Ten Typed Tools over MCP
Why agents get ten small, typed, token-budgeted tools over MCP instead of one giant search box, and what that buys in tokens, latency, and legible failures.
4 min read
Blog
Notes from the forest
Engineering notes on navigation, ingestion, agent memory and honest benchmarking, written by the people who ran the experiments.
Where the Latency Goes
17.5 s p95 for iterative RAG, 8.4 s for the navigator, same 12B model. A dissection of both loops, and why variance measured in cheap hops instead of full retrieve-and-read rounds collapses the tail.
3 min read
Anatomy of a Node Passport
Title, sixty-token summary, tags, aliases: why the passport is where MonkeyLLM spends its intelligence, and how field-weighted BM25 turns curation into recall@5 = 1.00 at 1.3 ms.
4 min read
BM25 Over SQLite vs a Vector Database
The category assumes an embedding service, an ANN index and a GPU. We measured entry search at recall@5 = 1.00 at 1.3 ms p95 with BM25 over SQLite, and kept the embedder optional.
4 min read
The Forest Principle: spend intelligence on the environment, not the model
Stigmergy applied to a corpus. What a navigable environment concretely means, what it buys in tokens and hardware, and what it honestly does not fix.
3 min read
Why multi-hop questions break classic RAG (and what to do about it)
Top-k retrieval is a single hop by construction. When an answer needs three, the bottleneck stops being the model and becomes the shape of your corpus.
4 min read
Want the long version?
The paper carries the full architecture, the benchmark tables and the findings that failed their criteria.