Universal API Connector MCP

Universal API Connector MCP

Enables AI agents to connect to any API by loading OpenAPI, GraphQL, gRPC, or SOAP specs, with a built-in catalog of 2500+ public APIs, supporting secure execution, response filtering, chaining, and parallel calls.

Category
访问服务器

README

<!-- mcp-name: io.github.TeodorMCP/universal-connector-mcp -->

<div align="center">

Universal API Connector MCP

Universal API Connector - Any API. One MCP server.

Any API. One MCP server.

Stop installing a new MCP server for every service: point this one at any OpenAPI/Swagger, GraphQL,<br> gRPC or SOAP spec - or pick one from the built-in catalog of 2500+ public APIs - and your AI agent<br> can call it. Securely, in fewer steps, with fewer tokens.

CI PyPI Python License: MIT

Add to Cursor

Installation · Meta-tools · API catalog · Examples · Configuration · Security

Live demo: the agent discovers, loads and calls an API in a single session

</div>

Why another one?

Most "universal API" MCP servers only speak REST/OpenAPI. This project is built around three differentiators:

  1. Truly universal - a normalized Operation model with pluggable spec adapters (OpenAPI, GraphQL, gRPC, SOAP). Tools and executors never care which protocol produced an operation.
  2. Security-first / local-first - a direct response to supply-chain attacks like the AgentBaiting / FakeGit campaign. Outbound requests are restricted to an allowlist, secrets never touch logs, responses are size-capped, and every call is audit-logged. No arbitrary code execution, no binary downloads, no telemetry.
  3. Built for context efficiency - field-level response filtering, chained and parallel execution, and response caching mean whole workflows fit in one tool call and responses stay small.

How it works

One connector, every protocol

The agent explores APIs like a filesystem instead of loading hundreds of tools at once (which would blow up the context window on large APIs like Stripe). It searches for operations, inspects the ones it needs, then executes them.

<details> <summary>Architecture (click to expand)</summary>

flowchart TD
    Agent["AI Agent"] -->|"MCP stdio"| Server["FastMCP Server"]
    Server --> Tools["Meta-tools"]
    Tools --> Registry["Operation Registry (normalized)"]
    Adapters["Spec Adapters"] --> Registry
    Tools --> Guard["Security Guard"]
    Guard --> Executor["Protocol Executors"]
    Executor --> Auth["Auth Manager"]
    Executor --> UpstreamAPI["Upstream API"]

</details>

Meta-tools

Tool Purpose
search_catalog Find ready-to-load public APIs (curated list + APIs.guru directory, 2500+ specs)
load_api Register an API from a spec URL/file (protocol auto-detected)
list_apis List loaded APIs
search_operations Fuzzy-search operations across loaded APIs
get_operation Full parameter/response schema for one operation
execute Call an operation (auth + security guard applied); extract returns only the fields you ask for
execute_chained Run a sequence of operations in one call, piping results between steps; nested lists run in parallel
execute_graph Run a dependency graph of operations; order is inferred from ${id.path} references and independent nodes run in parallel automatically
unload_api Remove a loaded API
audit_log Recent outbound calls (method, host, path, status)

Why fewer steps (and fewer tokens)

Fewer steps, fewer tokens

Where a per-API MCP server needs one tool round-trip per call - each returning a full JSON payload into the agent's context - this server collapses whole workflows:

  • extract - execute(..., extract=["items.*.name", "total_count"]) returns just those fields instead of a multi-kilobyte response. * fans out over arrays.
  • Chaining - execute_chained pipes step results into later params via ${save_as.path} references: one tool call instead of N.
  • Parallel groups - a nested list of steps runs concurrently, so "query three APIs and combine" is still one call:
execute_chained(steps=[
  [
    {"operation_id": "github.repos_get", "params": {"owner": "o", "repo": "r"},
     "save_as": "gh", "extract": ["stargazers_count"]},
    {"operation_id": "open_meteo.get_v1_forecast", "params": {"latitude": 52.5, "longitude": 13.4},
     "save_as": "weather", "extract": ["current_weather.temperature"]}
  ],
  {"operation_id": "github.issues_list_for_repo",
   "params": {"owner": "o", "repo": "r"}, "extract": ["*.title"]}
])
  • Response cache - successful GET/query results are cached for UCMCP_CACHE_TTL seconds (default 60), so repeated lookups are instant and free; pass fresh: true to bypass. Successful mutations invalidate that API's cached reads.

Built-in API catalog

You don't need to hunt for spec URLs. search_catalog searches two sources:

  1. A curated list of verified free/popular APIs: GitHub, GitLab, Stripe, OpenAI, Wikipedia, Open-Meteo (weather, no key), a countries GraphQL API, httpbin and the Swagger Petstore. Entries carry working spec URLs, base-URL overrides and auth hints (e.g. "optional GITHUB_TOKEN").
  2. The APIs.guru directory - 2500+ community-indexed OpenAPI specs (Google, AWS, Microsoft, Twilio, NASA, ...), fetched once per session and searched locally.
search_catalog(query="weather forecast")
  -> [{"name": "open_meteo", "spec": "https://...forecast.yml", "base_url": "https://api.open-meteo.com", ...}]
load_api(spec="https://...forecast.yml", name="open_meteo", base_url="https://api.open-meteo.com")
execute(operation_id="open_meteo.get_v1_forecast", params={"latitude": 52.52, "longitude": 13.41, "hourly": "temperature_2m"})

The catalog is discovery-only: it returns spec URLs, never loads or executes anything itself, so the security guard still applies to everything you load from it.

Installation

The server runs in any MCP host via uvx - no manual install needed, the package is fetched from PyPI automatically (add [all] for GraphQL/gRPC/SOAP support):

{
  "mcpServers": {
    "universal-connector": {
      "command": "uvx",
      "args": ["universal-connector-mcp"],
      "env": { "UCMCP_ALLOWED_HOSTS": "api.github.com" }
    }
  }
}

From source (development): pip install -e ".[all,dev]" and use "command": "universal-connector-mcp" instead. Extras: graphql, grpc, soap, keyring, all, dev; the core install supports OpenAPI/REST only.

Cursor

Add to Cursor

One click via the badge above, or add the JSON block to .cursor/mcp.json in your project (shared with your team) or ~/.cursor/mcp.json (personal, all projects). See examples/mcp.json for a full config with credentials.

Claude Desktop

Add the same mcpServers block to claude_desktop_config.json (Settings > Developer > Edit Config).

Claude Code

claude mcp add universal-connector -- uvx universal-connector-mcp

VS Code (Copilot)

Add the server under "servers" in .vscode/mcp.json:

{
  "servers": {
    "universal-connector": { "command": "uvx", "args": ["universal-connector-mcp"] }
  }
}

Windsurf / Cline / others

All major MCP hosts accept the same mcpServers block shown above in their respective config files (~/.codeium/windsurf/mcp_config.json, Cline's MCP settings, etc.).

AI agents installing this server for a user: follow llms-install.md.

Preloading APIs

Point UCMCP_APIS_CONFIG at a YAML file to auto-load APIs at startup - see examples/apis.example.yaml.

Managing APIs from chat

You never edit config files to manage APIs - just tell your agent:

  • "Connect the Stripe API" - the agent finds it in the catalog and loads it.
  • "What APIs are connected?" - list_apis shows them.
  • "Forget GitHub" - unload_api removes it (and from the remembered state).

When an API needs credentials, load_api tells the agent exactly which environment variable to set (e.g. STRIPE_API_KEY), whether it is already configured, and the agent relays copy-pasteable instructions - you add the variable to the env block of your MCP config and restart. Secrets are never typed into the chat.

Session persistence

The server remembers which APIs you loaded (their spec locations - never credentials or response data) in UCMCP_STATE_FILE and restores them automatically on the next start, so the agent can pick up right where it left off. Set UCMCP_STATE_FILE=off to disable.

Supported protocols

Protocol Spec source Notes
OpenAPI / Swagger OpenAPI 3.x or Swagger 2.0 (JSON/YAML), URL/file/raw Core install. Local $ref resolution, all HTTP methods.
GraphQL Introspection JSON or SDL pip install '.[graphql]'. Auto-generates selection sets; execute accepts a fields override.
gRPC Server reflection (grpc://host:port) pip install '.[grpc]'. Unary-unary methods; reflection must be enabled server-side.
SOAP WSDL (URL/file/raw) pip install '.[soap]'. One body object parameter per operation.

Each loaded operation gets an id namespaced as <api>.<operation> (e.g. github.repos_get).

Example session

More walkthroughs (weather, GitHub with auth, GraphQL, parallel multi-API workflows, internal APIs): docs/EXAMPLES.md

search_catalog(query="github")
load_api(spec="https://raw.githubusercontent.com/github/rest-api-description/main/descriptions/api.github.com/api.github.com.json", name="github")
search_operations(query="list repositories for user", api="github")
get_operation(operation_id="github.repos_list_for_user")
execute(operation_id="github.repos_list_for_user", params={"username": "torvalds"},
        extract=["*.name", "*.stargazers_count"])

GraphQL with field selection:

load_api(spec="https://countries.trevorblades.com/", protocol="graphql", name="countries")
execute(operation_id="countries.country", params={"code": "US", "fields": "name capital currency"})

Chaining (feed one result into the next; wrap steps in a nested list to run them in parallel):

execute_chained(steps=[
  {"operation_id": "github.repos_list_for_user", "params": {"username": "torvalds"}, "save_as": "repos"},
  {"operation_id": "github.repos_get", "params": {"owner": "torvalds", "repo": "${repos.data.0.name}"},
   "extract": ["description", "stargazers_count"]}
])

Configuration

All settings are environment variables prefixed with UCMCP_:

Variable Default Description
UCMCP_ALLOWED_HOSTS (empty) Comma-separated extra hosts allowed for outbound calls. Prefix with . for suffix matches.
UCMCP_DENIED_HOSTS (empty) Comma-separated hosts always blocked (wins over allow).
UCMCP_ALLOW_ALL_HOSTS false Disable the allowlist entirely (not recommended). Does not disable private-IP blocking.
UCMCP_BLOCK_PRIVATE_IPS true Block outbound calls that resolve to private/loopback/link-local/cloud-metadata IPs (SSRF protection).
UCMCP_MAX_REDIRECTS 5 Max HTTP redirects to follow; every hop is re-checked against the guard.
UCMCP_MAX_RESPONSE_BYTES 100000 Response body cap sent back to the agent.
UCMCP_HTTP_TIMEOUT 30 Per-request timeout (seconds).
UCMCP_MAX_RETRIES 2 Retries on transient HTTP failures (429/502/503/504).
UCMCP_CACHE_TTL 60 Seconds to cache successful GET/query responses (0 disables).
UCMCP_AUDIT_ENABLED true Toggle audit logging.
UCMCP_AUDIT_FILE (none) Append audit entries to this file (JSON lines).
UCMCP_USE_KEYRING false Also resolve secrets from the OS keyring.
UCMCP_APIS_CONFIG (none) Path to a YAML file of APIs to preload at startup.
UCMCP_STATE_FILE ~/.universal-connector-mcp/state.json Where loaded APIs are remembered between restarts (spec locations only - never secrets or data). Set to off to disable.

Credentials are looked up by convention from <API_NAME>_TOKEN, <API_NAME>_API_KEY, <API_NAME>_CLIENT_ID / <API_NAME>_CLIENT_SECRET (OAuth2 client credentials), etc.

Development

pip install -e ".[all,dev]"
pytest          # run the test suite
ruff check .    # lint

The test suite (60+ tests) and lint run in CI on Ubuntu and Windows with Python 3.10 and 3.12 on every push (.github/workflows/ci.yml); tagging v* builds and publishes to PyPI via trusted publishing (.github/workflows/release.yml).

Contributions welcome - see CONTRIBUTING.md. Security reports go through private reporting.

Releases are automated: tagging v* publishes to PyPI via trusted publishing (see docs/RELEASING.md).

Security model

  • Outbound allowlist - by default only hosts of explicitly loaded specs are reachable. Extend/limit via UCMCP_ALLOWED_HOSTS / UCMCP_DENIED_HOSTS.
  • SSRF protection - requests that resolve to private, loopback, link-local, reserved or cloud-metadata addresses are blocked (including spec fetches, GraphQL introspection and SOAP imports). Every redirect hop is re-validated. Reaching an internal address requires an explicit UCMCP_ALLOWED_HOSTS entry.
  • Secret handling - credentials come from environment variables or the OS keyring, are injected only at request time, and are redacted from audit logs and errors.
  • Response caps - responses are truncated to a configurable byte limit to protect the context window.
  • Audit log - method, host, path and status of every outbound call (never secrets or bodies) are recorded.

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