Compendium

MCP server that minimizes LLM token usage by compressing, summarizing, filtering, and chunk-referencing large context before it reaches the model.

Built in Rust with the official rmcp SDK.

Quick start (Cursor)

You need Node.js 18+. Compendium itself arrives via npm — no Rust install required.

1. Add the MCP server

Open Cursor MCP settings (~/.cursor/mcp.json or the project .cursor/mcp.json) and add:

{
  "mcpServers": {
    "compendium": {
      "command": "npx",
      "args": ["-y", "compendium-mcp"]
    }
  }
}

Restart MCP / reload Cursor. You should see one tool named compendium.

That alone is enough: filter, compress, summarize, cache, and BM25 actions all work without a local model (fast heuristics).

2. (Optional) Smarter summaries with Ollama

Want better summarize_smart / filter_relevant? Run a small model on your machine and point Compendium at it.

1. Install Ollama and start it (default: http://127.0.0.1:11434).

2. Pull a chat model, for example:

ollama pull qwen:latest
# or a smaller one: ollama pull qwen2.5:3b

3. Extend the MCP env block (URL must stay on localhost — Compendium blocks remote hosts on purpose):

{
  "mcpServers": {
    "compendium": {
      "command": "npx",
      "args": ["-y", "compendium-mcp"],
      "env": {
        "COMPENDIUM_LOCAL_LLM_URL": "http://127.0.0.1:11434/v1",
        "COMPENDIUM_LOCAL_LLM_MODEL": "qwen:latest"
      }
    }
  }
}

4. Reload MCP, then ask the agent to call compendium with action: "summarize_smart".
   In the result, "backend": "local_llm" means Ollama answered; "heuristic" means it fell back (Ollama down, wrong model name, or URL missing).

Notes

• Package name on npm is compendium-mcp (compendium was already taken). The CLI binary name is still compendium.

• First Ollama reply can be slow while the model loads; later calls are faster.

• Other local OpenAI-compatible servers work the same way (e.g. Lemonade http://127.0.0.1:13305/api/v1). See Environment.

Smoke-check from a terminal (any folder except this git repo root is fine):

npx -y compendium-mcp --help

Binary packaging details for maintainers: npm/DISTRIBUTION.md.

Related MCP server: claw-tsaver

Community

• Contributing

• Code of Conduct

• Security policy

• Support

Transports

Default HTTP bind: 127.0.0.1:8788 (override with arg or COMPENDIUM_HTTP_BIND).

Tools

Single MCP tool: compendium. Choose the operation with action:

Optional on most text actions: sanitize_input: true scrubs before processing. Soft payloads under COMPENDIUM_SIGNAL_MIN_CHARS (default 1000) bypass compress / summarize / summarize_smart unless force: true.

filter accepts optional query (top-level or filter.query) for BM25 line keep. prune_history supports prune.strategy: "afm" (Critical / Thematic / Distant tiers; distant blob cached for cache_get).

Example:

{
  "action": "filter",
  "text": "…noisy log…",
  "filter": { "strip_ansi": true, "keep_patterns": ["ERROR|WARN"] }
}

Response envelope: { "ok": true, "action": "filter", "result_json": "{...}" }. Parse result_json as JSON for the action-specific payload.

Project layout

package.json / bin/run.js  # npm wrapper for npx compendium-mcp
npm/                       # platform packages + distribution docs
.github/workflows/         # release cross-compile + npm publish
src/
  main.rs              # CLI: stdio | http
  lib.rs
  config.rs            # COMPENDIUM_* env config
  server.rs            # MCP tool handlers (rmcp macros)
  http.rs              # Streamable HTTP/SSE (feature = "http")
  pipeline/
    tokens.rs          # heuristic or tiktoken BPE (feature = "real-tokens")
    filter.rs
    compress.rs
    summarize.rs
    smart.rs           # summarize_smart + filter_relevant
    local_llm.rs       # OpenAI-compatible local SLM client
    chunk.rs           # chunk + resolve
    cache.rs           # session key/value cache
    stats.rs           # session savings counters
    prune.rs           # conversation history pruning
    output.rs          # domain-aware compress_output
tests/
  integration.rs
  e2e_smoke.rs         # spawns binary, MCP handshake, all tools

Build

# Default: heuristic tokens + stdio only
cargo build --release

# Exact BPE token counts (tiktoken-rs)
cargo build --release --features real-tokens

# Streamable HTTP transport
cargo build --release --features http

# Everything
cargo build --release --features real-tokens,http

Binary: target/release/compendium

Configure (advanced)

The Quick start config is enough for most people. Extra options:

Claude Desktop

Same command / args / env as Cursor, in Claude’s MCP config file.

Optional tuning env

"env": {
  "RUST_LOG": "compendium=info",
  "COMPENDIUM_DEFAULT_MAX_TOKENS": "2048",
  "COMPENDIUM_TOKENIZER": "cl100k_base",
  "COMPENDIUM_LOCAL_LLM_URL": "http://127.0.0.1:11434/v1",
  "COMPENDIUM_LOCAL_LLM_MODEL": "qwen:latest"
}

Local Cargo binary (developers)

{
  "mcpServers": {
    "compendium": {
      "command": "/absolute/path/to/Compendium/target/release/compendium",
      "env": {
        "RUST_LOG": "compendium=info",
        "COMPENDIUM_DEFAULT_MAX_TOKENS": "2048"
      }
    }
  }
}

Remote / sidecar (HTTP)

cargo run --features http -- http 127.0.0.1:8788
# MCP endpoint: http://127.0.0.1:8788/mcp

Point an MCP streamable-HTTP client at that URL (e.g. StreamableHttpClientTransport::from_uri).

Environment

Example tool calls

Filter noisy terminal output

{
  "name": "compendium_filter",
  "arguments": {
    "text": "\u001b[31mERROR\u001b[0m boom\n\n\nINFO ok",
    "options": {
      "strip_ansi": true,
      "keep_patterns": ["ERROR|WARN"]
    }
  }
}

Compress a large log

{
  "name": "compendium_compress",
  "arguments": {
    "text": "...",
    "options": {
      "content_type": "log",
      "max_tokens": 512
    }
  }
}

Chunk a document into references

{
  "name": "compendium_chunk",
  "arguments": {
    "text": "... huge file ...",
    "options": {
      "source": "file:///path/to/doc.md",
      "chunk_tokens": 400,
      "overlap_tokens": 40
    }
  }
}

Prefer the returned index_text in the model context; pull individual chunk contents by id only when needed.

Query-aware filter (local SLM or heuristic fallback)

{
  "action": "filter_relevant",
  "text": "... noisy cargo/test log ...",
  "query": "why did the auth tests fail",
  "smart": { "max_tokens": 512, "fallback": true }
}

Without COMPENDIUM_LOCAL_LLM_URL, summarize_smart / filter_relevant automatically use heuristics and set backend: "heuristic" plus fallback_reason in the result.

Local small language model

Follow Quick start §2 for Ollama.

Rules of thumb:

• Only loopback URLs (127.0.0.1, ::1, localhost) — no cloud endpoints.

• Without COMPENDIUM_LOCAL_LLM_URL, smart actions use heuristics and set backend: "heuristic".

• Calls use temperature=0 and seed=0 for stable outputs.

• Lemonade example: COMPENDIUM_LOCAL_LLM_URL=http://127.0.0.1:13305/api/v1 and COMPENDIUM_LOCAL_LLM_MODEL=Qwen3-4B-GGUF.

• llama.cpp OpenAI server: same pattern — set URL to its /v1 base and the served model id.

Develop / test

cargo test
cargo test --features real-tokens
cargo test --features http --test http_smoke
cargo test --test e2e_smoke
cargo run --features http -- http 127.0.0.1:8788

e2e_smoke spawns CARGO_BIN_EXE_compendium, completes the MCP initialize handshake over stdio, lists tools, then calls gateway actions. http_smoke (requires --features http) exercises streamable HTTP in-process.

Design notes

• Deterministic by default — heuristic pipeline needs no network; smart actions only call a configured local OpenAI-compatible URL and fall back to heuristics when unset or failing.

• Token backends — fast heuristic by default; opt into exact BPE with real-tokens.

• Zero stdout pollution (stdio mode) — tracing goes to stderr so JSON-RPC framing stays clean.

• Release profile — LTO + stripped binary for low footprint.

License

MIT