# Phaedrus

A server that connects Claude Code to a store of research findings, tracked literature sources, and tools for analyzing and submitting research candidates. Works with local files, no database required.

- MCP server · `type=mcp` · Claude Code, Codex, Cursor, GitHub Copilot, Windsurf (Devin Desktop), Cline, Zed
- Author: qompassai
- License: Apache-2.0 · policy=link-only · description only; the file stays with the author
- Source: https://github.com/qompassai/phaedrus
- Updated: 2026-10-21
- HTML: https://affpapa.org/en/nevibecode/mcp/phaedrus

## What it does

Phaedrus is a Rust binary that speaks MCP over stdio to Claude Code and exposes seven tools for managing a research feed. The `capability_context` tool surfaces the capability registry from a paired Lupin server (used for planning); `feed_summary` gives counts of findings by system and type. The `finding_get` and `finding_list` tools retrieve curated findings; `research_submit` files a candidate (finding, roadmap item, or test) into a scan inbox without modifying the curated store. The server connects two data directories: one for findings and watchlist, another for read-only access to Lupin's capability context (defaults are `~/workspace/research-feed-20261019` and `~/workspace/revenue-alignment-20261019`).

Writes are intentionally constrained: `revenue_candidate_draft` only assembles a draft payload for Lupin's opportunity submission, never writing to Lupin's own files. The server uses NDJSON JSON-RPC and has no network listener.

## Requirements
- Needs: a prebuilt binary from the repository releases

## Install

### Claude Code (native platform)

**Install**
- The MCP registry has no launch command for it: see the repository README

## When to use it
- Track curated research findings and summarize counts by system
- Submit research candidates to a scan inbox for review
- Query the list of monitored literature venues and search terms
- Draft a revenue opportunity submission from a research finding

## Example prompts
- List all findings for the Deep Learning system
- Submit a candidate finding: novel gradient optimization approach, potential for revenue
- Show me the watchlist and how many findings we have per system

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