catenary

Catenary

Stop wasting context on redundant file reads.

AI coding agents are smart. The bottleneck isn't intelligence — it's I/O. Every file an agent reads goes into an append-only context window. Every edit creates another copy. Three rounds of read-edit-verify on a single file puts three full copies in context, re-processed on every subsequent turn. In a typical session, this produces a 1,000x+ amplification between what you type and what the model actually processes.

Catenary replaces brute-force file scanning with graph navigation. Instead of reading a 500-line file to find a type signature, the agent asks the language server directly — 50 tokens instead of 2,000. Instead of grepping across 20 files to find a definition, one LSP query returns the exact location. The context stays lean across the entire session.

How It Works

graph LR
    A["AI Assistant<br/>(Claude, Gemini)"] <-->|MCP| B["Catenary<br/><br/>MCP ↔ LSP<br/>Bridge"]
    B <-->|LSP| C[rust-analyzer]
    B <-->|LSP| D[pyright]
    B <-->|LSP| E[gopls]

Catenary bridges MCP and LSP, giving agents the same code intelligence that powers your IDE. It manages multiple language servers, routes requests by file type, and provides automatic post-edit diagnostics — all through a single MCP server.

Quick Start

1. Install

cargo install --git https://github.com/TwoWells/Catenary catenary-mcp

2. Configure language servers

Add your language servers to ~/.config/catenary/config.toml:

[language.rust]
command = "rust-analyzer"

[language.python]
command = "pyright-langserver"
args = ["--stdio"]

3. Connect your AI assistant

Plugins and extensions register hooks and MCP server declarations but do not include the binary — step 1 above is required.

Claude Code

/plugin marketplace add TwoWells/Catenary
/plugin install catenary@catenary

The plugin registers the MCP server and adds hooks for post-edit diagnostics, editing state management, and workspace root sync.

Gemini CLI

gemini extensions install https://github.com/TwoWells/Catenary

The extension registers the MCP server and adds hooks for post-edit diagnostics and editing state management.

4. Verify

Check that your language servers and hooks are working:

catenary doctor

rust         rust-analyzer       ✓ ready
             hover definition references document_symbols search code_actions call_hierarchy

python       pyright-langserver  ✓ ready
             hover definition references document_symbols search

toml         taplo               - skipped (no matching files)

Hooks:
  Claude Code 1.3.6 (directory) ✓ hooks match
  Gemini CLI  1.3.6 (linked)    ✓ hooks match

Why This Matters

Operation	Tokens	Copies in context
Read a 500-line file	~2,000	+1 per read
Rewrite that file	~2,000	+1 (now 2 copies)
Read it again to verify	~2,000	+1 (now 3 copies)
Total for one file	~6,000	3 copies

LSP alternative	Tokens	Copies in context
grep for symbols + references	~200	0 (stateless)
Native edit + hook diagnostics	~300	0 (no re-read needed)
Total	~500	0 copies

Every token in context is re-processed on every turn. Bigger context windows don't fix this — they just let you waste more before hitting the wall.

Tools

Tool	Description
grep	Symbols, semantic references, and text matches
glob	Browse the workspace — file outlines, directory listings, glob matches
start_editing / done_editing	Batch editing — diagnostics deferred until you're done
hooks	Post-edit LSP diagnostics with quick-fix suggestions

Full Protocol Transparency

Catenary logs every protocol message — every MCP tool call, every LSP request and response, every hook invocation — to a local SQLite database. Run catenary to open the TUI dashboard and watch the message flow in real time, or query historical sessions with catenary query.

You can see exactly what Catenary sends to your language servers and what they send back. Nothing is hidden.

CLI Commands