witness-mcp
Enables to interact with job application workflows through MCP, allowing users to find jobs, generate non-trivial applications with proof-maps, and build offline dashboards, all without auto-submitting.
README
Witness
Witness does not auto-apply to jobs. It never submits anything for you — there is no submit button in the codebase. It does the opposite of a spam bot: it makes each application fewer, better, and unfakeable, then hands it to you to send.
An AI job tool that refuses to lie for you — and refuses to hit submit for you.
It pulls a company's real application questions (from public Greenhouse/Ashby APIs, not a guessed form), traces every claim in your draft back to a real line in your CV (a proof-map — over-claiming is a bug it catches, not a feature it maximizes), scores drafts for genericness before you ever see them, and writes it all to an offline dashboard you click through yourself. Built for Claude Code (MCP + skills) and as a CLI.

Adversarially tested against 40 failure modes. If a company's real form can't be loaded, you see a
fetch_failed flag and a red banner — Witness does not fake a form and call it done.
Why it's different
- Real ATS fields, not guesses. Greenhouse + Ashby return the exact questions a company asks (custom ones included). Others fall back to a standard set — and say so. Fetch failures are surfaced, never hidden.
- Every claim has a receipt. The proof-map binds each statement in a draft to a real fact in your CV; an honesty check flags anything unbacked. (AI-inflated resumes now get detected ~76% of the time and auto-rejected by half of hiring managers — Witness is built so you're never the one caught.)
- You submit, it never does. Human-in-the-loop is an enforced design contract (see
TRADEMARK.md/CONTRIBUTING.md), not an MVP gap. No auto-apply, no proxy/CAPTCHA bypass, no fake accounts — ever.
Plus a non-trivial layer nothing else does: it reframes a role onto the axis where you win and drafts a short behavioural teardown of the company's own product — a witness only you could produce.
Your data folder
Witness is local and bring-your-own-data. Point it at a folder with your CV-derived profile; outputs are written back into that same folder. Nothing personal lives in this repo.
my-jobsearch/
profile.json # from `parse-cv`, or copy examples/profile.example.json
config.json # optional; copy examples/config.example.json
output/jobs.json # written by Witness
applications/*.json # written by Witness (+ a non-trivial kit each)
dashboard.html # written by Witness — open it or serve on localhost
Quickstart (60 seconds)
python3 -m venv .venv && source .venv/bin/activate # Python 3.10+
pip install -r requirements.txt
export ANTHROPIC_API_KEY=… # optional (LLM CV-parsing + drafted answers)
D=~/my-jobsearch
python -m src.cli --data-dir $D parse-cv --cv ~/MyCV.pdf # CV -> profile.json
python -m src.cli --data-dir $D scrape # -> output/jobs.json
python -m src.cli --data-dir $D apply --url "<job_url>" # -> applications/<company>.json (+ kit)
python -m src.cli --data-dir $D dashboard # -> dashboard.html
python -m http.server -d $D 8899 # then open http://localhost:8899/dashboard.html
Claude Code (MCP + skills)
Add the server from .mcp.json and set JOBSEARCH_DATA to your folder, then just ask Claude:
"find jobs", "apply to <link>", "make this application non-trivial", "build my dashboard". The MCP
server (src/mcp_server.py) exposes get_profile, read_jobs, refresh_jobs, get_application_fields,
generate_application, make_nontrivial, score_triviality, build_dashboard, list_companies. Skills
live in .claude/skills/.
Architecture
CV ─parse_cv─▶ profile.json ─┐
job boards ─scrape_jobs(JobSpy)─▶ output/jobs.json
job URL ─ats(Greenhouse/Ashby public APIs)─▶ exact fields ─┐
generate_application ─┴─▶ applications/*.json (+ nontrivial kit)
build_dashboard ─▶ dashboard.html (offline, copy buttons)
Tests
python -m tests.adversarial # offline (40 checks)
python -m tests.adversarial --net # + live ATS probes
Using it responsibly
Job discovery uses JobSpy to read public listings. LinkedIn, Naukri, Indeed and others prohibit automated access in their Terms of Service. Witness makes no warranty of ToS compliance; whether and how you run discovery is your own decision and responsibility. A conservative delay is on by default — keep it, and don't run Witness as a scheduled/always-on service. Full terms in DISCLAIMER.md. Design invariants (no auto-submit, no circumvention) in CONTRIBUTING.md.
License
Apache-2.0. The name and its human-in-the-loop guarantee are covered by TRADEMARK.md — fork the code freely under a different name; don't ship a guardrail-stripped build as "Witness".
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。