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MaxCompute MCP Server

MaxCompute MCP Server

by datafe
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

read_query

Execute SELECT queries on MaxCompute to extract and analyze data from your tables.

Instructions

Execute a SELECT query on the MaxCompute project, only SELECT query is allowed

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "read_query"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesThe SELECT SQL query

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.3

TDQS

C2.9/5.0
Behavior2/5

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 only that it executes a SELECT query, but omits details such as whether results are returned directly, any limits on query size or execution time, idempotency, or safety. This leaves significant behavioral ambiguity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very concise—one sentence with clear front-loaded verb and object. It contains no fluff, but could include slightly more context without sacrificing conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite the tool's simplicity, the description fails to explain what the output of the query is (e.g., returns result set). Without an output schema, this is a critical gap. It also does not address edge cases or error conditions, leaving the agent underinformed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has one parameter 'query' with description 'The SELECT SQL query', providing 100% coverage. The tool description adds 'only SELECT query is allowed', which is redundant with the schema's parameter description. Thus the description adds no new semantic value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Execute a SELECT query' and the resource 'MaxCompute project'. It also includes the constraint that only SELECT queries are allowed, which clarifies its read-only intent. However, it does not explicitly differentiate from sibling tools like describe_table or list_tables, which also read data but in different ways.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It only mentions that SELECT queries are allowed, implying it is not for DDL/DML, but it does not reference sibling tools or describe contexts where each is appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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