You're Wasting Tokens... That Stops NOW

Most AI agents still explore documentation the expensive way:

open file → skim hundreds of irrelevant paragraphs → open another file → repeat

That burns tokens, floods context windows with noise, and forces models to reason through a lot of text they never needed in the first place.

jDocMunch-MCP lets AI agents navigate documentation by section instead of reading files by brute force.
It indexes a documentation set once, then retrieves exactly the section the agent actually needs, with byte-precise extraction from the original file.

Index once. Query cheaply forever.
Precision context beats brute-force context.

jDocMunch MCP

AI-native documentation navigation for serious agents

Commercial licenses

jDocMunch-MCP is free for non-commercial use.

Commercial use requires a paid license.

jDocMunch-only licenses

• Builder — $29 — 1 developer
• Studio — $99 — up to 5 developers
• Platform — $499 — org-wide internal deployment

Want both code and docs retrieval?

• Munch Duo Builder Bundle — $89
• Munch Duo Studio Bundle — $399
• Munch Duo Platform Bundle — $2,249

1.x compatibility commitment

Every 1.x license entitles you to every future 1.x release. We will never ship a 1.x version that:

• removes or renames an MCP tool (deprecated tool names keep their aliases),
• drops a Section field from the response shape,
• forces a reindex without auto-migrating your existing index on first load,
• changes the JSON wire format of any tool response in a way that breaks an existing consumer,
• or makes a previously-default behavior raise.

Anything that would require breaking these promises is reserved for a future major version (2.x). The full machine-checked contract is enforced via tests/test_server.py (tool-name and required-field invariants) and the replay-fixture gate that runs on every release.

Stop dumping documentation files into context windows. Start navigating docs structurally.

jDocMunch indexes documentation once by heading hierarchy and section structure, then gives MCP-compatible agents precise access to the explanations they actually need instead of forcing them to brute-read files.

It is built for workflows where token efficiency, context hygiene, and agent reliability matter.

Why this exists

Large context windows do not fix bad retrieval.

Agents waste money and reasoning bandwidth when they:

• open entire documents to find one configuration block
• repeatedly re-read headings, boilerplate, and unrelated sections
• lose important explanations inside oversized context payloads
• consume documentation as flat text inste

... View full README on GitHub