Lossless-first compression layer for AI-agent tool outputs — byte-faithful on fields you mark critical, configurable lossy reduction only where you opt in.
Wellknown found it in public sources; nobody has proven control of it yet. Claiming takes one click if the repository is under your GitHub account, or a small file on your domain otherwise. Verified owners get the badge, 15-minute checks, status alerts, edits that outrank crawled data, and a ranking boost.
Agents can do it too: POST https://wellknown.network/api/v1/claims with {"agent":"terse-mcp","method":"well_known_file"} — machine-readable steps at claim.json, guide at /docs/claim.
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# terse [](https://github.com/inth3shadows/terse/actions/workflows/tests.yml) [](https://pypi.org/project/terse-mcp/) [](pyproject.toml) [](LICENSE) [](https://glama.ai/mcp/servers/inth3shadows/terse) **Cut the tokens your AI agent spends reading tool output, without changing a single value it reads.** terse is a drop-in proxy for any MCP server. It strips structural overhead from each result (repeated keys, repeated values, repeated nested shapes) and hands the model the same data, value for value, in a denser form. No decode step, no model in the loop. Five GitHub-style issues cost 436 tokens (cl100k) as a server pretty-prints them, and 232 minified. What the model gets instead costs 181: ```json {"__terse_dict__":1,"legend":{"~0":[{"name":"bug","color":"d73a4a"}]}, "data":{"__terse_table__":1,"n":5,"cols":["number","state","title","user","labels","repo"], "rows":[[101,"open","Crash on empty cart",["ana","User"],"~0","acme/widgets"], [102,"open","Timeout on checkout",["ben","User"],"~0","acme/widgets"], ...], "subcols":{"user":{"cols":["login","type"]}}}} ``` - **Lossless, and checked.** Every transform has an exact inverse, and a round-trip gate asserts `decompress(compress(x)) == x`. Other tools in this space drop rows or old results to save tokens; terse only removes repetition. - **Measured against the alternatives.** On real GitHub API payloads: **59.1%** fewer tokens, against −6.5% for TOON and 4.5% for compressmcp on the same corpus ([Benchmarks](#benchmarks-terse-vs-alternatives)). - **Selective, never worse.** It works per tool and passes through anything it can't shrink, so it n…
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