Keep large tool output out of LLM context: 3x accuracy 95% fewer tokens
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…
Keep large tool output out of LLM context: 3x accuracy 95% fewer tokens launched on March 5, 2026 with 10 votes, #1,177 of 23,380 launches that month and more than 90% of that year's launches.
In plain words
Sift is an MCP gateway that improves LLM agent accuracy by storing tool outputs as local artifacts instead of placing raw JSON directly in prompts. When responses are large or paginated, it returns a compact artifact ID and schema hints, allowing the model to run small Python queries on the data instead of reasoning over full JSON in the context window. This approach prevents information loss from prompt truncation. It's designed for developers building LLM agents who need consistent, accurate reasoning across multiple tool calls.
written from the facts on this page · September 2026
From the sources
Reliability gateway for AI tool output: schema-stable, secret-safe, pagination-complete JSON for MCP and CLI agents. - lourencomaciel/sift-gateway
In the maker’s words, at launch
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…
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