Official StarRocks MCP server for SQL execution, database exploration, StarRocks resource templates, system information, table/database overviews, and Plotly chart generation.
StarRocks MCP can execute SQL through `read_query` and DDL/DML through `write_query`., The `write_query` tool is documented for CREATE, ALTER, DROP, INSERT, UPDATE, DELETE, and other commands that do not return a result set., The `read_query` tool can write full query results to files when `output_file` is provided; relative paths use STARROCKS_MCP_OUTPUT_DIR, while absolute and home-relative paths bypass that directory., Overview tools can sample rows, count rows, cache table summaries in memory, and return cached errors or stale summaries until refreshed., The `query_and_plotly_chart` tool executes a SQL query and uses a Python Plotly expression to generate chart output from the result DataFrame., Resource templates can expose database, table, proc, transaction, job, task, catalog, frontend, backend, and compute-node information., SSE mode is documented as deprecated; prefer stdio or streamable HTTP with explicit network controls.
Privacy notes
StarRocks credentials, connection URLs, database names, table names, schemas, SQL text, query results, sample rows, proc output, generated chart images, errors, and output-file paths may be visible to the MCP client and model provider., StarRocks datasets can contain customer records, telemetry, analytics, business metrics, security data, billing data, or other regulated information., Output files created by `read_query` remain on the machine where the MCP server runs, which may be local for desktop clients or remote for HTTP deployments., STARROCKS_URL, STARROCKS_PASSWORD, Keychain service names, database credentials, and generated result files should stay out of prompts, issues, logs, screenshots, and committed files.
Author
StarRocks
Submitted by
oktofeesh1
Claim status
unclaimed
Last verified
2026-06-06
Decision playbook
Review trust signals before you adopt
Signals are present but mixed. Use the checklist below to confirm the source and operational safety for your environment.
Compare context
Selected
0
Current score
63
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No major trust-signal divergence detected in the current selection.
Source and provenance checks
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Source link availableRequired
Open the canonical repository and verify ownership.
Done
Source provenance statusRequired
Marked as source-backed.
Done
Metadata reviewed
No reviewed flag detected in metadata.
Pending
Safety and privacy checks
Complete
Validate risk disclosures before installation or API wiring.
Safety notes presentRequired
Review the listed safety guidance before running commands.
Done
Privacy notes presentRequired
Review data handling notes before connecting accounts or secrets.
Done
Trust level risk gateRequired
Trust level does not block evaluation.
Done
Package and install checks
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Install payload available
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Done
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7 safety and 4 privacy notes across 6 risk areas. Review closely: credentials & tokens, network access.
6 areas
SafetyExecution & processesStarRocks MCP can execute SQL through `read_query` and DDL/DML through `write_query`.
SafetyExecution & processesThe `write_query` tool is documented for CREATE, ALTER, DROP, INSERT, UPDATE, DELETE, and other commands that do not return a result set.
SafetyLocal filesThe `read_query` tool can write full query results to files when `output_file` is provided; relative paths use STARROCKS_MCP_OUTPUT_DIR, while absolute and home-relative paths bypass that directory.
SafetyData retentionOverview tools can sample rows, count rows, cache table summaries in memory, and return cached errors or stale summaries until refreshed.
SafetyExecution & processesThe `query_and_plotly_chart` tool executes a SQL query and uses a Python Plotly expression to generate chart output from the result DataFrame.
SafetyData retentionResource templates can expose database, table, proc, transaction, job, task, catalog, frontend, backend, and compute-node information.
SafetyNetwork accessSSE mode is documented as deprecated; prefer stdio or streamable HTTP with explicit network controls.
PrivacyCredentials & tokensStarRocks credentials, connection URLs, database names, table names, schemas, SQL text, query results, sample rows, proc output, generated chart images, errors, and output-file paths may be visible to the MCP client and model provider.
PrivacyTelemetryStarRocks datasets can contain customer records, telemetry, analytics, business metrics, security data, billing data, or other regulated information.
PrivacyNetwork accessOutput files created by `read_query` remain on the machine where the MCP server runs, which may be local for desktop clients or remote for HTTP deployments.
PrivacyCredentials & tokensSTARROCKS_URL, STARROCKS_PASSWORD, Keychain service names, database credentials, and generated result files should stay out of prompts, issues, logs, screenshots, and committed files.
Safety notes
StarRocks MCP can execute SQL through `read_query` and DDL/DML through `write_query`.
The `write_query` tool is documented for CREATE, ALTER, DROP, INSERT, UPDATE, DELETE, and other commands that do not return a result set.
The `read_query` tool can write full query results to files when `output_file` is provided; relative paths use STARROCKS_MCP_OUTPUT_DIR, while absolute and home-relative paths bypass that directory.
Overview tools can sample rows, count rows, cache table summaries in memory, and return cached errors or stale summaries until refreshed.
The `query_and_plotly_chart` tool executes a SQL query and uses a Python Plotly expression to generate chart output from the result DataFrame.
Resource templates can expose database, table, proc, transaction, job, task, catalog, frontend, backend, and compute-node information.
SSE mode is documented as deprecated; prefer stdio or streamable HTTP with explicit network controls.
Privacy notes
StarRocks credentials, connection URLs, database names, table names, schemas, SQL text, query results, sample rows, proc output, generated chart images, errors, and output-file paths may be visible to the MCP client and model provider.
StarRocks datasets can contain customer records, telemetry, analytics, business metrics, security data, billing data, or other regulated information.
Output files created by `read_query` remain on the machine where the MCP server runs, which may be local for desktop clients or remote for HTTP deployments.
STARROCKS_URL, STARROCKS_PASSWORD, Keychain service names, database credentials, and generated result files should stay out of prompts, issues, logs, screenshots, and committed files.
Prerequisites
Python 3.10 or newer and uv available to the MCP client runtime.
StarRocks FE host, MySQL protocol port, database, username, and password.
Least-privilege StarRocks user with only the approved databases and operations.
Written policy for whether `write_query` may be used in the workspace.
Approved output directory when allowing `read_query` to write large results to disk.
StarRocks MCP Server is the official StarRocks Model Context Protocol server for
connecting Claude and other MCP clients to StarRocks databases. It supports SQL
execution, database and table exploration, StarRocks resource templates, system
information through proc paths, table and database overviews, and Plotly chart
generation from query results.
The package is published as mcp-server-starrocks and exposes the
mcp-server-starrocks command. The README documents stdio, HTTP, and
streamable HTTP modes, while noting that SSE mode is deprecated and no longer
maintained.
These sources were reviewed on 2026-06-06. Prefer the live repository,
README, PyPI metadata, package metadata, server entry point, implementation,
and license for current install commands, transport modes, environment
variables, tool behavior, resource templates, and licensing.
Features
Official StarRocks MCP server.
PyPI package mcp-server-starrocks.
Stdio, HTTP, streamable HTTP, and deprecated SSE transport modes.
read_query for SELECT, SHOW, DESCRIBE, and other result-returning commands.
write_query for DDL, DML, and other non-result commands.
analyze_query for query profile or explain-analyze output.
query_and_plotly_chart for SQL-backed Plotly chart generation.
table_overview and db_overview with row counts, schema details, sample rows, and in-memory caching.
starrocks:// resource templates for databases, tables, and schemas.
proc:// resource template for StarRocks internal system information.
Environment configuration through individual connection variables or STARROCKS_URL.
Use a dedicated StarRocks user with only the permissions needed for the
workflow. Do not enable write_query workflows unless writes are intended and
reviewed.
Use Cases
Ask Claude to inspect StarRocks databases and tables before drafting SQL.
Run reviewed SELECT queries for analytics and debugging.
Generate table or database overviews with schema details, row counts, and sample rows.
Analyze a query with profile or explain-analyze output.
Create a Plotly chart from an approved query result.
Explore StarRocks proc paths for frontend, backend, compute-node, transaction, job, task, and catalog information.
Write large read-query results to an approved output directory when model context is too small.
Safety and Privacy
StarRocks MCP is a database execution surface. Use least-privilege credentials,
review generated SQL, and require explicit approval for write_query, DDL,
DML, broad reads, output-file writes, proc inspection, and chart generation over
sensitive datasets.
read_query can write files on the server host, and absolute paths bypass the
configured output directory. Keep result paths controlled, avoid exposing
production credentials, and treat generated charts and cached overviews as
derived data that may reveal private rows or schema details.
Disclosure
StarRocks offers open-source and commercial analytics database products. This
listing is not sponsored, paid, or affiliate-driven, and it is scoped to the
source-backed official StarRocks MCP server.
Duplicate Check
No StarRocks MCP Server, StarRocks/mcp-server-starrocks,
mcp-server-starrocks, or matching source URL entry was found in
content/mcp, content/tools, content/guides, content/agents, or
content/skills.
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How it compares
StarRocks MCP Server side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.
3 trust signals differ across this comparison (Package trust, Source provenance, Submitter).
Official StarRocks MCP server for SQL execution, database exploration, StarRocks resource templates, system information, table/database overviews, and Plotly chart generation.
✓StarRocks MCP can execute SQL through `read_query` and DDL/DML through `write_query`.
The `write_query` tool is documented for CREATE, ALTER, DROP, INSERT, UPDATE, DELETE, and other commands that do not return a result set.
The `read_query` tool can write full query results to files when `output_file` is provided; relative paths use STARROCKS_MCP_OUTPUT_DIR, while absolute and home-relative paths bypass that directory.
Overview tools can sample rows, count rows, cache table summaries in memory, and return cached errors or stale summaries until refreshed.
The `query_and_plotly_chart` tool executes a SQL query and uses a Python Plotly expression to generate chart output from the result DataFrame.
Resource templates can expose database, table, proc, transaction, job, task, catalog, frontend, backend, and compute-node information.
SSE mode is documented as deprecated; prefer stdio or streamable HTTP with explicit network controls.
✓The server exposes a single `query` tool that can execute any valid DuckDB SQL statement against the configured database.
Without `--readonly`, the server can create the database file, create tables, insert data, update rows, delete rows, and mutate database state.
With `--readonly`, the server opens DuckDB with native read-only protection and fails to start if the database file or parent directory is missing.
The `--keep-connection` option can hold a persistent DuckDB connection and file lock for the server lifetime.
Treat SQL generated by a model as executable code; review queries before running them on important data.
✓MariaDB MCP connects Claude to a live database and exposes schema inspection plus SQL execution tools.
The default read-only mode allows SELECT, SHOW, DESCRIBE, DESC, and USE-style queries, but the README warns that database privileges are the only reliable way to guarantee read-only access.
The server includes a `create_database` tool and optional vector-store tools that can create, insert into, search, and delete vector-store tables when enabled.
If `MCP_READ_ONLY=false` or credentials have broad privileges, model-generated SQL can create, modify, delete, or expose database state.
The server checks for risky FILE privilege behavior, but teams should revoke FILE and other unnecessary global privileges from the connected MariaDB user.
HTTP and SSE transports require explicit authentication and restricted host/origin settings before any non-local use.
Embedding providers can receive document text or derived content when vector-store tooling is enabled.
✓Use a read-only database user with restricted schemas unless write access is explicitly needed and reviewed.
Privacy notes
✓StarRocks credentials, connection URLs, database names, table names, schemas, SQL text, query results, sample rows, proc output, generated chart images, errors, and output-file paths may be visible to the MCP client and model provider.
StarRocks datasets can contain customer records, telemetry, analytics, business metrics, security data, billing data, or other regulated information.
Output files created by `read_query` remain on the machine where the MCP server runs, which may be local for desktop clients or remote for HTTP deployments.
STARROCKS_URL, STARROCKS_PASSWORD, Keychain service names, database credentials, and generated result files should stay out of prompts, issues, logs, screenshots, and committed files.
✓Tool calls and results can expose database paths, table names, schemas, query text, row values, file paths referenced by SQL, and analytical results to the MCP client and model provider.
DuckDB can query local files and extensions depending on SQL, configuration, and installed capabilities; keep the server scoped to approved data directories.
Do not point writable sessions at production, customer, regulated, or irreplaceable DuckDB files without backups and explicit approval.
Query errors can reveal schema names, file paths, and data-shape details.
✓Database credentials, hostnames, database names, schemas, table names, column names, SQL text, query results, errors, and log files may be visible to the MCP client and model provider.
MariaDB data can include customer records, credentials, audit logs, business metrics, payment data, healthcare data, or other regulated information.
Vector-store tables can persist source documents, embeddings, metadata, and semantic-search results inside MariaDB.
OPENAI_API_KEY, GEMINI_API_KEY, HF_MODEL settings, SSL certificate paths, dotenv files, and database passwords should stay out of prompts, issues, logs, screenshots, and committed files.
Review log retention because the server writes logs to `logs/mcp_server.log` by default.
✓Queried schemas, table names, rows, connection details, and application data may be exposed through tool calls.
Prerequisites
Python 3.10 or newer and uv available to the MCP client runtime.
StarRocks FE host, MySQL protocol port, database, username, and password.
Least-privilege StarRocks user with only the approved databases and operations.
Written policy for whether `write_query` may be used in the workspace.
Python and `uvx` available to the MCP client runtime.
Existing DuckDB database file when using `--readonly`.
Path to a DuckDB database file that Claude is allowed to query.
Decision on whether the server should run in read-only mode before connecting it to an agent.
Python 3.11 and uv available to the MCP client runtime.
Reviewed checkout of the MariaDB MCP repository with dependencies installed.
MariaDB database host, port, database name, username, and password.
Least-privilege MariaDB user scoped to only the databases and operations Claude should access.
PostgreSQL database (local or remote PostgreSQL server)
PostgreSQL connection string (PostgreSQL connection URI with user, password, host, port, and database format)
Node.js and npx (for running @modelcontextprotocol/server-postgres package)
Network access (if connecting to remote PostgreSQL server)
Install
uv run --with mcp-server-starrocks mcp-server-starrocks