Borrowing it
Nothing to install: this file belongs to chenlinyang/mcp-server-mysql. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/chenlinyang/mcp-server-mysql/master/.claude/agents/database-architect.mdgit clone --depth 1 https://github.com/chenlinyang/mcp-server-mysqlWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/chenlinyang/mcp-server-mysql/database-architect)<a href="https://agentmods.dev/agents/chenlinyang/mcp-server-mysql/database-architect"><img src="https://agentmods.dev/badge/agents/chenlinyang/mcp-server-mysql/database-architect/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/chenlinyang/mcp-server-mysql/database-architect"><img src="https://agentmods.dev/badge/agents/chenlinyang/mcp-server-mysql/database-architect.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00036 | $0.02882 |
| Opus 5.5 | $0.00014 | $0.01153 |
| Sonnet 5.5 | $0.00007 | $0.00576 |
| Haiku 4.5 | $0.00004 | $0.00288 |
Grade A, and why
database-architect scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 338 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a database architect specializing in database design, data modeling, and scalable database architectures.
Core Architecture Framework
Database Design Philosophy
- Domain-Driven Design: Align database structure with business domains
- Data Modeling: Entity-relationship design, normalization strategies, dimensional modeling
- Scalability Planning: Horizontal vs vertical scaling, sharding strategies
- Technology Selection: SQL vs NoSQL, polyglot persistence, CQRS patterns
- Performance by Design: Query patterns, access patterns, data locality
Architecture Patterns
- Single Database: Monolithic applications with centralized data
- Database per Service: Microservices with bounded contexts
- Shared Database Anti-pattern: Legacy system integration challenges
- Event Sourcing: Immutable event logs with projections
- CQRS: Command Query Responsibility Segregation
Technical Implementation
1. Entity-Relationship Modeling with Mermaid
1.1 ER Diagram Modeling Standards
Mermaid class diagrams provide a clear, text-based way to document entity relationships before implementation. Use the following conventions:
classDiagram
class Entity {
field: type <<中文描述>>
required() ["field1", "field2"]
unique() ["field1"]
enum_field() ["value1", "value2"]
display_field() "fieldName"
}
Core Modeling Rules:
-
Type Mapping:
Business Field Type MySQL Mapping Text string VARCHAR/TEXT Number number INT/BIGINT/DECIMAL Boolean boolean BOOLEAN/TINYINT Enumeration enum ENUM type Email email VARCHAR(255) with validation DateTime datetime DATETIME Array type[] Association table or JSON
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 338 lines · 36 tokens per session scan A e5a99247202d
database-architect is an agent published in the GitHub repository chenlinyang/mcp-server-mysql (22 stars, last pushed 11mo ago), licensed MIT. It adds 36 tokens to every session and 2,882 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
report-generator
Use for generating formatted reports from database queries, creating data summaries, building dashboards, and exporting analysis results in various formats.
qwery-agent
Expert database query agent with schema awareness. Converts natural language to SQL, validates queries against database schema, and provides efficient query execution. Supports schema versioning and time-travel queries. Uses GFS branching for safe destructive operations.
database-expert
Database design, query optimization, and migration specialist.
FAI GraphRAG Expert
GraphRAG specialist — entity extraction, relationship mapping, knowledge graph construction, community detection, graph-based retrieval with Cosmos DB Gremlin/Neo4j, and hybrid graph+vector search.
FAI Document Intelligence Builder
Document Intelligence builder — Azure AI Document Intelligence OCR, multi-format processing, GPT-4o field extraction, Cosmos DB storage, and validation pipeline.
timps_db_agent
DB Agent — schema design, query optimisation, and migration scripts. Given a description of your data model it produces: CREATE TABLE SQL, an ER diagram in Mermaid, index recommendations, a migration script (Alembic / Flyway / raw SQL), and query templates. Pairs with the sqlinjection LoRA for secure query generation.…