LLM agents often place raw JSON tool outputs directly in the prompt. After a few tool calls, earlier results get compacted or truncated and answers become incorrect or inconsistent.

I built Sift, a drop-in MCP gateway that stores tool outputs as local artifacts (filesystem blobs indexed in SQLite) and returns an `artifact_id` plus compact schema hints when responses are large or paginated.

Instead of reasoning over full JSON in the prompt, the model runs a small Python query:

    def run(data, schema, params):
        return max(data, key=lambda x: x["magnitude"])["place"]

Query code runs in a constrained subprocess (AST/import guards + timeout/memory caps). Only the computed result is returned to the model.

Benchmark (Claude Sonnet 4.6, 103 questions across 12 datasets):

- Baseline (raw JSON in prompt): 34/103 (33%), 10.7M input tokens

- Sift (artifact + code query): 102/103 (99%), 489K input tokens

Open benchmark + MIT code: https://github.com/lourencomaciel/sift-gateway

Install:

    pipx install sift-gateway
    sift-gateway init --from claude

Works with Claude Code, Cursor, Windsurf, Zed, and VS Code. Existing MCP servers and tools require no changes.