Data Nexus MCP
Data Nexus MCP
모듈형이며 안전한 플랫폼으로, REST API, MCP(Model Context Protocol) 및 Vue.js 웹 UI를 통해 SQL 및 NoSQL 데이터베이스에 연결할 수 있습니다.
아키텍처
┌─────────────┐ ┌──────────────┐ ┌─────────────────┐
│ Web UI │────▶│ REST API │────▶│ core-db-command │
│ (Vue.js) │ │ (FastAPI) │ │ (Python lib) │
└─────────────┘ └──────┬───────┘ └────────┬────────┘
│ │
┌──────▼───────┐ │
│ MCP Server │───────────────┘
└──────┬───────┘
│
┌──────▼───────┐
│ MCP Client │──▶ AI Agents / LLMs
└──────────────┘모듈
모듈 | 패키지 | 설명 |
core-db-command |
| 모든 데이터베이스 드라이버를 위한 플러그인 기반 Python 라이브러리 |
rest-api-command |
| OAuth2/LDAP/기본 인증을 포함한 FastAPI REST 계층 |
mcp-server |
| MCP 도구: |
mcp-client |
| 에이전트 통합을 위한 MCP 클라이언트 브리지 |
web-ui |
| Vue 3 + Pinia + Monaco Editor 쿼리 스튜디오 |
config |
|
|
Related MCP server: Database MCP Server
지원되는 데이터베이스 유형
드라이버 레지스트리는 13개 카테고리에서 50개 이상의 데이터베이스 유형을 지원합니다:
관계형: PostgreSQL, MySQL, MSSQL, Oracle, CockroachDB, TiDB, YugabyteDB, TimescaleDB, pgvector
문서: MongoDB, DocumentDB, Firestore, Couchbase (스텁)
키-값: Redis, DynamoDB, Memcached/etcd/RocksDB (스텁)
와이드 컬럼: Cassandra, ScyllaDB, Bigtable/HBase (스텁)
그래프: Neo4j, Neptune/JanusGraph/ArangoDB (스텁)
시계열: InfluxDB, ClickHouse, Prometheus/QuestDB (스텁)
벡터: Qdrant, Weaviate, Milvus, Pinecone
검색: Elasticsearch, OpenSearch, Splunk/Solr (스텁)
웨어하우스: BigQuery, Snowflake, Redshift/Databricks (스텁)
멀티 모델: Cosmos DB, OrientDB (스텁)
임베디드: SQLite, DuckDB, Realm/LMDB (스텁)
원장: QLDB/BigchainDB (스텁)
NewSQL: Spanner (스텁)
완전히 구현된 드라이버에는 PostgreSQL, MySQL, MSSQL, Oracle, MongoDB, Redis, SQLite, DuckDB, Elasticsearch, ClickHouse, Neo4j, InfluxDB, Cassandra, DynamoDB, BigQuery, Snowflake, Qdrant, Weaviate, Milvus, Pinecone, Cosmos DB, Firestore가 포함됩니다. 스텁 드라이버는 등록되어 있으며 확장 가능합니다.
빠른 시작
사전 요구 사항
Python 3.11+
Node.js 20+ (웹 UI 개발용)
Docker 및 Docker Compose (선택 사항)
1. Python 종속성 설치
cp .env.example .env
pip install -e ".[dev]"2. 연결 구성
config/connections.yaml을 편집하고 환경 변수를 통해 비밀 정보를 설정합니다:
connections:
- name: postgres_prod
type: postgresql
host: localhost
port: 5432
database: mydb
user: readonly_user
password: ${PG_PASSWORD}3. REST API 시작
db-rest-api
# or: uvicorn rest_api_command.app:app --reloadAPI 문서: http://localhost:8000/docs
4. 웹 UI 시작 (개발 모드)
cd web-ui
cp .env.example .env
npm install
npm run devhttp://localhost:5173 열기 — 기본 자격 증명: admin / changeme
5. Docker Compose로 실행
docker compose up -d서비스:
REST API: http://localhost:8000
웹 UI: http://localhost:5173
PostgreSQL, MySQL, MongoDB, Redis, Elasticsearch
REST API 엔드포인트
메서드 | 경로 | 설명 |
GET |
| 연결 목록 (자격 증명 없음) |
POST |
| 매개변수화된 쿼리 |
POST |
| 원시 SQL / 네이티브 명령 |
GET |
| 데이터베이스 스키마 |
GET |
| 테이블/컬렉션 목록 |
GET |
| 테이블 구조 |
GET/POST |
| 쿼리 기록 |
MCP 서버
.env 또는 Cursor MCP env에서 구성:
MCP_REST_API_URL=http://localhost:8000
# Option A: bearer token (when REST_API_AUTH_MODE=oauth2)
MCP_REST_API_TOKEN=<jwt-from-/api/auth/token>
# Option B: username/password (works with basic auth; auto-fetches JWT if oauth2)
MCP_REST_API_USER=admin
MCP_REST_API_PASSWORD=changeme실행:
db-mcp-serverCursor/Claude MCP 구성에 추가:
{
"mcpServers": {
"data-nexus-mcp": {
"command": "db-mcp-server",
"cwd": "/path/to/data_nexus_mcp",
"env": {
"MCP_REST_API_URL": "http://localhost:8000",
"MCP_REST_API_USER": "admin",
"MCP_REST_API_PASSWORD": "changeme"
}
}
}
}참고: REST_API_* 변수는 REST API 프로세스(db-rest-api)에 속하며 MCP 서버 구성에는 포함되지 않습니다.
MCP 클라이언트
db-mcp-client # list available tools
db-mcp-client query local_sqlite "SELECT 1"인증
REST_API_AUTH_MODE를 다음 중 하나로 설정합니다:
basic— HTTP 기본 인증 (개발 기본값)oauth2—/api/auth/token을 통한 JWT 베어러 토큰ldap— LDAP 바인드 (REST_API_LDAP_SERVER및REST_API_LDAP_BASE_DN필요)
새 드라이버 추가
core_db_command/drivers/mydb.py생성BaseDriver를 서브클래싱하고driver_type설정@DriverRegistry.register로 데코레이트core_db_command/drivers/registry_loader.py에서 가져오기
from core_db_command.base import BaseDriver, DriverRegistry
@DriverRegistry.register
class MyDBDriver(BaseDriver):
driver_type = "mydb"
async def connect(self): ...
async def disconnect(self): ...
async def query(self, query, params=None): ...
async def execute(self, command, params=None): ...
async def list_tables(self, schema=None): ...
async def describe_table(self, table, schema=None): ...테스트
pytest tests/ -v보안 참고 사항
자격 증명은 REST API 또는 MCP 서버에서 절대 반환되지 않음
비밀 정보는 YAML 구성에서
${ENV_VAR}플레이스홀더를 사용해야 함모든 API 엔드포인트는 인증이 필요함
쿼리 입력은 유효성 검사 및 길이 제한이 적용됨
프로젝트 구조
data-nexus-mcp/
├── core_db_command/ # Core library + drivers
├── rest_api_command/ # FastAPI REST API
├── mcp_server/ # MCP server
├── mcp_client/ # MCP client
├── web-ui/ # Vue.js frontend
├── config/ # YAML connection config
├── tests/ # Unit tests
├── docker-compose.yml
├── Dockerfile
└── pyproject.toml라이선스
MIT
Available Tools
5 toolsdescribe_tableA
Describe the structure of a specific table or collection.
| Name | Required | Description | Default |
|---|---|---|---|
| table | Yes | ||
| schema | No | ||
| connection_name | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description's verb 'describe' implies a read-only, non-destructive operation, which is basic transparency. However, it doesn't disclose any additional behaviors (e.g., permission requirements, response shape, or limitations) beyond that.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that is direct and front-loaded with the core action and target. Every word earns its place, with zero waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple and has an output schema to document return values, but the description lacks context about relationships to sibling tools and does not mention the need for connection_name or that schema is optional. It is complete for a trivial description but not for a comprehensive agent-facing tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description adds no meaning to the parameters. The schema titles (table, schema, connection_name) are self-explanatory, but the description doesn't explain their roles or any requirements beyond the schema's 'required' fields.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Describe') and resource ('structure of a specific table or collection'), clearly distinguishing it from siblings like query_database and execute_sql, which run queries. It precisely communicates what the tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you need the structure of one specific table, but does not explicitly mention when to avoid it or compare with get_schema or list_tables. No exclusions or alternatives are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
execute_sqlB
Execute raw SQL or native database command on a named connection.
| Name | Required | Description | Default |
|---|---|---|---|
| params | No | ||
| command | Yes | ||
| connection_name | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the full burden of behavioral disclosure. It mentions 'raw SQL or native database command', implying potential write operations and risks, but fails to explicitly state that commands can modify data, require admin privileges, or that results may vary by connection type. The output schema exists but is not referenced.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence of 11 words, making it very concise. It includes the essential elements (execute, raw SQL, named connection) without fluff. However, the brevity leaves out important contextual details that could be added without significant lengthening.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (3 parameters, no annotations, 0% schema coverage, output schema present), the description is incomplete. It omits the role of 'params', fails to explain what 'native command' means, and does not address security or error behavior. The output schema exists but is not mentioned, so the agent cannot infer what will be returned.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It defines 'connection_name' and 'command' but provides no additional meaning beyond the schema fields. The 'params' parameter is an optional object described only as 'anyOf object or null', with no explanation of its role (e.g., parameterized queries). The description adds minimal value over the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool executes raw SQL or native database commands on a named connection. This distinguishes it from siblings like 'query_database' and 'describe_table' by emphasizing raw execution and the need for a named connection.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not specify when to use this tool versus siblings like 'query_database' or 'list_tables'. It lacks guidance on prerequisites (e.g., connection setup, permissions) and when not to use it (e.g., for read-only queries or exploratory analysis).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_schemaC
Retrieve schema information for a named database connection.
| Name | Required | Description | Default |
|---|---|---|---|
| connection_name | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden of behavioral disclosure. It does not mention whether the operation is read-only, requires permissions, or what happens if the connection_name is invalid. The minimal phrasing 'Retrieve schema information' provides no insight into side effects, errors, or performance implications.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is front-loaded and contains no unnecessary words. It is concise and easy to parse. However, it could be slightly more informative without losing conciseness, such as noting the output schema or the nature of the schema information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema, the description does not need to explain return values, but it still lacks context about how to use the connection_name parameter, what the tool returns (beyond schema info), and how it relates to sibling tools. The presence of siblings like 'list_tables' and 'describe_table' makes the lack of differentiation a significant gap. The description is too minimal to be fully actionable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has a single parameter 'connection_name' with type string, but the description adds no meaning beyond the schema. With 0% schema description coverage, the description should explain what constitutes a valid connection name or how to obtain it. It does not, so it provides no added value for parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves schema information for a named database connection. It uses a specific verb-resource pair ('Retrieve schema information') and implies a scope of a connection, which distinguishes it from sibling tools like 'describe_table' that focus on individual tables. However, it does not specify what 'schema information' includes (e.g., tables, columns, types), which slightly reduces precision.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 'list_tables' or 'describe_table'. There are no prerequisites, exclusions, or context about valid connection names. The description lacks any advisory information to help an agent decide between siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_tablesC
List all tables or collections in a named database connection.
| Name | Required | Description | Default |
|---|---|---|---|
| schema | No | ||
| connection_name | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
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 states the tool 'list[s]' tables, implying a read-only operation, but does not explicitly confirm non-destructiveness, error conditions (e.g., invalid connection_name), rate limits, or behavior when 'schema' is null vs. specified. These are important gaps for a database connection tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no wasted words. However, it is so brief that it sacrifices information needed for tool selection and correct invocation. Slightly more detail (e.g., about the schema parameter) could be added without sacrificing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema (which helps with return values), the description lacks essential context about input semantics (especially the schema parameter), error handling, and behavioral guarantees. For a tool with 0% schema description coverage and no annotations, this description is insufficiently complete for correct agent invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning the description must compensate entirely. While 'connection_name' is mentioned in the description ('named database connection'), the 'schema' parameter (which is optional and can be null) is not explained at all. The description does not clarify how 'schema' affects the listing (e.g., filter by schema, ignored if null). This is a significant gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('List') and resource ('tables or collections') and context ('in a named database connection'), clearly distinguishing this tool from siblings like 'describe_table' (which describes a single table) and 'get_schema' (which retrieves schema-level metadata). It leaves no ambiguity about what this tool returns.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal usage guidance. It does not specify when to use this tool versus 'get_schema' or 'describe_table', nor does it mention any prerequisites (e.g., connection must exist). It lacks exclusion criteria or alternative suggestions, relying on the user/agent to infer context from sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
query_databaseD
Run a parameterized query on a named database connection.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| params | No | ||
| connection_name | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full burden for behavioral disclosure. It fails to indicate whether the tool is read-only, whether it modifies data, what permissions are needed, or any side effects. The agent cannot determine safety or appropriateness from the description alone.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (one sentence), but this brevity comes at the cost of completeness. It is under-specified rather than concise. Every sentence should earn its place; here, it leaves critical gaps.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema (not shown), the description is incomplete. It does not explain return values, error handling, connection requirements, or the role of 'params'. For a tool with 3 parameters and zero annotations, much more context is needed to be minimally viable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning the schema provides no parameter descriptions. The description adds no value: it does not explain the purpose of 'query', 'params', or 'connection_name', nor how to structure the 'params' object. The agent is left guessing parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Run a parameterized query on a named database connection' clearly states the verb and resource, but it does not differentiate itself from the sibling tool 'execute_sql', which likely performs a similar function. The term 'parameterized query' hints at safety but is not explicit enough to distinguish usage.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 siblings like 'get_schema' or 'execute_sql'. There is no mention of prerequisites, when-not-to-use, or alternatives. The agent must rely on the tool name alone to infer context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
5 tool updates
v0.1.0- First observed
describe_table - First observed
execute_sql - First observed
get_schema - First observed
list_tables - First observed
query_database
TDQS
Scored across 5 tools
Tools are mostly distinct: query_database and execute_sql both run queries but one is parameterized and the other raw; get_schema, list_tables, describe_table each target different metadata. Minor potential for confusion between the two query tools, but descriptions clarify the difference.
All tool names follow a consistent verb_noun pattern with lowercase and underscores: query_database, get_schema, list_tables, describe_table, execute_sql. No mixed conventions or irregularities.
5 tools are well-scoped for a database MCP server. The set covers core operations—querying, schema retrieval, table listing, structure description, raw execution—without being overwhelming or too sparse.
The tool surface covers essential query and schema exploration operations. The execute_sql tool can handle DDL, but there are no dedicated create/alter/drop tools; transaction commands are also missing. Overall, it's nearly complete for a typical read-heavy interaction pattern.
Maintenance
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