StarRocks MCP 服务器充当 AI 助手和 StarRocks 数据库之间的桥梁。它允许直接执行 SQL、探索数据库、通过图表进行数据可视化以及检索详细的架构/数据概览，而无需复杂的客户端设置。
**直接 SQL 执行：**运行SELECT查询（ read_query ）和 DDL/DML 命令（ write_query ）。
**数据库探索：**列出数据库和表，检索表模式（ starrocks://资源）。
**系统信息：**通过proc://资源路径访问内部 StarRocks 指标和状态。
**详细概述：**获取表（ table_overview ）或整个数据库（ db_overview ）的综合摘要，包括列定义、行数和样本数据。
**数据可视化：**执行查询并直接从结果生成 Plotly 图表（ query_and_plotly_chart ）。
**智能缓存：**表格和数据库概览缓存在内存中，以加快重复请求的速度。必要时可绕过缓存。
**灵活的配置：**通过环境变量设置连接详细信息和行为。
Related MCP server: starrocks-mcp
MCP 服务器通常通过 MCP 主机运行。配置会传递给主机，指定如何启动 StarRocks MCP 服务器进程。
使用已安装包的uv ：
{
"mcpServers": {
"mcp-server-starrocks": {
"command": "uv",
"args": [
"run",
"--with",
"mcp-server-starrocks",
"mcp-server-starrocks"
],
"env": {
"STARROCKS_HOST": "default localhost",
"STARROCKS_PORT": "default 9030",
"STARROCKS_USER": "default root",
"STARROCKS_PASSWORD": "default empty",
"STARROCKS_DB": "default empty",
"STARROCKS_OVERVIEW_LIMIT": "default 20000"
}
}
}
}使用uv和本地目录（用于开发）：
{
"mcpServers": {
"mcp-server-starrocks": {
"command": "uv",
"args": [
"--directory",
"path/to/mcp-server-starrocks", // <-- Update this path
"run",
"mcp-server-starrocks"
],
"env": {
"STARROCKS_HOST": "default localhost",
"STARROCKS_PORT": "default 9030",
"STARROCKS_USER": "default root",
"STARROCKS_PASSWORD": "default empty",
"STARROCKS_DB": "default empty",
"STARROCKS_OVERVIEW_LIMIT": "default 20000"
}
}
}
}环境变量：
STARROCKS_HOST ：（可选）StarRocks FE 服务的主机名或 IP 地址。默认为localhost 。
STARROCKS_PORT ：（可选）StarRocks FE 服务的 MySQL 协议端口。默认为9030 。
STARROCKS_USER ：（可选）StarRocks 用户名。默认为root 。
STARROCKS_PASSWORD ：（可选）StarRocks 密码。默认为空字符串。
STARROCKS_DB ：（可选）如果工具参数或资源 URI 中未指定，则使用默认数据库。设置后，连接将尝试USE此数据库。如果table_overview和db_overview等工具的参数中省略了数据库部分，则会使用此数据库。默认为空（无默认数据库）。
STARROCKS_OVERVIEW_LIMIT ：（可选）概览工具（ table_overview 、 db_overview ）在获取数据填充缓存时生成的文本总数的近似字符数限制。这有助于防止非常大的模式或大量的表占用过多的内存。默认为20000 。
read_query
**描述：**执行 SELECT 查询或返回 ResultSet 的其他命令（例如SHOW 、 DESCRIBE ）。
输入： { "query": "SQL query string" }
**输出：**包含查询结果的文本内容，格式类似于 CSV，包含标题行和行数摘要。失败时返回错误消息。
write_query
**描述：**执行不返回 ResultSet 的 DDL（ CREATE 、 ALTER 、 DROP ）、DML（ INSERT 、 UPDATE 、 DELETE ）或其他 StarRocks 命令。
输入： { "query": "SQL command string" }
**输出：**确认成功的文本内容（例如，“查询成功，X 行受影响”）或报告错误。成功后，更改将自动提交。
query_and_plotly_chart
**描述：**执行 SQL 查询，将结果加载到 Pandas DataFrame 中，并使用提供的 Python 表达式生成 Plotly 图表。专为支持 UI 中的可视化而设计。
输入：
{
"query": "SQL query to fetch data",
"plotly_expr": "Python expression string using 'px' (Plotly Express) and 'df' (DataFrame). Example: 'px.scatter(df, x=\"col1\", y=\"col2\")'"
}**输出：**包含以下内容的列表：
TextContent ：DataFrame 的文本表示以及图表用于 UI 显示的注释。
ImageContent ：生成的 Plotly 图表以 base64 编码，为 PNG 图像（ image/png ）。查询失败或未产生数据时，返回文本错误消息。
table_overview
**描述：**获取特定表的概览：列（来自DESCRIBE ）、总行数和示例行数（ LIMIT 3 ）。除非refresh为 true，否则使用内存缓存。
输入：
{
"table": "Table name, optionally prefixed with database name (e.g., 'db_name.table_name' or 'table_name'). If database is omitted, uses STARROCKS_DB environment variable if set.",
"refresh": false // Optional, boolean. Set to true to bypass the cache. Defaults to false.
}**输出：**包含格式化概览（列数、行数、示例数据）或错误消息的文本内容。缓存结果包含先前的错误（如适用）。
db_overview
**描述：**获取指定数据库中所有表的概览（列数、行数、示例行数）。除非refresh为 true，否则将使用每个表的表级缓存。
输入：
{
"db": "database_name", // Optional if STARROCKS_DB env var is set.
"refresh": false // Optional, boolean. Set to true to bypass the cache for all tables in the DB. Defaults to false.
}**输出：**文本内容包含数据库中所有表的串联概览，并以标题分隔。如果数据库无法访问或不包含任何表，则返回错误消息。
starrocks:///databases
**描述：**列出配置用户可以访问的所有数据库。
等效查询： SHOW DATABASES
MIME 类型： text/plain
starrocks:///{db}/{table}/schema
**描述：**获取特定表的架构定义。
等效查询： SHOW CREATE TABLE {db}.{table}
MIME 类型： text/plain
starrocks:///{db}/tables
**描述：**列出特定数据库中的所有表。
等效查询： SHOW TABLES FROM {db}
MIME 类型： text/plain
proc:///{+path}
**描述：**访问 StarRocks 内部系统信息，类似于 Linux 的/proc 。 path参数指定所需的信息节点。
等效查询： SHOW PROC '/{path}'
MIME 类型： text/plain
常见路径：
/frontends有关 FE 节点的信息。
/backends - 有关 BE 节点的信息（用于非云原生部署）。
/compute_nodes - 有关 CN 节点的信息（用于云原生部署）。
/dbs有关数据库的信息。
/dbs/<DB_ID> - 根据 ID 获取特定数据库的信息。
/dbs/<DB_ID>/<TABLE_ID> - 根据 ID 获取特定表的信息。
/dbs/<DB_ID>/<TABLE_ID>/partitions - 表的分区信息。
/transactions按数据库分组的交易信息。
/transactions/<DB_ID> - 特定数据库 ID 的事务信息。
/transactions/<DB_ID>/running - 为数据库 ID 运行事务。
/transactions/<DB_ID>/finished - 数据库 ID 的已完成事务。
/jobs有关异步作业（模式更改、汇总等）的信息。
/statistic每个数据库的统计信息。
/tasks有关代理任务的信息。
/cluster_balance负载平衡状态信息。
/routine_loads有关例行加载作业的信息。
/colocation_group - 有关 Colocation Join 组的信息。
/catalog - 有关已配置目录的信息（例如，Hive、Iceberg）。
此服务器未定义任何内容。
table_overview和db_overview工具利用内存缓存来存储生成的概览文本。
缓存键是(database_name, table_name)的元组。
调用table_overview时，它会首先检查缓存。如果结果存在且refresh参数为false （默认值），则立即返回缓存结果。否则，它会从 StarRocks 获取数据，将其存储在缓存中，然后返回。
当调用db_overview时，它会列出数据库中的所有表，然后尝试使用与table_overview相同的缓存逻辑（首先检查缓存，如果需要则获取，如果refresh为false或缓存未命中）检索每个表的概览。如果db_overview的refresh为true ，则会强制刷新该数据库中的所有表。
STARROCKS_OVERVIEW_LIMIT环境变量为填充缓存时每个表生成的概览字符串的最大长度提供了一个软目标，有助于管理内存使用情况。
缓存结果（包括原始提取期间遇到的任何错误消息）都会被存储并在后续缓存命中时返回。
Analyze a query and get analyze result using query profile. Use set_session_db to set a per-session default database
| Name | Required | Description | Default |
|---|---|---|---|
| db | No | database | |
| sql | No | Query SQL | |
| uuid | No | Query ID, a string composed of 32 hexadecimal digits formatted as 8-4-4-4-12 |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions 'using query profile' but does not disclose whether the tool is read-only, requires authentication, has side effects, or what state (e.g., query must be previously executed) is needed. The behavioral traits are minimal.
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 two sentences, front-loaded with the purpose, and includes a concise usage hint. Every sentence adds value without unnecessary words or repetition.
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 presence of an output schema and the moderate complexity (3 parameters), the description covers the basic purpose and provides a hint about the database parameter. However, it does not clarify the difference between analyzing by SQL vs. UUID, or that the query may need to have been executed first. It is adequate but leaves gaps.
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?
Schema description coverage is 100%, providing baseline parameter descriptions. The description adds value by explaining that 'set_session_db' can set a per-session default database, indirectly clarifying that the 'db' parameter may be omitted if a default is set. This goes beyond the schema's simple 'database' label.
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 states the action ('analyze a query') and the resource ('query'), and mentions using 'query profile', which indicates the tool's specific function. However, the phrasing 'get analyze result' is slightly redundant, and it doesn't clearly distinguish from sibling tools like 'db_summary' or 'read_query'.
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 includes a hint to use 'set_session_db' for setting a default database, but provides no guidance on when to use this tool versus alternatives (e.g., 'read_query' or 'query_and_plotly_chart'). There is no mention of prerequisites, exclusions, or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run a query to get it's query dump and profile, output very large, need special tools to do further processing
| Name | Required | Description | Default |
|---|---|---|---|
| db | No | database | |
| query | Yes | query to execute |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the output is very large and needs special tools, which is useful. However, it does not mention other behavioral traits like destructiveness, permissions, or side effects, leaving some gaps.
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 sentence, front-loaded with the action. It is concise and to the point, though a bit more structure could improve readability.
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 tool has 2 parameters, no output schema, and no nested objects, the description provides adequate context but does not explain what 'query dump' and 'profile' entail or the return format. It is minimally complete for a simple tool.
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?
Schema description coverage is 100% (both parameters have descriptions: 'database' and 'query to execute'). The description adds no additional meaning beyond the schema, so baseline 3 is appropriate.
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 states 'Run a query to get it's query dump and profile', which clearly identifies the verb (run) and resource (query dump and profile). It also mentions the output is very large, adding context. However, it does not differentiate from sibling tools like 'query_and_plotly_chart' or 'read_query'.
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 implies usage context by stating 'need special tools to do further processing', suggesting this tool is for large outputs requiring post-processing. However, it does not explicitly state when to use this tool versus alternatives, nor provide conditions to avoid.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Quickly get summary of a database with tables' schema and size information. Use set_session_db to set a per-session default database
| Name | Required | Description | Default |
|---|---|---|---|
| db | No | Database name. Optional: uses current database by default. | |
| limit | No | Output length limit in characters. Defaults to 10000. Higher values show more tables and details. | |
| refresh | No | Set to true to force refresh, ignoring cache. Defaults to false. |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided; the description does not disclose if the tool is read-only, cached, or has side effects. It mentions the refresh parameter but does not explain caching behavior in text, leaving agents without key safety cues.
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 concise sentence with an additional useful hint. It is front-loaded and contains no filler, efficiently conveying purpose and context.
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 three parameters and no annotations, the description covers the main use case but falls short on behavioral transparency. The presence of an output schema reduces the need to describe return values. Overall adequate but with gaps.
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 for all parameters. The description adds some value by linking the 'db' parameter to set_session_db, but does not significantly expand on parameter meaning beyond the schema.
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 tool retrieves a database summary including table schema and size, with a specific verb and resource. It distinguishes from siblings by mentioning set_session_db for default database context.
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 indicates when to use (quickly get summary) and references set_session_db for setting a default database. However, it does not explicitly state when not to use or compare to siblings like read_query or table_overview.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
using sql query to extract data from database, then using python plotly_expr to generate a chart for UI to display. Use set_session_db to set a per-session default database
| Name | Required | Description | Default |
|---|---|---|---|
| db | No | database | |
| query | Yes | SQL query to execute | |
| format | No | chart output format, json|png|jpeg | jpeg |
| plotly_expr | Yes | a one function call expression, with 2 vars binded: `px` as `import plotly.express as px`, and `df` as dataframe generated by query `plotly_expr` example: `px.scatter(df, x="sepal_width", y="sepal_length", color="species", marginal_y="violin", marginal_x="box", trendline="ols", template="simple_white")` |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It explains the two-step process (query then chart) but does not mention side effects, errors, rate limits, or output format details. The description is simple but lacks depth.
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 concise with two sentences, the first clearly stating the main function. The second sentence provides a related tip but is somewhat tangential. It is well-structured and front-loaded.
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 no output schema and no annotations, the description is fairly complete for a combined query-chart tool. It explains the process and mentions a prerequisite. However, it lacks details on output format or error handling, leaving some gaps.
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?
Schema coverage is 100% with descriptions, so baseline is 3. The description adds context about the overall workflow but does not elaborate on individual parameters beyond the schema. The mention of set_session_db is peripheral.
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 it uses SQL query to extract data and then generates a chart with plotly_expr. It distinguishes from siblings like read_query (which only returns data) by explicitly mentioning chart generation. However, it could be more precise by contrasting with other query tools.
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 mentions using set_session_db to set a default database, which is helpful but does not provide guidance on when to use this tool over its siblings (e.g., read_query for data only, analyze_query for analysis). No explicit exclusions or alternatives are given.
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 or commands that return a ResultSet. Set output_file to write the full result to disk instead of returning it inline (useful for large results).. Use set_session_db to set a per-session default database
| Name | Required | Description | Default |
|---|---|---|---|
| db | No | database | |
| query | Yes | SQL query to execute | |
| output_file | No | If set, write the full result to this file and return only a summary + small preview inline. Relative paths resolve against STARROCKS_MCP_OUTPUT_DIR (default: ~/.mcp-server-starrocks/output/). Absolute paths (and ~) are used as-is. Format is inferred from the file extension (.csv, .tsv, .json, .jsonl, .ndjson) unless output_format is given. NOTE: the file is written on the server's filesystem, which may not be the client machine in remote/http deployments. | |
| output_format | No | Override file format: csv|tsv|json|jsonl. If omitted, inferred from output_file extension; defaults to csv. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully disclose behavioral traits. It explains the output_file feature and notes that files are written on the server's filesystem, which is important for remote deployments. However, it does not explicitly state that the tool is read-only or discuss error handling or authentication.
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 concise, with the purpose stated first. It includes two sentences plus a minor note, and every part adds value. The only flaw is an extra period after 'inline'.
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?
The description lacks details about the return structure when output_file is not used. It mentions returning 'inline' but does not specify the format or content (e.g., rows, columns). Given there is no output schema, this information is crucial for correct usage.
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?
Schema coverage is 100%, so baseline is 3. The description adds significant value beyond the schema for output_file and output_format, explaining path resolution, environment variables, and format inference. For db, it adds no extra meaning.
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 explicitly states 'Execute a SELECT query or commands that return a ResultSet', which provides a specific verb and resource. It distinguishes this tool from siblings like write_query and analyze_query by focusing on read-only queries that produce a result set.
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 explicit guidance on when to use this tool versus alternatives (e.g., write_query for modifications, analyze_query for explaining). The only instruction is to use set_session_db for default database, which is a side note, not a usage guideline for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Set or clear the default database for THIS MCP session. Subsequent tool calls without an explicit db argument will use this database. Pass an empty string or null to clear and fall back to the server's global default. Returns the new effective default for this session.
| Name | Required | Description | Default |
|---|---|---|---|
| db | No | Database name to set as the per-session default. Empty/null clears the override. |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully carries the burden. It discloses that setting affects subsequent calls without explicit db argument, clarifies clearing behavior, and states the return value. No behavioral contradictions.
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?
Two sentences, front-loaded with purpose, no redundant words. Every sentence adds critical information.
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 single parameter and no annotations, the description is fully complete. It explains purpose, usage, parameter semantics, and return value. No gaps.
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?
Schema coverage is 100%, but the description adds value by explaining that empty string or null clears the override, which is not explicitly in the schema. It clarifies the parameter's effect beyond the bare description.
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 tool sets or clears the default database for the MCP session, using specific verbs ('set', 'clear', 'fall back'). It distinguishes from sibling query tools by focusing on session state management.
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 explains when to use (set default) and how to clear (empty/null). While it doesn't explicitly state when not to use or list alternatives, the context of sibling tools makes the usage clear. The guidance is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get an overview of a specific table: columns, sample rows (up to 3), and total row count. Uses cache unless refresh=true. Use set_session_db to set a per-session default database
| Name | Required | Description | Default |
|---|---|---|---|
| table | Yes | Table name, optionally prefixed with database name (e.g., 'db_name.table_name'). If database is omitted, uses the default database. | |
| refresh | No | Set to true to force refresh, ignoring cache. Defaults to false. |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses caching behavior and refresh option, which is important for understanding tool behavior. No destructive actions are implied.
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?
Two sentences, no fluff. First sentence clearly states the tool's action and output, second provides important context about caching and database setup. Every word earns its place.
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 simplicity (2 params, output schema exists), the description covers key aspects: output components, caching, default database. Minor omission like error behavior or limit on sample rows is compensated by output schema.
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?
Schema coverage is 100%, and the description does not add semantic meaning beyond the schema descriptions. The parameter details are fully covered by the schema, so description adds no extra 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 it provides an overview of a table including columns, sample rows, and row count. However, it does not explicitly differentiate from sibling tools like read_query or analyze_query, which are for querying rather than generating a summary.
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 gives context on caching and default database setup via set_session_db, but lacks explicit guidance on when to use this tool versus alternatives (e.g., read_query for raw data).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Execute a DDL/DML or other StarRocks command that do not have a ResultSet. Use set_session_db to set a per-session default database
| Name | Required | Description | Default |
|---|---|---|---|
| db | No | database | |
| query | Yes | SQL to execute |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description covers non-ResultSet nature but lacks details on side effects, permissions, or error behavior.
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?
Single sentence, front-loaded with core purpose, no unnecessary words.
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?
Missing output schema; description doesn't specify return format or error handling, but purpose and parameters are adequately covered.
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?
Schema covers both parameters; description adds value by suggesting set_session_db for default database, enhancing understanding of db parameter.
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?
Description clearly states it executes DDL/DML commands without ResultSet, distinguishing it from sibling tools like read_query which returns results.
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?
Mentions using set_session_db for default database but does not explicitly state when to use vs alternatives or when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.4.0read_query2 fields changedInput schema / properties / output_fileAdded value: +{
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "default": null,
+ "description": "If set, write the full result to this file and return only a summary + small preview inline. Relative paths resolve against STARROCKS_MCP_OUTPUT_DIR (default: ~/.mcp-server-starrocks/output/). Absolute paths (and ~) are used as-is. Format is inferred from the file extension (.csv, .tsv, .json, .jsonl, .ndjson) unless output_format is given. NOTE: the file is written on the server's filesystem, which may not be the client machine in remote/http deployments."
+}Input schema / properties / output_formatAdded value: +{
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "default": null,
+ "description": "Override file format: csv|tsv|json|jsonl. If omitted, inferred from output_file extension; defaults to csv."
+}set_session_dbv0.3.0analyze_query12 fields changedInput schema / additionalPropertiesAdded value: +falseInput schema / properties / dbAdded value: +{
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "default": null,
+ "description": "database"
+}Input schema / properties / sql / anyOfAdded value: +[
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+]Input schema / properties / sql / defaultAdded value: +nullInput schema / properties / sql / titleRemoved value: -"Sql"Input schema / properties / sql / typeRemoved value: -"string"Input schema / properties / uuid / anyOfAdded value: +[
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+]Input schema / properties / uuid / defaultAdded value: +nullInput schema / properties / uuid / titleRemoved value: -"Uuid"Input schema / properties / uuid / typeRemoved value: -"string"Input schema / requiredRemoved value: -[
- "uuid",
- "sql"
-]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "result": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "result"
+ ],
+ "type": "object",
+ "x-fastmcp-wrap-result": true
+}collect_query_dump_and_profiledb_overviewdb_summaryquery_and_plotly_chart5 fields changedInput schema / additionalPropertiesAdded value: +falseInput schema / properties / dbAdded value: +{
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "default": null,
+ "description": "database"
+}Input schema / properties / formatAdded value: +{
+ "default": "jpeg",
+ "description": "chart output format, json|png|jpeg",
+ "type": "string"
+}Input schema / properties / plotly_expr / titleRemoved value: -"Plotly Expr"Input schema / properties / query / titleRemoved value: -"Query"read_query3 fields changedInput schema / additionalPropertiesAdded value: +falseInput schema / properties / dbAdded value: +{
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "default": null,
+ "description": "database"
+}Input schema / properties / query / titleRemoved value: -"Query"table_overview4 fields changedInput schema / additionalPropertiesAdded value: +falseInput schema / properties / refresh / titleRemoved value: -"Refresh"Input schema / properties / table / titleRemoved value: -"Table"Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "result": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "result"
+ ],
+ "type": "object",
+ "x-fastmcp-wrap-result": true
+}write_query3 fields changedInput schema / additionalPropertiesAdded value: +falseInput schema / properties / dbAdded value: +{
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "default": null,
+ "description": "database"
+}Input schema / properties / query / titleRemoved value: -"Query"v1.0.0analyze_querydb_overviewquery_and_plotly_chartread_querytable_overviewwrite_queryScored across 8 tools
Each tool targets a distinct function: query execution, analysis, chart generation, schema summary, etc. There is slight overlap between read_query and query_and_plotly_chart, but their outputs differ (raw data vs chart), and descriptions clarify the distinction. No major confusion.
Tool names follow no consistent pattern: some are verb_noun (e.g., read_query), some are noun_verb (e.g., db_summary), some include conjunctions (query_and_plotly_chart), and verbs vary (analyze, collect, set, write). This inconsistency may confuse an agent trying to infer tool purposes from naming.
With 8 tools, the server is well-scoped for a database MCP server covering querying, analysis, schema browsing, and charting. Each tool serves a clear purpose without being excessive or minimal.
The tool set covers core database interactions: query (read and write), analysis, schema overview, and charting. A minor gap is the absence of a tool to list all databases, but db_summary and set_session_db partially address this. Overall, the surface is reasonably complete for the stated purpose.
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