Jev Filter

Name: Jev Filter
Author: apixly-ai

作者：apixly-ai版本 0.4.2i★ –i在你的电脑上运行

注册名称io.github.apixly-ai/jev-filter

安装

概览

作者提供的说明（英文原文）

Read-only semantic filtering, code search, log triage and traceable evidence for AI agents.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 在你的电脑上运行
传输方式: stdio
分类: 搜索与 AI 记忆
你需要填写：TYPESAFE_API_KEY

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
需要你自己填写： TYPESAFE_API_KEY

标为机密的值是你账户的钥匙。千万不要分享给别人，也不要粘贴到聊天里。

你需要

npm已安装 Node.js（用于 npx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "jev-filter": {
      "command": "npx",
      "args": [
        "-y",
        "@apixly/jev-filter",
        "mcp",
        "--root",
        "{workspace}"
      ],
      "env": {
        "TYPESAFE_API_KEY": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add jev-filter --env TYPESAFE_API_KEY=your-value -- npx -y @apixly/jev-filter mcp --root '{workspace}'

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "jev-filter": {
      "command": "npx",
      "args": [
        "-y",
        "@apixly/jev-filter",
        "mcp",
        "--root",
        "{workspace}"
      ],
      "env": {
        "TYPESAFE_API_KEY": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "jev-filter": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@apixly/jev-filter",
        "mcp",
        "--root",
        "{workspace}"
      ],
      "env": {
        "TYPESAFE_API_KEY": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

这个服务器在你的电脑上运行，而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上，无需账户。

需要你填写的值

TYPESAFE_API_KEY 机密必填 env
Your TypeSafe Jev API key. Inference calls are billable; do not publish the key.

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

它在你的电脑上运行；安装后，工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 apixly-ai 验证，该账户拥有此仓库
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
– 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
– 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
… 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行；我们只能读取互联网端点的工具列表

与“搜索与 AI 记忆”中的其他服务器比较i

星标数多于此分类中其他 7277 个服务器的 0%
发布到注册中心的时间比此分类中其他 7277 个服务器的 72% 更新
基础检查结果等于或优于此分类中其他 7277 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

Jev Filter 安全吗？

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

你需要填写：TYPESAFE_API_KEY

它在我的电脑上运行还是在云端？

它在你的电脑上运行。你的 AI 应用会在需要时启动它，数据不会经过作者的服务器（除非它自己调用在线服务）。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（0.4.2）于15天前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。

有什么可以替代它？

同一分类中有：Context7, Codebase Memory, Exa

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Context7

作者：upstash

Up-to-date docs for AI agents

电脑或云端★ 64,224MIT昨天更新

安装

Codebase Memory

作者：DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

在你的电脑上运行★ 48,148MIT21天前更新

安装

Exa

作者：exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

在云端运行★ 5,213MIT上个月更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

简体中文 · Quick start · Documentation · Agent setup · Benchmarks

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.

Discover and connect: View the MCP Servers listing ↗ · Set up a client and four read-only tools →

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

Filter search results for RAG and AI agents: select passages against explicit evidence requirements before they enter the main model's context.
Search code by behavior: combine complete code symbols, optional bounded recall and semantic judgment.
Triage correlated logs: preserve request history and separate current failures from recovered events.

Copyable synthetic inputs and analysis contracts are included. Read the tutorials on the documentation site · FAQ.

Measured advantages

Paid live evidence from 2026-10-04, using synthetic inputs. Source hashes, per-run usage, timing and failures are public; each result belongs to its stated scope.

Returned tool context: 91.6–97.2% less across 24 real agent runs. Filtered completion was 12/12, raw completion 11/12, and all 24/24 ID sets were correct.
Same-output selection: Jev's batched API path screened 96 records in a median 1.313 s, versus 13.421 s for Luna and 14.543 s for Astra through signed-in Codex CLI. All 9/9 runs selected the same 48 IDs; CLI startup is included, and no primary-agent continuation follows Jev.
Jev-stage batching: 2 requests instead of 96, 56.5% fewer input tokens and 2.58× filtering speed versus single-record parallel Jev calls. This measures the Jev stage, not a whole-agent speedup.

Latency and cost vary. Five of six whole-operation cells were slower by 0.1–18.4%; cold API-equivalent cost ranged from 23.2% lower to 1.1% higher. Subscription billing remains unknown. Smaller context does not guarantee lower latency or billing.

Methods, negative results and reproduction · Per-run whole-operation data

Selection paths, measured results and scope

Fast semantic selection, checked against the same expected answer. All paths screened 96 records against two semantic conditions and returned exactly the same 48 matching IDs.

Measured path	Median screening time	Exact complete runs
Jev `jev-1.13.0` · auto-batched API	1.313 s	3/3
Luna `gpt-5.6-luna` · signed-in Codex CLI	13.421 s	3/3
Astra `gpt-6-astra` · signed-in Codex CLI	14.543 s	3/3

This compares available execution paths, including CLI startup and the primary-agent turn; native model latency is not isolated. Primary paths use medium reasoning and no tools. Three repetitions are a smoke sample, not a whole-agent speedup or workload guarantee. Per-run evidence · Method and reproduction

Advantage	Observed result	Scope and trade-off
Get repeated judgments sooner	2.58× filtering speed: 3.215 → 1.246 s, 61.2% less elapsed time; 56.5% fewer Jev input tokens.	Single-record parallel vs automatic batch-parallel Jev, 96 synthetic records × two predicates, three repetitions per arm; all 12/12 runs exact. Jev stage only, parallel worker cap 12; not a primary-model speed comparison. Data
Keep the agent's context focused	91.6–97.2% less returned tool context; filtered completion 12/12, raw 11/12; all 24/24 ID sets correct.	24 real agent runs, three synthetic scenarios, two primary models, two repetitions per arm. Five/six cells slower by 0.1–18.4%; cold API-equivalent cost ranged from 23.2% lower to 1.1% higher. Subscription billing remains unknown. Data
Recover more relevant code	Correct matches 4/12 → 10/12, zero false positives.	Exact vs opt-in recall, both with native semantic filtering. Returned context grew 5,284 → 9,706 bytes; absent lexical overlap still misses. Data
Inspect the change that matters	All 4/4 target changes selected in each repetition; returned context 23.9% lower than retaining every file.	Eight synthetic files, two repetitions. Semantic triage adds model usage and increases median whole-operation time 503 → 1,465 ms. Data
Connect real interfaces	13/13 paid E2E checks pass, including exact MCP evidence recovery and 64/64 survey topic labels.	Python CLI, fresh npm 0.4.0 JS/native package, official MCP, local Chromium extraction and survey. Small synthetic fixtures; resolved model identity is unavailable from these public adapters. Data
Reach inside a scroll container	Verified nested-scroll completion 0/3 → 3/3 under the same strict 0.55 / 0.10 policy.	Pinned browser source A/B; program adds observed container evidence. Six ordinary goal runs still need review. More successful work consumes more calls, context and time. Data

Whole-operation chart and reproduction

Method and all live results · Per-run JSON · CSV · Reproducible driver

Earlier evidence: offline contracts, batching and open-web limitations

Experiment	Dated result	Scope
Offline release contracts · 2026-10-04	3/13 → 13/13 expected behaviors; 3 → 0 false fixture actions.	Scripted responses, no model calls; reviews and diagnostic context increase. Data
Jev batching · 2026-09-22	56.5% fewer input tokens; batch + parallel 32.4% faster than single-record parallel.	96 synthetic records × two predicates, two repetitions; Jev stage only. Data
Historical whole operation · 2026-09-23	88–97% less returned tool context.	48 runs over four synthetic scenarios; cost and latency vary. Data
Historical hosted fixtures · 2026-09-30	Browser 15/15 Camofox, 18/18 CDP; desktop 12/12.	Local fixtures under the then-current planner policy; not a current strict-policy or open-web completion guarantee. Data
Public websites · 2026-09-30	16/48 read-only goals reached; 16/34 after excluding access walls and provider failures.	24 goals in one environment. Custom widgets and verification were weak. Data

Full methods and historical data. The latest browser experiments retain failed pruning variants and unknown provider attempts; they do not turn a safe stop into a completed goal.

Watch the program turn a goal into a verified result

<img src="docs/assets/showcase/browse.gif" alt="Recorded synthetic shop run: Jev selects observed controls, the program pauses before placing an order, and verification follows confirmation" width="100%"

只显示了开头部分；其余内容请在仓库中阅读

Jev Filter

概览

PopNx MCP 的三大支柱

它能帮你做什么

安装

安装之前

需要你填写的值

它能做什么

基础检查i

作者与仓库一致

仍在维护

有许可证

端点有响应

使用 OAuth 登录

工具描述中没有隐藏指令

与“搜索与 AI 记忆”中的其他服务器比较i

常见问题

替代选择

Context7

Codebase Memory

Exa

作者的 README

Jev Filter

Quick start

Practical tutorials

Measured advantages

Watch the program turn a goal into a verified result