ontology-mcp
登录查询代理 — Ontology MCP 与知识图谱
一个概念验证(POC),使用 OWL/SHACL/SKOS 知识图谱 + 两个 MCP 服务器, 将登录诊断查询路由到 SQL Server 和 MongoDB,并支持条件性 New Relic 升级。
架构概览
User prompt (VS Code Copilot)
│
▼ LLM classifies category natively — no tool call
│
ontology-mcp ──► Fuseki KG (SPARQL)
│ get_diagnosis_plan(category)
│ returns: capability_id, required_entities,
│ validation_sequence, newrelic_tool
▼
data-mcp ──► SQL Server (UM_Users, UM_UserPartnermapping,
│ UM_UserMobileNumberVerified)
├──────► MongoDB (users collection — 9 projected fields)
├──────► SHACL Validator (shapes read from KG shacl graph, evaluated in sequence order)
└──────► New Relic (only when all_shapes_pass=true — 2-step NRQL)Related MCP server: openclaw-brain
服务概览
服务 | 类型 | 启动方 | 用途 |
Apache Jena Fuseki | 本地进程 | 您(手动) | ontology-mcp 知识图谱查询 |
| stdio 子进程 | VS Code 自动生成 | 诊断规划 |
| stdio 子进程 | VS Code 自动生成 | 数据库查询 + 验证 |
SQL Server | 远程/LocalDB | 已在运行 | 数据查询 |
MongoDB | 远程服务器 | 已在运行 | 数据查询 |
New Relic | 云服务 | 始终可用 | 升级(所有形状均通过) |
只有 Fuseki 需要手动启动。两个 MCP 服务器均由 VS Code 自动生成。
前置条件
1. Java 11+
java -version2. Apache Jena Fuseki JAR
该 JAR 已从 git 中排除(54 MB)。请从 jena.apache.org 下载并放置于:
infra/fuseki/fuseki-server.jar3. Python 3.12+
python --version4. Python 依赖
cd c:\Ontology
python -m pip install -r requirements.txt5. SQL Server 的 ODBC 驱动程序
如果尚未安装,请从 Microsoft 下载 ODBC Driver 17 或 18 for SQL Server。
6. 安装了 GitHub Copilot(Agent 模式)的 VS Code
VS Code 1.99+ 并安装 GitHub Copilot 扩展。
本地启动分步指南
步骤 1 — 启动 Fuseki
cd c:\Ontology
java -jar infra\fuseki\fuseki-server.jar --config infra\fuseki\config\login-kg.ttl保持此终端窗口打开。访问 http://localhost:3030 进行验证。
步骤 2 — 加载知识图谱
首次运行或任何 schema/工件变更后必需。
$env:PYTHONIOENCODING = "utf-8"
python scripts/generate/generate.py --schema login --version 1.0.0
python scripts/kg/load_kg.py --schema login --version 1.0.0
python scripts/kg/promote.py --schema login --version 1.0.0步骤 3 — 配置密钥
将 .env.example 复制为 .env 并填写您的值:
SQL_SERVER_HOST=your-server
SQL_SERVER_DATABASE=your-database
SQL_SERVER_TRUSTED_CONNECTION=yes
SQL_SERVER_ENCRYPT=yes
SQL_SERVER_TRUST_CERT=yes
MONGODB_URI=mongodb://your-host:27017
MONGODB_DATABASE=your-database
NEW_RELIC_API_KEY=NRAK-xxxxxxxxxxxxxxxxxxxx
NEW_RELIC_ACCOUNT_ID=your-account-id
NEW_RELIC_REGION=US
APP_ENV=prod步骤 4 — 注册两个 MCP 服务器
在工作区根目录创建 .vscode/mcp.json:
{
"servers": {
"ontology-mcp": {
"type": "stdio",
"command": "python",
"args": ["-m", "mcp_server.server"],
"cwd": "c:\\Ontology",
"env": {
"PYTHONPATH": "c:\\Ontology\\src",
"PYTHONIOENCODING": "utf-8"
}
},
"data-mcp": {
"type": "stdio",
"command": "python",
"args": ["-m", "mcp_server.diagnostic_server"],
"cwd": "c:\\Ontology",
"env": {
"PYTHONPATH": "c:\\Ontology\\src",
"PYTHONIOENCODING": "utf-8"
}
}
}
}重新加载 VS Code(Ctrl+Shift+P → Developer: Reload Window)。
完整诊断流程
User: "testgdpr1235@gep.com can't reset password"
│
│ LLM classifies: category = "password_reset" (no tool call)
│
▼
① ontology-mcp / get_diagnosis_plan(category="password_reset")
Reads x_capability_registry from login.yaml (no Fuseki needed for this step)
Returns: capability_id, required_entities, validation_sequence, newrelic_tool
│
▼ (agent extracts username from user message; asks if missing)
│
② data-mcp / query_sql_user(username, capability_id)
SELECT from UM_Users → islocked, isactive, isdeleted, usertype, emailaddress, ...
│
③ data-mcp / query_sql_mobile_verification(username, capability_id)
SELECT from UM_UserMobileNumberVerified → ismobilenumberverified
│
④ data-mcp / query_sql_partner_mappings(username, capability_id)
SELECT from UM_UserPartnermapping → bpc, partnercode, isactive, contactcode
│
⑤ data-mcp / query_mongo_user(username, capability_id)
db.users.find_one({...}, { 9 diagnostic fields }) → MongoDB document
│
⑥ data-mcp / validate_login_shapes(username, capability_id, validation_sequence)
Runs only the shapes in validation_sequence (plan-scoped)
Returns: per-shape PASS/FAIL, all_shapes_pass, advisories (e.g. dr_012)
│
┌────┴──────────────────────────┐
violations found all_shapes_pass = true
│ │
report per shape ⑦a data-mcp / query_newrelic_login_mfa(username, capability_id)
with mapped rule OR
dr_003..dr_008 ⑦b data-mcp / query_newrelic_reset_password(username, capability_id)
→ Transaction → Log per traceId (max 7 days)仅获取
required_entities中列出的实体。对于不需要这些步骤的类别, 步骤②–⑤会被跳过(例如account_locked跳过合作伙伴和移动端查询)。
MCP 工具参考
ontology-mcp — 知识图谱规划工具(3 个工具)
工具 | 步骤 | 输入 | 返回 |
| 0 — 强制首次调用 |
|
|
| 仅回退 |
| 全部 8 个类别,包含 |
| 按需 |
| 来自 KG 描述符图的完整列/字段映射 |
get_diagnosis_plan直接从login.yaml读取能力注册表 — 无需调用 Fuseki。get_entity_descriptor查询 Fuseki 描述符图 — 需要 Fuseki 正在运行。
data-mcp — 实时数据工具(7 个工具)
全部 7 个工具都需要来自 get_diagnosis_plan 的 capability_id。未携带该参数调用将返回结构化错误。
工具 | 步骤 | 来源 | 返回 |
| 1a |
| userid, username, emailaddress, usertype, authenticationtype, islocked, isactive, isdeleted, issystemuser, mobileno |
| 1b |
| ismobilenumberverified + 已执行的 SQL |
| 1c |
| 所有映射行、总计数、活跃计数 |
| 1d |
| 9 个投影字段 + 已执行的查询 |
| 2 | SQL + MongoDB | 每个形状的 PASS/FAIL、 |
| 3a | New Relic NerdGraph |
|
| 3b | New Relic NerdGraph | 3 个重置 URI 的事务 + 日志(dr_011) |
诊断类别(8 个)
类别 | 触发条件 |
| 无法登录 / 认证 / 访问应用,SSO 失败,凭据被拒绝 |
| 未收到重置链接或忘记密码邮件 |
| 重置期间未收到 OTP 邮件 |
| 未收到短信 OTP(手机已验证) |
| 账户已停用 / 非活跃 / 已暂停 / 已禁用 |
| 多次尝试失败后账户被锁定 |
| 缺少 / 非活跃的合作伙伴(BPC)映射 |
| SQL 与 MongoDB 字段不匹配 |
SHACL 形状(8 个,按顺序求值)
# | 形状 | 条件 | 规则 |
1 |
| isLocked=1 或 isActive=0 或 isDeleted=1 | dr_003 |
2 |
| isSystemUser=1 | dr_005 |
3 |
| userType=Buyer 且 authenticationType=SSO | dr_006 |
4 |
| 无活跃的合作伙伴映射行 | dr_004 |
5 |
| 供应商无活跃的非零 BPC | dr_007 |
6 |
| 无有效的已注册电子邮件地址(重置/OTP 流程) | — |
7 |
| SQL 与 MongoDB 的 isMobileNumberVerified 不匹配 | dr_002 |
8 |
| SQL 与 MongoDB 的合作伙伴映射字段不匹配 | dr_008 |
每个类别的
validation_sequence仅运行这些形状的相关子集。advisories(例如dr_012电子邮件不匹配)与形状一起返回,但不影响all_shapes_pass。
New Relic 查询结构(两步)
Step 1: Transaction table (max 7 days lookback, filtered by APP_ENV)
/Account/Login → LoginUserName, traceId, RequiresTwoFactor, TwoFactorDetails
/Account/RecoverPassword → traceId, errorMessage, RecoveryUserName, RecoveryEmail
/Account/PreResetPassword → traceId, errorMessage, PreResetUserName
/Account/ResetPassword → LoginUserName, traceId, errorMessage
Step 2: Log table (per traceId from Step 1)
SELECT * FROM Log WHERE `trace.id` = '{traceId}' SINCE {transaction_timestamp}知识图谱 — 命名图
知识图谱按版本存储 6 个命名图 + 1 个元图:
命名图 IRI | 内容 | 被查询方 |
| 诊断剧本 — 8 个类别、必需实体、验证序列 |
|
| 实体列/字段映射 |
|
| 决策规则(dr_001..dr_012) |
|
| SHACL 节点形状 + 约束 |
|
| OWL 类 + 属性 | 可供检查 |
| SKOS 概念方案 + 标签 | 可供检查 |
| 活跃版本指针 | 每次 Fuseki 查询(图发现) |
每次诊断在两个阶段查询 Fuseki:
get_diagnosis_plan(步骤 0)—get_active_graphs(元图)+get_capability_plan(能力图)→ 完整诊断剧本validate_login_shapes(步骤 2)— 读取 shacl 图(形状)、descriptors 图(用于物化的字段/类型映射)和 rules 图(形状→规则)— 验证器由 KG 驱动
回退机制(每次记录警告):如果 Fuseki 不可达,get_diagnosis_plan 从 login.yaml 读取 x_capability_registry,validate_login_shapes 回退到程序化的 shacl_validator.py。
工件重新生成
当任何 YAML schema 文件变更时:
$env:PYTHONIOENCODING = "utf-8"
python scripts/generate/generate.py --schema login --version 1.0.0
python scripts/kg/load_kg.py --schema login --version 1.0.0
python scripts/kg/promote.py --schema login --version 1.0.0项目结构
c:\Ontology\
├── src/
│ └── mcp_server/ # PYTHONPATH=c:\Ontology\src
│ ├── server.py # ontology-mcp entrypoint (KG planning tools)
│ ├── diagnostic_server.py # data-mcp entrypoint (DB/NR tools)
│ ├── tool_meta.py # loads config/tool_descriptions.yaml
│ ├── connectors/
│ │ ├── sql_connector.py # pyodbc — UM_Users, UM_UserPartnermapping, ...
│ │ ├── mongo_connector.py # pymongo — users collection (projected)
│ │ └── newrelic_connector.py # NerdGraph GraphQL — 2-step NRQL
│ ├── diagnostics/
│ │ ├── data_fetcher.py # orchestrates SQL + MongoDB fetch
│ │ ├── kg_shacl_validator.py # KG-driven SHACL interpreter (PRIMARY)
│ │ └── shacl_validator.py # programmatic evaluation (Fuseki-down fallback)
│ ├── tools/
│ │ ├── get_diagnosis_plan.py # ontology-mcp: reads x_capability_registry
│ │ ├── list_capabilities.py # ontology-mcp: lists all 8 categories
│ │ ├── get_descriptor.py # ontology-mcp: SPARQL descriptors graph
│ │ ├── fetch_user_data.py # data-mcp: 4 individual SQL/Mongo queries
│ │ ├── validate_shapes.py # data-mcp: shape evaluation + advisories
│ │ └── query_newrelic.py # data-mcp: NR login + reset handlers
│ ├── kg/
│ │ └── sparql_client.py # Fuseki HTTP client + graph discovery
│ └── registry/
│ └── schema_registry.py # registry.yaml + load_capability_registry()
│
├── ontology/
│ ├── schemas/
│ │ ├── registry.yaml
│ │ └── login/v1.0.0/
│ │ ├── login.yaml # root: x_capability_registry + x_shacl_rules + x_decision_rules
│ │ ├── shared/types.yaml
│ │ ├── shared/enums.yaml # AuthenticationTypeEnum, UserTypeEnum
│ │ ├── shared/subsets.yaml
│ │ └── entities/
│ │ ├── abstract_user.yaml
│ │ ├── user.yaml # SQL UM_Users
│ │ ├── partner_mapping.yaml # SQL UM_UserPartnermapping
│ │ ├── mobile_verification.yaml # SQL UM_UserMobileNumberVerified
│ │ └── user_document.yaml # MongoDB users collection
│ └── sparql/
│ ├── get_entity_descriptor.sparql
│ └── get_decision_rules.sparql
│
├── artifacts/login/v1.0.0/
│ ├── owl/login.owl.ttl
│ ├── shacl/login.shacl.ttl
│ ├── skos/login.skos.ttl
│ ├── rules/login.rules.ttl
│ ├── descriptors/login.descriptors.json
│ └── jsonld/login.context.jsonld + login.agent_template.json
│
├── scripts/
│ ├── generate/generate.py + gen_*.py + _yaml_loader.py
│ └── kg/load_kg.py + promote.py
│
├── config/
│ └── tool_descriptions.yaml # single source of truth for all MCP tool descriptions
│
├── infra/fuseki/
│ ├── fuseki-server.jar # not committed — download separately
│ ├── config/login-kg.ttl
│ └── data/ # TDB2 storage — gitignored
│
├── .github/copilot-instructions.md # Copilot workspace instructions (auto-loaded)
├── CLAUDE.md # Claude Code workspace instructions (auto-loaded)
├── .vscode/mcp.json # MCP server registration (2 servers)
├── .env / .env.example # secrets — .env never committed to git
└── requirements.txt故障排查
Error | Cause | Fix |
| Fuseki 未运行 | 启动 Fuseki(步骤 1) |
| Agent 跳过了 | 重新开始对话; |
|
| 检查 |
|
| 确认 |
SQL Server 连接错误 |
| 检查 |
| 缺少依赖 |
|
| Windows 控制台编码 | 添加 |
Fuseki 图为空 | 重启后 Fuseki 全新启动 | 运行 |
每日工作流
# 1. Start Fuseki
java -jar infra\fuseki\fuseki-server.jar --config infra\fuseki\config\login-kg.ttl
# 2. Load KG (only after schema or artifact changes)
$env:PYTHONIOENCODING = "utf-8"
python scripts/kg/load_kg.py --schema login --version 1.0.0
python scripts/kg/promote.py --schema login --version 1.0.0
# 3. Open VS Code — both MCP servers start automatically扩展 Schema
添加新实体(新的 SQL 表或 MongoDB 集合)
创建
ontology/schemas/login/v1.0.0/entities/new_entity.yaml将
- entities/new_entity添加到login.yaml的 imports 中运行 generate + load + promote
添加或修改诊断类别
编辑
login.yaml中的x_capability_registry在
login.yaml的x_shacl_rules中添加/更新匹配的 shape——KG 驱动的验证器从shacl图中读取它;无需编辑 Python 即可处理sh_in/sh_property/sparql/cross_sourceshapes运行 generate + load + promote(使新的 shape/rule 进入 KG)
重启 MCP 服务器
添加或修改 SHACL shape
Shapes 从 KG 执行,而非代码。编辑 login.yaml 中的 x_shacl_rules,然后重新生成并重新加载。kg_shacl_validator.py(通用引擎)无需更改,除非你引入全新的约束类型。
添加新的 schema 版本
复制
ontology/schemas/login/v1.0.0/→v1.1.0/编辑
v1.1.0/中的实体文件为
v1.1.0运行 generate + load + promote
两个版本在 KG 中共存——始终可以通过 promote.py 回滚。
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