openapi-mcp-builder

openapi-mcp-builder

Enables users to convert any OpenAPI or Swagger spec URL into a hosted MCP server on the Agentic Tools Platform, with tools for analyzing, trimming, and managing OpenAPI specifications.

Category
访问服务器

README

openapi-mcp-builder

An MCP server that turns any OpenAPI (or Swagger) spec URL into a hosted MCP server on the Agentic Tools Platform, and hands the caller back the ready-to-use MCP gateway URL.

Under the hood it drives the platform's experimental /v1/openapi-servers/* endpoints:

Tool Endpoint / behavior
analyze_openapi_spec_url Local: GET spec URL, summarize operations by tag
pick_openapi_endpoints Local: list all operations + MCP App UI (inline picker in supporting clients)
search_openapi_operations Local: keyword search over paths/tags/opIds
validate_openapi_tool_filter Local: unknown keys, regex check for paths
export_trimmed_openapi_spec Shrink (operation keys, tags, path, prefix, related)
reupload_openapi_spec_text Reupload spec body without a public URL
build_tool_filter_for_tags Build {"include_tags":[...]} for tool_filter
create_mcp_from_openapi_url POST /v1/openapi-servers + SAS PUT + poll
list_openapi_mcp_servers GET /v1/openapi-servers
get_openapi_mcp_server GET /v1/openapi-servers/{id}
update_openapi_mcp_server PATCH /v1/openapi-servers/{id}
delete_openapi_mcp_server DELETE /v1/openapi-servers/{id}
refresh_openapi_mcp_server POST /v1/openapi-servers/{id}/refresh
list_openapi_mcp_server_tools GET /v1/openapi-servers/{id}/tools
reupload_openapi_spec_from_url PATCH ?reupload=true + SAS PUT + poll

How the spec-URL workflow works

user -> MCP client (e.g. Agent Studio)
              |   tool call: create_mcp_from_openapi_url(spec_url, name, ...)
              v
   openapi-mcp-builder
     1. GET  spec_url                         # validate JSON / YAML
     2. POST /v1/openapi-servers              # metadata only -> spec_upload_url
     3. PUT  <spec_upload_url>                # Azure Blob SAS, x-ms-blob-type: BlockBlob
     4. GET  /v1/openapi-servers/{id}  (poll) # until parse_status terminal
     5. return { gateway_url, tool_count, parse_status, ... }

The gateway_url in the final response is the MCP URL the agent connects to.

Large specs (operation limits)

The executor enforces a maximum number of OpenAPI operations per server (e.g. 50). A tool_filter alone may not help if the platform still counts all operations in the uploaded file before the filter is applied. In that case you must use a physically smaller spec (fewer path operations in the document):

  1. Run export_trimmed_openapi_spec on the spec_url with include_operation_keys (exact GET /path list), include_tags, and/or path_substrings (literal substrings in the path, e.g. dailyLog for ProjectSight). This returns a trimmed OpenAPI JSON string and trimmed_operation_count.
  2. Call reupload_openapi_spec_text with that JSON as spec_text on the existing server (no Gist or extra hosting required).

For filters that the platform does apply to the live parse, use tool_filter (include_tags, include_paths as regex such as .*[Dd]ailyLog.*not glob patterns like *daily*, which are invalid regex). Use analyze_openapi_spec_url per-tag counts and set PLATFORM_MAX_OPENAPI_OPERATIONS / MAX_TRIMMED_SPEC_EXPORT_BYTES in .env for hints and export size.

Optional: set CREATE_PREFLIGHT_ENFORCE=true so create_mcp_from_openapi_url stops before register when the downloaded spec is over the operation cap and you did not pass tool_filter or acknowledge_openapi_operation_limit=true (see .env.example).

For agent authors (Studio / Cursor)

  1. Always run analyze_openapi_spec_url first. If exceeds_platform_limit is true, do not call create_mcp_from_openapi_url until you have a plan.
  2. tool_filter controls which operations become MCP tools; it may not reduce the operation count the executor sees in the uploaded file. To pass a hard cap, use export_trimmed_openapi_spec (smaller document) + reupload_openapi_spec_text.
  3. Use search_openapi_operations to map a user phrase (e.g. “daily log”) to real paths and tags, or call pick_openapi_endpoints with the same spec_url when the client supports MCP Apps (inline UI; see MCP App picker). Alternatively use the standalone endpoint picker (build and serve at /endpoint-picker/; see that README) to copy include_operation_keys for export_trimmed_openapi_spec. include_paths in tool_filter must be regex, not globs; run validate_openapi_tool_filter with strict=true when you need to fail on unknown keys or glob-like path patterns.
  4. analyze_openapi_spec_url reports external_ref_* when the document uses non-#/ $refs (file or URL). Bundle to a single file when possible. Invalid field names (e.g. path_pattern) are a common failure mode — see docs/PLATFORM.md for open questions to confirm with the platform team.

MCP App (inline endpoint picker)

The tool pick_openapi_endpoints is registered with FastMCP AppConfig pointing at a ui:// HTML resource. MCP clients that implement the MCP Apps / UI extension (for example recent VS Code or Claude builds) can render the picker inside the chat, receive the operation list from the tool result, and proxy export_trimmed_openapi_spec when the user runs export from the UI.

  • Build the bundle (writes into src/openapi_mcp_builder/static/endpoint_picker_mcp.html):

    cd apps/endpoint-picker-mcp
    npm install
    npm run build
    

    From the repo root you can also run npm run build:endpoint-picker-mcp (see root package.json).

  • Agent Studio must explicitly support MCP Apps and negotiate io.modelcontextprotocol/ui; if it does not, pick_openapi_endpoints still returns the same JSON as a normal tool (no iframe).

  • Manual check: connect this server in an MCP Apps–capable client, invoke pick_openapi_endpoints with a public spec_url, confirm the table appears, select operations, and run export from the UI.

Authentication (OAuth provider OBO)

When deployed inside Agent Studio, every tool call carries the signed-in user's on-behalf-of TID token as Authorization: Bearer <token>. openapi-mcp-builder extracts that header and forwards it to the Agentic AI Platform, so every action (create, list, patch, delete) runs as the end user — no static service credentials required.

Three modes are supported, evaluated per request:

  1. OBO passthrough — use the caller's Authorization header (production).
  2. Static tokenTOOLS_API_ACCESS_TOKEN env var (local dev / stdio).
  3. Client credentialsTOOLS_API_CLIENT_ID + TOOLS_API_CLIENT_SECRET mint a token against TOOLS_API_TOKEN_URL (service-to-service).

The generated OpenAPI MCP server itself defaults to auth_config.provider = "passthrough", so the upstream REST API receives the same end-user token at tool-invocation time unless you override it.

Install

python -m venv .venv
# Windows PowerShell
.venv\Scripts\Activate.ps1
# macOS / Linux
source .venv/bin/activate

pip install -e ".[dev]"
cp .env.example .env   # then edit

Requires Python 3.10+.

Run

Stdio (Claude Desktop, local MCP clients)

MCP_TRANSPORT=stdio openapi-mcp-builder
# or
MCP_TRANSPORT=stdio python -m openapi_mcp_builder

Example Claude Desktop / Cursor MCP config:

{
  "mcpServers": {
    "openapi-mcp-builder": {
      "command": "openapi-mcp-builder",
      "env": {
        "MCP_TRANSPORT": "stdio",
        "TOOLS_API_ENV": "dev",
        "TOOLS_API_ACCESS_TOKEN": "eyJhbGciOi..."
      }
    }
  }
}

HTTP (Agent Studio, remote MCP hosts) — default

openapi-mcp-builder   # listens on 0.0.0.0:8754 with MCP_TRANSPORT=http

Agent Studio should be configured to send Authorization: Bearer <OBO-token> on every MCP request. Leave TOOLS_API_ACCESS_TOKEN empty so the server requires the OBO header.

Environment selector

TOOLS_API_ENV picks the Tools API base URL:

TOOLS_API_ENV Base URL
dev (default) https://tools.dev.Build Flows-ai.com
stage https://tools.stage.Build Flows-ai.com
prod https://tools.ai.Build Flows.com

Set TOOLS_API_TOOLS_API_BASE_URL to override explicitly.

Example call

// tool: create_mcp_from_openapi_url
{
  "spec_url": "https://api.redocly.com/registry/bundle/hcss-64o/identity/v1/openapi.yaml?branch=main",
  "name": "hcss-identity",
  "description": "HCSS Identity API (get bearer tokens)",
  "tags": ["hcss", "identity"],
  "auth_provider": "passthrough"
}

Response:

{
  "ok": true,
  "id": "srv_01HQZ...",
  "name": "hcss-identity",
  "parse_status": "success",
  "tool_count": 3,
  "gateway_url": "https://tools.dev.Build Flows-ai.com/openapi/hcss-identity",
  "mcp_server_url": "https://tools.dev.Build Flows-ai.com/openapi/hcss-identity",
  "path": "/openapi/hcss-identity",
  "spec_bytes": 48321,
  "spec_content_type": "application/yaml",
  "waited_seconds": 4.12
}

Point your agent at mcp_server_url and you're done.

Project layout

openapi-mcp/
├── pyproject.toml
├── README.md
├── .env.example
├── src/openapi_mcp_builder/
│   ├── __init__.py
│   ├── __main__.py        # CLI entrypoint / transport selection
│   ├── server.py          # FastMCP tools
│   ├── workflow.py        # download -> register -> upload -> poll
│   ├── client.py          # Async HTTP client for the Tools API
│   ├── auth.py            # OBO passthrough + fallbacks
│   ├── config.py          # Pydantic Settings
│   ├── models.py          # Request / response schemas
│   ├── operation_key.py  # Canonical operation_key for trim
│   ├── spec_external_refs.py
│   ├── spec_inspect.py   # Per-tag / path operation summaries (tool_filter)
│   ├── spec_ref_prune.py # Prune components to $ref-closure after trim
│   ├── spec_trim.py      # Shrink paths in a spec for reupload
│   ├── static_resources.py  # MCP App HTML loader (ui:// endpoint picker)
│   ├── static/           # bundled endpoint_picker_mcp.html (built by Vite)
│   └── tool_filter_validate.py
├── docs/
│   └── PLATFORM.md     # Open questions for the platform (op cap vs filter)
├── apps/
│   ├── endpoint-picker/      # static UI: paste spec → include_operation_keys JSON
│   └── endpoint-picker-mcp/  # Vite MCP App bundle for inline picker (npm run build)
└── tests/
    ├── conftest.py
    ├── test_auth.py
    ├── test_spec_inspect.py
    ├── test_spec_external_refs.py
    ├── test_spec_ref_prune.py
    ├── test_spec_trim.py
    ├── test_tool_filter_validate.py
    ├── test_endpoint_picker.py  # pick_openapi_endpoints + MCP HTML smoke
    └── test_workflow.py   # respx-mocked end-to-end flow

Tests

pytest

License

MIT

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选