retrieval-mcp 0.2.3

Installable MCP server for the Retrieval API: academic-paper search + journal memory + ACE playbook + semantic code search

retrieval-mcp

An MCP server for the Retrieval academic-paper API — semantic paper search, document matching, ACE journal memory, and index inventory. Self-contained: it talks to the backend over HTTP only (just mcp + httpx), so it installs anywhere with uvx / pip — no repo checkout, no GPU, no models.

By default it targets the compute box on the lab LAN (http://10.100.100.111:8000), which trusts LAN callers so no key is needed. Off-LAN, point RETRIEVAL_API_URL at the public gateway (https://retrieval.rnarket.com) and set RETRIEVAL_API_KEY (sk-...).

Tools

Every tool's full docstring (purpose + each argument with its default + an example) is what your LLM sees — call them by name. Summary:

Paper retrieval

Tool	What it does
search_papers	Semantic hybrid search over 95k+ top-venue CS papers (filters: venue, year, title_only)
search_within_paper	Every matching passage inside one paper
match_document / match_paper	Content-nearest papers to a passage / to a paper
list_conferences / corpus_stats	Venue registry / corpus size

Journal work-memory (scoped to the current project by default)

Tool	What it does
journal_record	Record a work note (memory) or a file's current content (doc, latest-wins)
journal_search	Search memory — keyword (FTS5, no embedding) or hybrid/dense/sparse
journal_recent	List recent entries
journal_index_dir	Batch-index a local dir's files into the journal (latest-wins per file)

Code KB (source stays local — only chunks are uploaded)

Tool	What it does
index_code	AST-chunk a repo locally (40+ languages) and index it, scoped to you
search_code	Semantic code search with path:line citations
index_inventory	Your indexed-file tree: user → host → project → dir → file

ACE playbook (accumulated, curated lessons per project)

Tool	What it does
ace_context_aware / ace_playbook	Retrieve relevant / list all curated bullets
ace_enhance_prompt / ace_smart_generate	Attach playbook lessons to a prompt (no LLM call)
ace_smart_reflect	Curate a transferable lesson into the playbook (grow-and-refine dedup)

Code KB language coverage

index_code chunks 40+ languages structurally via tree-sitter (chonkie CodeChunker): Python, TypeScript/TSX/JS/JSX (React), Java, Kotlin (incl. Jetpack Compose .kt/.kts), Swift, Go, Rust, C/C++, C#, Ruby, PHP, Lua, Scala, Dart, R, Julia, Elixir, Erlang, Haskell, OCaml, SQL, GraphQL, Protobuf, HTML, CSS/SCSS (Tailwind = CSS classes), Vue, Svelte, shell, PowerShell, Dockerfile, Terraform/HCL, CMake, YAML/JSON/TOML/XML, and more. Grammarless config/text files fall back to line-window chunks; docs (.md) and binaries are skipped (docs belong in the journal via journal_index_dir).

Install

Claude Code

# LAN (no key):
claude mcp add retrieval -- uvx retrieval-mcp
# Off-LAN (public gateway + key):
claude mcp add retrieval \
  --env RETRIEVAL_API_URL=https://retrieval.rnarket.com \
  --env RETRIEVAL_API_KEY=sk-... \
  -- uvx retrieval-mcp

Claude Desktop / any MCP client

claude_desktop_config.json (macOS: ~/Library/Application Support/Claude/, Windows: %APPDATA%\Claude\):

{
  "mcpServers": {
    "retrieval": {
      "command": "uvx",
      "args": ["retrieval-mcp"],
      "env": {
        "RETRIEVAL_API_URL": "https://retrieval.rnarket.com",
        "RETRIEVAL_API_KEY": "sk-...",
        "JOURNAL_PROJECT_SLUG": "my-project"
      }
    }
  }
}

No uv? pip install retrieval-mcp then use "command": "retrieval-mcp".

Config (env)

Var	Default	Notes
RETRIEVAL_API_URL	http://10.100.100.111:8000	LAN compute box (no key). Off-LAN, set to https://retrieval.rnarket.com.
RETRIEVAL_API_KEY	—	sk-... key for the gateway (create under /auth/keys). Required off-LAN.
JOURNAL_PROJECT_SLUG	current dir name	Journal namespace; scopes journal reads/writes so projects don't leak into each other.

License

MIT