菜单
Configures MCP (Model Context Protocol) servers in a Rasa CALM assistant. Use when defining MCP servers in endpoints.yml, setting up authentication (API key, OAuth 2.0, pre-issued token), or connecting to multiple external services.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Use when a user wants to evaluate or test a Rasa assistant using the eval scenario framework — at any stage of the process: setting up the evaluation infrastructure for the first time, deciding which scenario types to cover for a flow, writing or generating scenario YAML files, learning about supported assertion types or how to write good success criteria, running goal-driven simulations where an LLM plays the user role against a live Rasa bot, or analyzing whether existing scenarios are comprehensive. Covers both the authoring side ("write scenarios", "generate evals", "help me write criteria") and the execution side ("simulate a conversation", "run my eval scenarios"). Not for scripted end-to-end tests, unit tests, NLU training, or general Rasa bot development.
Builds skills (capabilities) for a Rasa CALM assistant. Use when the user asks to create, add, or build a skill, capability, or feature for their Rasa assistant.
Configures config.yml and endpoints.yml for Rasa CALM assistants. Covers pipeline (command generators, flow retrieval), policies (FlowPolicy), action endpoint, and language settings. Use when setting up a new project or modifying pipeline components.
Configures model_groups in endpoints.yml for LLM and embedding providers. Covers single and multi-deployment setups, routing strategies, failover, self-hosted models, and caching. Use when adding or changing LLM providers or setting up multi-LLM routing.
Enables and configures LLM-powered Contextual Response Rephraser in Rasa CALM. Covers endpoints.yml setup, per-response metadata, and prompt customization. Use when setting up or tuning the Contextual Response Rephraser.
Adds knowledge base search to a Rasa CALM assistant using EnterpriseSearchPolicy, connects vector stores (Faiss, Milvus, Qdrant), and configures generative or extractive search modes. Use when setting up RAG, connecting a vector store, overriding pattern_search, or implementing a custom information retriever.
| name | rasa-configuring-mcp-server |
| description | Configures MCP (Model Context Protocol) servers in a Rasa CALM assistant. Use when defining MCP servers in endpoints.yml, setting up authentication (API key, OAuth 2.0, pre-issued token), or connecting to multiple external services. |
| license | Apache-2.0 |
| metadata | {"author":"rasa","version":"0.1.0","rasa_version":">=3.14.0","docs-url":"https://rasa.com/docs/reference/integrations/mcp-servers"} |
MCP servers expose external tools — APIs, databases, services — to a Rasa assistant. All
MCP servers are defined in endpoints.yml and shared across direct flow tool calls and
ReAct sub agents.
This feature is in beta and available starting from Rasa 3.14.0.
endpoints.yml (see "Server definition").Register every MCP server in endpoints.yml.
| Key | Required | Description |
|---|---|---|
name | yes | Unique identifier — referenced from flows and sub agent configs. Duplicates cause a validation error at startup |
url | yes | URL where the MCP server is running |
type | yes | http or https |
# endpoints.yml
mcp_servers:
- name: trade_server
url: http://localhost:8080
type: http
- name: payment_server
url: https://api.payment-service.com
type: https
Add auth fields directly to the server entry in endpoints.yml when the MCP server
requires credentials. Sensitive values (api_key, token, client_secret) must
use ${ENV_VAR} syntax — plain text is rejected by validation.
Sent as Authorization: Bearer <key> by default. Add header_name and
header_format to override the header.
mcp_servers:
- name: secure_api_server
url: https://api.example.com
type: https
api_key: "${API_KEY}"
header_name: "X-API-Key" # optional, default: Authorization
header_format: "{key}" # optional, default: Bearer {key}
mcp_servers:
- name: oauth_server
url: https://api.example.com
type: https
oauth:
client_id: "${CLIENT_ID}"
client_secret: "${CLIENT_SECRET}"
token_url: "https://auth.example.com/oauth/token"
scope: "read:data write:data" # optional
audience: "https://api.example.com" # optional
timeout: 10 # optional
mcp_servers:
- name: token_server
url: https://api.example.com
type: https
token: "${ACCESS_TOKEN}"