Jev Filter MCP server

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Jev Filter

Semantic filtering for AI agent tool results. Turn large tool outputs into compact, typed decisions before they enter your agent's context. Keep source records inside your program, return relevant evidence and unresolved IDs, and recover originals by ID.

• Filter with context. Screen search results, complete code symbols and correlated logs against your task and supplied facts.
• Keep decisions traceable. Return typed answers, source IDs and review items; preserve missing facts and partial failures.
• Verify actions in your program. For short browser and desktop goals, Jev selects observed controls; the program owns execution, safety gates and final-state checks.

An independent MIT project maintained by Apixly. Official npm package: @apixly/jev-filter. What it does and when to use it.

Quick start

Node.js 22+ · macOS / Linux · Python included in the npm distribution. Windows: use WSL or install Python natively. Installation and doctor are offline; inference needs a TypeSafe Jev API key.

npm install -g @apixly/jev-filter
jev-filter doctor

Run this from any directory:

export TYPESAFE_API_KEY='your-key'

jev-filter query --input - --mode choose \
  --task 'Choose the CURRENT unresolved DNS failure' <<'JSON'
[
  {"id":"a","text":"DNS recovered; requests now succeed."},
  {"id":"b","text":"DNS lookup still fails before connection."}
]
JSON

Expected selection, with diagnostics omitted:

{"selected_ids":["b"],"review_ids":[],"complete":true}

For real batches, collect and filter in one call, before raw output reaches the conversation:

jev-filter exec --task 'Find unresolved network failures' \
  --analysis analysis.json -- your-collector --json

Start with the copyable analysis contract. Parse the packet even on exit 2: complete=false and review_ids need attention. Small examples teach the interface; exact paths, IDs, selectors, calculations and short outputs usually belong in native tools. Installation, keys and result handling →

Practical tutorials

Shortened. The full README is on GitHub.