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StarRocks MCP Server

Official StarRocks MCP server for SQL execution, database exploration, StarRocks resource templates, system information, table/database overviews, and Plotly chart generation.

by StarRocks · submitted by oktofeesh1·added 2026-06-06·
Review first review before installing

Open the source and read safety notes before installing.

Citation facts

Source-backed facts for citing this resource, derived directly from the registry — also available as plain text for AI assistants.

Source URLs
https://github.com/StarRocks/mcp-server-starrocks/blob/main/README.md, https://github.com/StarRocks/mcp-server-starrocks, https://www.starrocks.io/
Brand
StarRocks
Brand domain
starrocks.io
Brand asset source
brandfetch
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.
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

Baseline

Delta

No baseline selected

No major trust-signal divergence detected in the current selection.

Source and provenance checks

Needs review

Confirm ownership and provenance before trusting install instructions.

  • 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

Needs review

Check package metadata and artifact integrity signals.

  • Install payload available

    Install or copy payload is available for review.

    Done
  • Package verification flag

    No package verification flag provided.

    Pending
  • Checksum metadata

    No checksum provided for downloaded artifact.

    Pending

Compare-driven decision checks

Needs review

Use compare context to validate trade-offs before adoption.

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  • Baseline comparison available

    No baseline peer selected yet.

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  • Diverging trust signals identified

    No major trust-signal divergence found.

    Pending

Setup at a glance

CLI install

Copy-ready — paste the snippet to get started.

15 minutes

Adoption plan

Balanced adoption plan

Current risk score 24/100. Use staged verification before broader rollout.

Risk 24

Pre-adoption checks

Validate source and review signals before any execution.

  • Confirm source provenanceRequired

    Source URL/provenance metadata is present.

    Done
  • Confirm metadata review state

    No review metadata found; increase manual validation.

    Pending
  • Verify install payload

    Install/config payload exists and can be inspected.

    Done

Security checks

Confirm safety, privacy, and package integrity signals.

  • Review safety notesRequired

    Safety notes are present.

    Done
  • Review privacy notesRequired

    Privacy notes are present.

    Done
  • Verify package integrity metadata

    No package verification/checksum metadata.

    Pending

Rollout

Adopt in controlled steps based on the selected plan.

  • Run in isolated sandbox firstRequired

    Use a constrained sandbox and observe behavior across multiple tasks.

    Pending
  • Roll out graduallyRequired

    Roll out to a small cohort before wider usage.

    Pending
  • Set monitoring and fallback

    Define rollback path and monitor errors after adoption.

    Pending

Evidence readiness

Evidence readiness matrix · balanced

Missing required evidence: Metadata review. Risk score 31.

Risk 31

Source provenance

Present

Source repository/provenance is listed.

Required in this preset

Metadata review

Missing

Review metadata is missing.

Required in this preset

Safety notes

Present

Safety notes are present.

Required in this preset

Privacy notes

Present

Privacy notes are present.

Optional in this preset

Package integrity

Missing

Package integrity metadata is missing.

Optional in this preset

Install payload

Present

Install payload is available.

Required in this preset

Required gaps: Metadata review

Decision timeline

Decision timeline · balanced

Blocking gaps: Check metadata review status. Risk 28.

Risk 28

triage

Confirm source provenanceRequired

Source/provenance metadata is available.

Done

triage

Check metadata review statusRequired

Review metadata is missing.

Pending

verify

Review safety notesRequired

Safety notes are available.

Done

verify

Review privacy notes

Privacy notes are available.

Done

verify

Validate package integrity metadata

Package integrity metadata is missing.

Pending

rollout

Verify install payload and commandsRequired

Install payload is available.

Done

Blockers: Check metadata review status

Prerequisite readiness

Prerequisite readiness

5 prerequisites to line up before setup. Have accounts and credentials ready first. Includes a review or approval gate.

0/5 ready
Account & credentials1Install & runtime1Network & hosting1Review & approval215 minutes

Safety & privacy surface

Safety & privacy surface

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.

Schema details

Install type
cli
Troubleshooting
No
Source repository stats
Scope
Source repo
Collection metadata
Estimated setup
15 minutes
Difficulty
advanced
Tool listing metadata
Full copyable content
{
  "mcpServers": {
    "starrocks": {
      "command": "uv",
      "args": ["run", "--with", "mcp-server-starrocks", "mcp-server-starrocks"],
      "env": {
        "STARROCKS_HOST": "<starrocks-fe-host>",
        "STARROCKS_PORT": "9030",
        "STARROCKS_USER": "<least-privilege-user>",
        "STARROCKS_PASSWORD": "<starrocks-password>",
        "STARROCKS_DB": "<approved-database>",
        "MCP_TRANSPORT_MODE": "stdio",
        "STARROCKS_MCP_OUTPUT_DIR": "<approved-output-dir>"
      }
    }
  }
}

About this resource

Content

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.

Source Review

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.
  • Optional macOS Keychain password lookup.

Installation

For local stdio usage with uv:

{
  "mcpServers": {
    "starrocks": {
      "command": "uv",
      "args": ["run", "--with", "mcp-server-starrocks", "mcp-server-starrocks"],
      "env": {
        "STARROCKS_HOST": "<starrocks-fe-host>",
        "STARROCKS_PORT": "9030",
        "STARROCKS_USER": "<least-privilege-user>",
        "STARROCKS_PASSWORD": "<starrocks-password>",
        "STARROCKS_DB": "<approved-database>",
        "MCP_TRANSPORT_MODE": "stdio",
        "STARROCKS_MCP_OUTPUT_DIR": "<approved-output-dir>"
      }
    }
  }
}

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.

Source citations

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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).

Field

Official StarRocks MCP server for SQL execution, database exploration, StarRocks resource templates, system information, table/database overviews, and Plotly chart generation.

Open dossier

MCP server for querying a local DuckDB database file from Claude through a single SQL query tool, with optional DuckDB-native read-only mode.

Open dossier

Official MariaDB MCP server for schema inspection, read-only SQL, optional database creation, and vector-store search against MariaDB databases.

Open dossier

Official MCP server providing read-only access to PostgreSQL databases with schema inspection and query capabilities

Open dossier
Next steps
Trust
Review statusNot reviewedNot reviewedNot reviewedNot reviewed
Package trustDiffersPackage not verifiedPackage not verifiedPackage not verifiedPackage verified
Source provenanceDiffersSource-backedSource-backedSource-backedNo submission link
SubmitterDiffersoktofeesh1oktofeesh1oktofeesh1
Install riskReview firstReview firstReview firstReview first
Notes Safety ✓ Privacy ✓ Safety ✓ Privacy ✓ Safety ✓ Privacy ✓ Safety ✓ Privacy ✓
BrandStarRocks logoStarRocksMariaDB logoMariaDBAnthropic logoAnthropic
Categorymcpmcpmcpmcp
SourceSource-backedSource-backedSource-backedFirst-party
AuthorStarRocksktanaka101MariaDBAnthropic
Added2026-06-062026-06-062026-06-062025-09-16
Platforms
Harness
Source repo
Safety notesStarRocks 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 notesStarRocks 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
uvx mcp-server-duckdb --db-path ./data.duckdb --readonly
uv run server.py
claude mcp list && claude mcp status postgres
Config
{
  "mcpServers": {
    "starrocks": {
      "command": "uv",
      "args": ["run", "--with", "mcp-server-starrocks", "mcp-server-starrocks"],
      "env": {
        "STARROCKS_HOST": "<starrocks-fe-host>",
        "STARROCKS_PORT": "9030",
        "STARROCKS_USER": "<least-privilege-user>",
        "STARROCKS_PASSWORD": "<starrocks-password>",
        "STARROCKS_DB": "<approved-database>",
        "MCP_TRANSPORT_MODE": "stdio",
        "STARROCKS_MCP_OUTPUT_DIR": "<approved-output-dir>"
      }
    }
  }
}
{
  "mcpServers": {
    "duckdb": {
      "command": "uvx",
      "args": ["mcp-server-duckdb", "--db-path", "./data.duckdb", "--readonly"]
    }
  }
}
{
  "mcpServers": {
    "mariadb": {
      "command": "uv",
      "args": ["--directory", "<mariadb-mcp-checkout>", "run", "server.py"],
      "env": {
        "DB_HOST": "<mariadb-host>",
        "DB_PORT": "3306",
        "DB_USER": "<least-privilege-user>",
        "DB_PASSWORD": "<mariadb-password>",
        "DB_NAME": "<approved-database>",
        "MCP_READ_ONLY": "true"
      }
    }
  }
}
Manual-only setup:
{
  "postgres": {
    "args": [
      "-y",
      "@modelcontextprotocol/server-postgres",
      "postgresql://localhost/mydb"
    ],
    "command": "npx"
  }
}
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