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OceanBase Oracle MCP Server

OceanBase Oracle MCP Server

by EwanJasper
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get_table_stats

Retrieves real-time and statistics-based table metrics including row counts, average row length, block count, column count, and last analysis time for Oracle tables.

Instructions

获取表的统计信息：实时行数、统计信息行数、平均行长度、块数、列数、最后分析时间。

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": {
      "table_name": "<table_name>"
    },
    "name": "get_table_stats"
  }
}

Input Schema

NameRequiredDescriptionDefault
table_nameYesOracle 表名（大写）

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description must fully disclose behavioral traits. It only lists output fields and does not mention read-only nature, required permissions, potential staleness of statistics, or any side effects. This is insufficient for safe agent invocation.

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

Conciseness5/5

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

The description is a single, focused sentence that immediately conveys the tool's function and lists the key statistics. Every word earns its place with no redundancy, making it highly concise and efficiently front-loaded.

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

Completeness3/5

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

Given no output schema, the description adequately specifies the return fields (six statistics), which is moderately complete for an agent to understand what information is retrieved. However, it omits behavioral context like execution cost, access requirements, and return format, which are needed for complete understanding.

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?

Schema description coverage is 100% for the single parameter (table_name), so the schema already provides its meaning. The description adds no additional semantics beyond what the schema states, keeping the score at baseline.

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

Purpose5/5

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

The description clearly states the tool's purpose: getting table statistics. It explicitly lists the specific statistics returned (real row count, stats row count, avg row length, blocks, columns, last analysis time), matching the tool name and distinguishing it from siblings like get_table_schema.

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 like get_table_schema or query_db. It lacks context such as prerequisites, performance implications, or scenarios where this tool is preferred, leaving the agent to infer usage from the name alone.

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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