQL query to execute
Returns: Query results as an object of type { columns: string[]; rows: Record<string, unknown>[]; rowCount: number; }
Add the ability to specify a custom log file on windows
Add more query tools
MIT License - see the LICENSE file for details.
View schema information for a specific table
| Name | Required | Description | Default |
|---|---|---|---|
| table_name | Yes | Name of the table to describe |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It implies a read-only operation ('View'), but does not specify permissions, rate limits, error handling, or what 'schema information' entails (e.g., columns, types, constraints). This leaves significant gaps for a tool with mutation potential.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that is front-loaded and wastes no words. It directly conveys the core purpose without unnecessary elaboration, earning full marks for conciseness.
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 lack of annotations and output schema, the description is incomplete. It does not explain what 'schema information' includes or the return format, which is crucial for a tool that likely provides structured data. This leaves the agent with insufficient context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the parameter 'table_name' well-documented. The description adds no additional meaning beyond the schema, such as format examples or constraints, so it meets the baseline for high schema coverage without compensating value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('View') and resource ('schema information for a specific table'), making the purpose understandable. However, it does not explicitly differentiate from sibling tools like 'get_db_schema' or 'list_tables', which might also provide schema-related information, so it misses the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as 'get_db_schema' or 'list_tables'. It lacks context on prerequisites, exclusions, or specific scenarios, leaving the agent without clear usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the schema for all tables in the database
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('Get') but lacks details on permissions, rate limits, response format, or potential side effects. For a tool that likely returns structured data, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly. Every part of the sentence contributes directly to understanding the tool's purpose.
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 complexity (a tool that likely returns database metadata), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the schema includes (e.g., table names, columns, types) or the return format, leaving significant gaps for an AI agent to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate, but it could have mentioned if any implicit parameters (e.g., database connection) are assumed. Baseline for 0 params is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and resource ('schema for all tables in the database'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'describe_table' or 'list_tables', which might provide similar or overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as 'describe_table' or 'list_tables'. The description implies a broad scope ('all tables'), but it doesn't specify prerequisites, exclusions, or contextual recommendations for choosing this tool over others.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all tables in the database
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool lists tables but doesn't mention important behavioral aspects like whether it returns metadata, pagination behavior, performance characteristics, or error conditions. This leaves significant gaps for a tool that interacts with a database.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It's perfectly front-loaded and wastes no space, making it ideal for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a database tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what information is returned about each table, whether there are any constraints or limitations, or how this differs from sibling tools. The agent would need to guess about important operational details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so the schema already fully documents the input requirements. The description appropriately doesn't add parameter information beyond what's already covered, earning a baseline score of 4 for zero-parameter tools.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List') and resource ('all tables in the database'), making the tool's purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'describe_table' or 'get_db_schema', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives like 'get_db_schema' or 'describe_table'. The description only states what the tool does, leaving the agent to infer usage context without explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Execute a SELECT query to read data from the database
| Name | Required | Description | Default |
|---|---|---|---|
| sql | Yes | SQL query to execute |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is for 'read data' operations, which implies non-destructive behavior, but doesn't clarify important aspects like authentication requirements, rate limits, error handling, or result format. The description is too minimal to provide adequate transparency for a database query tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single sentence that communicates the essential purpose without any wasted words. It's front-loaded with the core functionality and uses precise technical language ('SELECT query', 'read data'). Every word earns its place in this minimal but complete statement.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a database query tool with no annotations and no output schema, the description is insufficiently complete. It doesn't address critical context like what happens with invalid SQL, whether transactions are supported, what the return format looks like, or any performance considerations. The description covers only the most basic purpose without addressing the operational complexity of database interactions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds no parameter-specific information beyond what's already in the schema (which has 100% coverage). The schema fully documents the single 'sql' parameter with its type and constraints. The description doesn't provide additional context about SQL syntax requirements, supported SQL features, or query limitations. Baseline score of 3 is appropriate when schema coverage is complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Execute a SELECT query') and resource ('read data from the database'), making the purpose immediately understandable. It distinguishes from siblings like 'describe_table' or 'list_tables' by focusing on query execution rather than metadata retrieval. However, it doesn't explicitly mention what type of database or data is involved, which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'get_db_schema' or 'describe_table'. It doesn't mention prerequisites (e.g., needing valid SQL syntax) or constraints (e.g., read-only queries only). While the description implies usage for reading data, it lacks explicit when-to-use or when-not-to-use instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a clearly distinct purpose with no overlap: describe_table focuses on a single table's schema, get_db_schema covers all tables' schemas, list_tables simply enumerates table names, and query_database handles data retrieval. An agent can easily differentiate these based on their specific scopes.
All tools follow a consistent verb_noun pattern (describe_table, get_db_schema, list_tables, query_database) with clear, descriptive names. The naming is uniform and predictable, making it easy for agents to understand the action and target.
With 4 tools, this server is well-scoped for database interaction, covering essential operations like schema inspection, table listing, and data querying. Each tool earns its place without feeling excessive or insufficient for the domain.
The tools provide solid coverage for read-only database operations, including schema exploration and data querying. However, there are minor gaps for a full database management scope, such as lacking write operations (e.g., insert, update, delete) or administrative functions, which agents might need to work around.
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