Mcp Cornell Resume

Created By
johndezra year ago
A Model Context Protocol (MCP) server that automatically generates Cornell-style study notes and summaries from the conversational context, with RAG active recall question generation and Notion integration.
Overview

What is Mcp Cornell Resume?

Mcp Cornell Resume is a Model Context Protocol (MCP) server that automatically generates Cornell-style study notes and summaries from conversational context, integrating active recall question generation and Notion synchronization.

How to use Mcp Cornell Resume?

To use the MCP server, clone the repository, install the required dependencies, configure your environment with API keys, and run the server. You can then integrate it with compatible applications like Claude Client Desktop.

Key features of Mcp Cornell Resume?

  • Real-time Cornell-style note generation from chat history.
  • Context-aware active recall question generation using vector similarity.
  • Semantic search integration with Pinecone for relevant note retrieval.
  • Automatic synchronization with Notion for organized note storage.
  • OpenAI-powered text processing and question generation.

Use cases of Mcp Cornell Resume?

  1. Generating structured study notes from lecture conversations.
  2. Creating active recall questions for exam preparation.
  3. Organizing notes in Notion for easy access and review.

FAQ from Mcp Cornell Resume?

  • Can I use Mcp Cornell Resume with any chat application?

Yes, it can be integrated with any MCP-compatible application.

  • Is there a limit to the number of notes I can generate?

The only limitation is the context window of the LLM, which may affect longer conversations.

  • How does the integration with Notion work?

The server automatically saves generated notes to your Notion database, formatted for easy reading.

Server Config

{
  "mcpServers": {
    "resume_to_notion": {
      "command": "/path-to-uv/uv",
      "args": [
        "--directory",
        "/path-to-project/mcp-cornell-resume",
        "run",
        "main.py"
      ]
    }
  }
}
Project Info
Created At
a year ago
Updated At
a year ago
Author Name
johndezr
Star
-
Language
-
License
-
Category

Recommend Servers

View All
Hellogrowthcrm

7 hours ago
Voyei

2 days ago
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)

14 hours ago