n8n AI Agent for DVM MCP

Created By
belokoleka year ago
An AI agent built in n8n which can find and use Model Context Protocol (MCP) Server Tools served as Data Vending Machines (DVM) over the Nostr network.
Overview

What is n8n AI Agent for DVM MCP?

The n8n AI Agent for DVM MCP is an AI agent built using n8n that interacts with Model Context Protocol (MCP) Server Tools, functioning as Data Vending Machines (DVM) over the Nostr network. It enables AI to discover and utilize tools that are not installed locally by querying the network.

How to use n8n AI Agent for DVM MCP?

To use the n8n AI Agent, you need to set up a self-hosted n8n instance, install the necessary community nodes, and import the provided workflows from GitHub. You will also need to configure credentials for various APIs and set workflow variables.

Key features of n8n AI Agent for DVM MCP?

  • Queries the Nostr network for available MCP tools.
  • Posts requests to MCP tools and waits for responses.
  • Reads responses and interacts with users based on the tool outputs.
  • Supports integration with various APIs and tools through a low-code platform.

Use cases of n8n AI Agent for DVM MCP?

  1. Discovering and using new tools over the Nostr network.
  2. Automating data retrieval and processing tasks using AI.
  3. Enhancing AI capabilities by integrating with external data sources.

FAQ from n8n AI Agent for DVM MCP?

  • What is the Model Context Protocol (MCP)?

MCP is an open protocol that allows AI agents to access various data sources and tools.

  • How do I set up the n8n AI Agent?

Follow the installation instructions on the n8n website and import the workflows from GitHub.

  • Is there a cost associated with using n8n AI Agent?

n8n is open-source and free to use, but you may incur costs for any external APIs you integrate.

Project Info
Created At
a year ago
Updated At
a year ago
Author Name
belokolek
Star
1
Language
-
License
MIT license
Tags

Recommend Servers

View All
GovQL
@Alex Stout

# govql-mcp-server An MCP (Model Context Protocol) server for [GovQL](https://govql.us) — gives AI clients like Claude Desktop, Claude Code, and Cursor direct access to the US Congressional GraphQL API at [api.govql.us/graphql](https://api.govql.us/graphql) without bespoke HTTP wiring. For the design rationale (why FastMCP-Python, the passthrough+curated philosophy, roadmap through v0.4), see [design.md](https://github.com/govql/govql/blob/main/mcp-server/docs/design.md). ## What you can do with it Ask an agent questions like: - *"How did Vermont's two senators vote on the most recent nomination?"* - *"Which legislators in the 118th Congress switched parties during their service?"* - *"Compare Senator Sanders' voting record to Senator Murkowski's on cloture votes in the most recent Congress."* The agent picks the right tool, writes the GraphQL query against the live schema, and parses the response — no manual API wrangling. ## Install The server runs as a per-client subprocess over stdio. Pick your client: ### Claude Desktop Edit `claude_desktop_config.json` (Settings → Developer → Edit Config): ```json { "mcpServers": { "govql": { "command": "uvx", "args": ["govql-mcp-server"] } } } ``` Restart Claude Desktop. The `govql` tools appear in the tools panel. ### Claude Code Add to `.mcp.json` in your project (or `~/.mcp.json` for global): ```json { "mcpServers": { "govql": { "command": "uvx", "args": ["govql-mcp-server"] } } } ``` ### Cursor Settings → MCP → Add Server. Use the same `command` / `args` as above. ### Other clients Any MCP-compatible client that supports stdio servers will work. The command is `uvx govql-mcp-server` with no required arguments. ## Tools | Tool | Purpose | |---|---| | `execute_graphql` | Run any GraphQL query against the GovQL endpoint. Returns the result plus an `last_ingest` timestamp so the agent can reason about data freshness. | | `list_types` | Returns the names and kinds of every type in the GovQL schema. Optional `kind` filter (`"OBJECT"`, `"INPUT_OBJECT"`, `"ENUM"`, etc.) to narrow further. Start here when you don't know what's queryable. | | `describe_type` | Returns one type's full details — fields, arg signatures, input fields, enum values. Call after `list_types` to learn the shape of a specific type before writing a query. | ## Configuration All env vars are optional — the package is zero-config for end users. | Env var | Default | Purpose | |---|---|---| | `GOVQL_ENDPOINT` | `https://api.govql.us/graphql` | Endpoint to query. Override to point at a local dev stack. | | `GOVQL_TIMEOUT_MS` | `30000` | Per-request HTTP timeout. | | `LOG_LEVEL` | `INFO` | Logging level. Logs go to stderr only (stdout is reserved for the MCP transport). | ## Limits (enforced by the upstream API) - Max query depth: 10 - Max query complexity: ~10 billion points (`first: N` multiplies child cost by N — keep page sizes reasonable on deeply nested queries) - Rate limit: 100 requests / 60 s per source IP A depth or complexity violation surfaces as a GraphQL `errors` entry in the tool response so the agent can adjust and retry. ## Data freshness Every `execute_graphql` response includes a `last_ingest` ISO timestamp. Vote data refreshes hourly; legislator data refreshes daily. ## Status Version 0.1.0 ships three foundational tools: a GraphQL passthrough (`execute_graphql`) and two narrow schema-discovery tools (`list_types`, `describe_type`). Curated higher-level tools (`find_legislator`, `get_voting_record`, `compare_voters`, etc.) are planned for subsequent releases — see [design.md](https://github.com/govql/govql/blob/main/mcp-server/docs/design.md) for the roadmap. ## Links - [GovQL project site](https://govql.us) - [GraphQL API](https://api.govql.us/graphql) - [Source / issues](https://github.com/govql/govql)

21 hours ago
Meok Bs7121 Mcp

7 hours ago