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DeepSeek-MCP

DeepSeek-MCP

by Abbas-F-R
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

review_sql

Analyzes SQL queries, table schemas, migrations, and index performance to identify issues and optimize database behavior.

Instructions

Review SQL queries, table schemas, database migrations, and index performance

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": {
      "sql_content": "<sql_content>"
    },
    "name": "review_sql"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelNoModel to use (default: deepseek-chat)
providerNoAI Provider
sql_contentYesSQL queries or DDL schema definition
database_typeNoDB Engine (e.g. PostgreSQL, SQL Server, MySQL, SQLite)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.2/5.0
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 does not reveal any behavioral traits beyond the act of reviewing, such as whether it sends data to an external AI model, returns a detailed report, or has any side effects. This lack of transparency leaves significant uncertainty for the agent.

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, concise sentence that front-loads the verb and resource list. It avoids filler and uses every word meaningfully, making it easy to scan.

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?

The description lists what it reviews but omits expected output, return format, or any behavioral context. There is no output schema, and the description does not explain what the agent should expect after invocation, making it incomplete for a review tool that presumably returns analysis or suggestions.

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%, and each parameter has a clear description (e.g., sql_content, database_type). The tool description itself adds no extra parameter context, but since the schema already provides full semantics, the baseline score of 3 is appropriate.

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 with a specific verb 'Review' and lists concrete resources: SQL queries, table schemas, database migrations, and index performance. This distinguishes it from sibling tools like generate_sql or review_code by focusing on SQL-specific review tasks.

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?

No guidance is provided on when to use this tool versus alternatives such as generate_sql or review_performance. There is no mention of prerequisites, explicit use cases, or exclusions, leaving the agent to infer applicability without support.

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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