Skip to main content
Glama
Sign In
DBJavaGenix

DBJavaGenix

by ZhaoXingPeng
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

ai_summarize_schema

Generate a natural language overview of a database schema, breaking it into modules, core entities, and relationships for easier understanding and LLM alignment.

Instructions

对整库 schema 做自然语言概述: 总览、模块划分 (按前缀)、核心实体 (列数 + 命中模式)、关键关系。便于用户理解大库,或在生成前对齐 LLM 的 mental model。

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_names": "<table_names>"
    },
    "name": "ai_summarize_schema"
  }
}

Input Schema

NameRequiredDescriptionDefault
table_namesYes整库表名列表
foreign_keysNo可选 FK 关系 [{from_table, to_table, ...}]
table_column_countsNo可选, {表名: 列数} 用于推断核心实体

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A3.8/5.0
Behavior3/5

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 describes the output structure but does not disclose any behavioral traits such as whether it performs AI inference, potential latency, required permissions, or side effects. The description adds context beyond the schema but lacks important behavioral disclosures.

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 a single sentence that front-loads the main purpose and then lists components. It is concise and free of fluff, though a slightly more structured breakdown (e.g., bullet points) could enhance readability.

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 the complexity (3 params, no output schema), the description provides a good high-level overview. However, it does not specify the return format or structure of the 'overview, modules, entities, relationships', leaving ambiguity. It covers the tool's purpose but not all contextual details needed for correct invocation.

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

Parameters4/5

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

Schema description coverage is 100%, so the baseline is 3. However, the description adds value by explaining that table_column_counts infer core entities and that foreign_keys support key relationships, which goes beyond the brief schema descriptions. This clarifies the role of optional parameters.

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 it provides a natural language overview of the entire database schema, including modules by prefix, core entities with column counts, and key relationships. This distinguishes it from sibling tools like db_table_describe or ai_infer_business_names which have narrower scopes.

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

Usage Guidelines3/5

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

The description mentions it is useful for understanding large databases or aligning LLM mental models before generation, but it does not explicitly contrast with sibling tools or state when not to use it. There is no guidance on alternatives or exclusions, making the usage context only implied.

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

Deploy Server

Other Tools