프로토타입 · 미리 보기: 공식 레지스트리의 모든 서버를 담고 있으며 30분마다 동기화해요. 리뷰와 계정 기능은 다음 단계에서 열려요.
PopNx MCPAI를 쓰는 사람을 위한 MCP 서버 · 쉬운 말로 설명해요

grad-agent

제작자: i-ninte버전 0.2.9i★ –i내 컴퓨터에서 실행
레지스트리 이름io.github.i-ninte/grad-agent

설치

개요

제작자가 쓴 설명(영어 원문)

Autonomous MCP agent for grad school applications: prof discovery, cold emails, SOP, tracking.

PopNx MCP의 세 기둥

이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.

할 수 있는 일

이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.

  • 연결 방식: 내 컴퓨터에서 실행
  • 전송 방식: stdio
  • 카테고리: 기타
  • 입력해야 할 값: ANTHROPIC_API_KEY, SMTP_SERVER, SMTP_PORT, SMTP_USERNAME, SMTP_PASSWORD, SMTP_FROM, GITHUB_TOKEN, HF_TOKEN

설치

쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.

설치하기 전에

  • 기본 점검: 1개 중 1개 통과
  • 내 컴퓨터에서 내 사용자 계정의 권한(파일, 네트워크, 앱)으로 실행돼요.
  • 직접 입력할 값: ANTHROPIC_API_KEY, SMTP_SERVER, SMTP_PORT, SMTP_USERNAME, SMTP_PASSWORD, SMTP_FROM, GITHUB_TOKEN, HF_TOKEN, GRAD_AGENT_HOME, LOR_LETTERHEAD_PATH, LOR_LETTERHEAD_WIDTH

비밀로 표시된 값은 내 계정의 열쇠예요. 절대 다른 사람과 공유하거나 채팅에 붙여 넣지 마세요.

필요한 것

  • PyPIPython의 uv 설치(uvx 명령어용)

어떤 앱을 쓰세요?

위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.

Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요

{
  "mcpServers": {
    "grad-agent": {
      "command": "uvx",
      "args": [
        "grad-agent"
      ],
      "env": {
        "ANTHROPIC_API_KEY": "your-value",
        "SMTP_SERVER": "your-value",
        "SMTP_PORT": "your-value",
        "SMTP_USERNAME": "your-value",
        "SMTP_PASSWORD": "your-value",
        "SMTP_FROM": "your-value",
        "GITHUB_TOKEN": "your-value",
        "HF_TOKEN": "your-value",
        "GRAD_AGENT_HOME": "your-value",
        "LOR_LETTERHEAD_PATH": "your-value",
        "LOR_LETTERHEAD_WIDTH": "your-value"
      }
    }
  }
}

터미널에서 이 명령어를 실행하세요

claude mcp add grad-agent --env ANTHROPIC_API_KEY=your-value --env SMTP_SERVER=your-value --env SMTP_PORT=your-value --env SMTP_USERNAME=your-value --env SMTP_PASSWORD=your-value --env SMTP_FROM=your-value --env GITHUB_TOKEN=your-value --env HF_TOKEN=your-value --env GRAD_AGENT_HOME=your-value --env LOR_LETTERHEAD_PATH=your-value --env LOR_LETTERHEAD_WIDTH=your-value -- uvx grad-agent

실행하기 전에 빈 값을 채워 주세요.

Cursor에서 열기

또는 설정을 직접 복사하기

프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요

{
  "mcpServers": {
    "grad-agent": {
      "command": "uvx",
      "args": [
        "grad-agent"
      ],
      "env": {
        "ANTHROPIC_API_KEY": "your-value",
        "SMTP_SERVER": "your-value",
        "SMTP_PORT": "your-value",
        "SMTP_USERNAME": "your-value",
        "SMTP_PASSWORD": "your-value",
        "SMTP_FROM": "your-value",
        "GITHUB_TOKEN": "your-value",
        "HF_TOKEN": "your-value",
        "GRAD_AGENT_HOME": "your-value",
        "LOR_LETTERHEAD_PATH": "your-value",
        "LOR_LETTERHEAD_WIDTH": "your-value"
      }
    }
  }
}

브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.

VS Code에서 열기

또는 설정을 직접 복사하기

프로젝트의 .vscode/mcp.json에 붙여 넣으세요

{
  "servers": {
    "grad-agent": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "grad-agent"
      ],
      "env": {
        "ANTHROPIC_API_KEY": "your-value",
        "SMTP_SERVER": "your-value",
        "SMTP_PORT": "your-value",
        "SMTP_USERNAME": "your-value",
        "SMTP_PASSWORD": "your-value",
        "SMTP_FROM": "your-value",
        "GITHUB_TOKEN": "your-value",
        "HF_TOKEN": "your-value",
        "GRAD_AGENT_HOME": "your-value",
        "LOR_LETTERHEAD_PATH": "your-value",
        "LOR_LETTERHEAD_WIDTH": "your-value"
      }
    }
  }
}

브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.

이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.

선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.

직접 입력할 값

  • ANTHROPIC_API_KEY 비밀 필수 env
    Anthropic API key used by Claude Haiku for hook synthesis and verification.
  • SMTP_SERVER 필수 env
    SMTP host for the review mailer.
  • SMTP_PORT 필수 env
    SMTP port.
  • SMTP_USERNAME 필수 env
    SMTP username; used as From address by default.
  • SMTP_PASSWORD 비밀 필수 env
    SMTP password. Gmail App Password recommended.
  • SMTP_FROM 필수 env
    Envelope From address for drafts emailed to the user's review inbox.
  • GITHUB_TOKEN 비밀 env
    Optional. Personal access token used to enumerate the user's public and private GitHub repos during catalog sync.
  • HF_TOKEN 비밀 env
    Optional. HuggingFace token used to enumerate the user's published models and datasets during catalog sync.
  • GRAD_AGENT_HOME env
    Optional. Override the default ~/.grad-agent/ location for profile, programs, secrets, and data.
  • LOR_LETTERHEAD_PATH env
    Optional. Absolute path to a letterhead / company logo image (.png / .jpg / .pdf) embedded at the top of Letters of Recommendation during LaTeX compile. Leave unset for a plain top block. Can also be…
  • LOR_LETTERHEAD_WIDTH env
    Optional. LaTeX length for the letterhead image width (e.g. '2.5in'). Used only when LOR_LETTERHEAD_PATH is set.

이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.

할 수 있는 일

내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.

기본 점검i

누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 7일 점검

  • ✓ 통과

    제작자와 저장소 일치

    레지스트리 이름이 GitHub 계정 i-ninte 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요

  • … 아직 점검 안 함

    계속 관리되고 있음

    아직 GitHub에서 이 저장소를 읽어 오지 않았어요

  • … 아직 점검 안 함

    라이선스 있음

    아직 GitHub에서 이 저장소를 읽어 오지 않았어요

  • – 해당 없음

    엔드포인트 응답

    내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요

  • – 해당 없음

    OAuth로 로그인

    내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요

  • … 아직 점검 안 함

    도구 설명에 숨겨진 지시 없음

    내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요

기타 카테고리의 다른 서버와 비교i

  • 이 카테고리의 다른 서버 32623개 중 0%보다 스타가 많아요
  • 이 카테고리의 다른 서버 32623개 중 49%보다 최근에 레지스트리에 공개됐어요
  • 기본 점검 결과가 이 카테고리의 다른 서버 32623개 중 100%와 같거나 더 좋아요

레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.

자주 묻는 질문

grad-agent, 안전한가요?

해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.

어떤 키나 코드가 필요한가요?

입력해야 할 값: ANTHROPIC_API_KEY, SMTP_SERVER, SMTP_PORT, SMTP_USERNAME, SMTP_PASSWORD, SMTP_FROM, GITHUB_TOKEN, HF_TOKEN

내 컴퓨터에서 실행되나요, 클라우드에서 실행되나요?

내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).

아직 관리되고 있나요?

GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.2.9)은 42일 전 레지스트리에 올라왔어요.

어떤 앱에서 쓸 수 있나요?

Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Python (uv)도 있어야 해요.

대신 쓸 수 있는 건 뭐가 있나요?

같은 카테고리에는: Spec Driven Development, Remote Desktop Commander, Draugr

대안

같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.

카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i

Spec Driven Development

제작자: formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

내 컴퓨터에서 실행★ 440MIT14개월 전 업데이트
설치

Remote Desktop Commander

제작자: desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

클라우드에서 실행★ 211그저께 업데이트
설치

Draugr

제작자: draugr-dev

Security scanning for AI agents: SAST, SCA, secrets, IaC, DAST, ranked by real risk.

내 컴퓨터에서 실행오늘 업데이트
설치

제작자의 README

README 열기

제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.

grad-agent

An autonomous MCP agent that helps you apply to fully funded MS and PhD programs.

Discover and draft

  • Discovers professors on arXiv in your research areas, or deadline-driven per program (run_program_batch)
  • Resolves each professor's identity by anchoring on the trigger paper's canonical Semantic Scholar authorId, not the name string — name collisions like "Wei Zhang" are structurally impossible to confuse
  • Refuses to draft when identity cannot be anchored; the specific mismatch (e.g. identity ambiguous: 2 comparable candidates, h=40 vs h=38) is persisted to a skipped sheet for audit
  • Verifies each candidate is actually faculty (h-index and paper-count gates)
  • Filters by region (target_regions: [US, Canada] in your profile; keyword table + homepage TLD + LLM fallback)
  • Scrapes their lab page for a recruiting signal + email address
  • Matches them to your strongest shipped project (tag overlap + TF-IDF semantic layer + learned response-rate bias)
  • Drafts a specific, fact-checked cold email: Claude Haiku writes a hook spanning the prof's recent papers, then a second call verifies every claim against the abstracts and rewrites anything unsupported
  • Scores each draft 1 to 10 for fit, with a one-line reason, so you can triage in seconds
  • Freshness warnings on every draft: cross-checks the S2 affiliation against the prof's live homepage (flags MISMATCH if they may have moved labs) and flags researchers who have not published in 2+ years

Learn and follow through

  • Detects professor replies via read-only IMAP and tags the log automatically
  • Learns from outcomes: projects that earn replies rank up in future matching (outcome_report shows what works)
  • Drafts follow-up nudges for profs silent 10+ days; never nudges the same prof twice
  • Generates an interview prep one-pager when a prof replies: their papers summarised, likely questions, your talking points

Track everything

  • Compiles per-school SOPs to PDF (LaTeX), versioned so no draft is ever overwritten
  • Tracks outreach, LOR requests, program deadlines, and external scholarships (Mastercard, Commonwealth, Fulbright, Rhodes, and more) in xlsx/yaml
  • Emails every draft to your inbox for review; nothing is ever sent to a professor without you

Bundled Claude Code / Claude Desktop skills

Installing grad-agent ships a set of Anthropic Skills you can invoke from any Claude client that supports them (Claude Code, Claude Desktop, MCP-aware IDEs). grad-agent init symlinks them into ~/.claude/skills/ automatically; to (re)install without running init, use grad-agent install-skills (add --force to replace existing entries, or --target to link elsewhere).

  • `sop-writing` — rules for MS/PhD Statement of Purpose drafting, synthesized from MIT CommLab, MIT CAPD, Berkeley Grad Div, Harvard GSAS, Stanford, and CS-faculty guides. Pairs with the LaTeX SOP compile step.
  • `lor-writing` — rules for letters of recommendation, including the common case where the applicant ghost-drafts a letter for a recommender to sign. Encodes multi-letter differentiation, recommender realism, HHMI bias hygiene, and a banned-phrase regex sweep. Supports an optional letterhead / company logo via the LOR_LETTERHEAD_PATH env var (with LOR_LETTERHEAD_WIDTH, default 2.2in) so the LaTeX compile drops your image into the top of the letter — leave it unset for a plain top block the recommender can paste onto their own letterhead. grad-agent init will prompt for the letterhead path interactively; to change it later (or swap letterheads between recommenders) run grad-agent set-letterhead /path/to/image.png --width 2.5in (pass no path to clear it).
  • Cold-email drafter (grad_agent/drafters/cold_email.py) — rewritten around PhD outreach best-practices (specific paper anchor, one concrete overlap, no un-anchored superlatives, no "I read your paper" claims).

Runs as a stdio MCP server for Claude Code / Claude Desktop / any MCP client, or as a plain CLI.

Published on:

Install

Pick one:

# Recommended for MCP clients (Claude Desktop, Claude Code, etc.)

uvx grad-agent server # single-shot, no persistent install

Persistent CLI install

pipx install grad-agent

Or in a venv

python3 -m venv .venv && source .venv/bin/activate pip install grad-agent

Set up in 5 minutes

grad-agent init

This writes:

  • ~/.grad-agent/profile.yaml — your identity, projects, preferences
  • ~/.grad-agent/programs.yaml — target programs (seeded)
  • ~/.grad-agent/scholarships.yaml — external scholarships with deadlines (seeded)
  • ~/.grad-agent/.env — secrets template

Fill in ~/.grad-agent/.env:

ANTHROPIC_API_KEY=sk-ant-...

SMTP_SERVER=smtp.gmail.com # presets for Outlook/Yahoo/Zoho in the template SMTP_PORT=587 SMTP_USERNAME=you@gmail.com SMTP_PASSWORD= SMTP_FROM=you@gmail.com

Optional:

S2_API_KEY= # free Semantic Scholar key, dedicated rate limits

(client-side throttle already enforces >=1.1s between requests)

IMAP_SERVER=imap.gmail.com # read-only reply detection; defaults to SMTP creds GITHUB_USERNAME=your-gh GITHUB_TOKEN=github_pat_... HF_USERNAME=your-hf

Fill in the important bits of ~/.grad-agent/profile.yaml:

  • name, identity_line, portfolio
  • cv_path, transcript_path (absolute paths)
  • degree_status: bachelors | masters (drives PhD eligibility gating)
  • target_term, target_degree
  • research_areas: [nlp, ai4health, ...]
  • target_regions: [US, Canada] — only draft for profs in these regions (empty = anywhere)
  • seed_projects: 3 to 10 flagship projects with name, pitch, link, tags

Profile edits apply immediately, even while a long-running MCP session is open.

Then:

grad-agent sync        # scan projects (GitHub + HF + local)

grad-agent run # one batch, drafts land in your inbox

Register with Claude Code

Three commands, in order:

pipx install grad-agent

pipx ensurepath # macOS/Linux: opens ~/.local/bin on PATH

Windows: opens %USERPROFILE%\.local\bin on PATH

claude mcp add grad-agent grad-agent server

On Windows you may need to open a new PowerShell or Terminal window after pipx ensurepath for the PATH change to take effect.

Prefer a zero-install one-liner? Skip pipx and use uvx:

claude mcp add grad-agent uvx grad-agent server

Then in a new Claude Code session:

/mcp

You should see grad-agent connected with ~36 tools. Before it does anything useful, run grad-agent init (or uvx grad-agent init) and fill in ~/.grad-agent/.env and ~/.grad-agent/profile.yaml as described in the setup section above.

If you skipped pipx ensurepath, grad-agent register-claude prints an absolute-path variant of the command that works without PATH changes.

Register with Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json:

{

"mcpServers": { "grad-agent": { "command": "uvx", "args": ["grad-agent", "server"] } } }

The uvx command needs no prior install. If you already ran pipx install grad-agent, you can use "command": "grad-agent", "args": ["server"] instead.

Any other MCP client (Cursor, Zed, Windsurf) uses the same manifest shape; just point them at uvx grad-agent server.

Daily autonomous run

The package ships scheduler templates for all three OSes. grad-agent schedule emits the right one for your platform:

grad-agent schedule --dest .

Then follow the install instructions the command prints. In case you want them up front:

macOS (launchd):

cp com.gradagent.daily.plist ~/Library/LaunchAgents/

launchctl load ~/Library/LaunchAgents/com.gradagent.daily.plist

Linux (systemd user timer, fires at 08:00 local):

mkdir -p ~/.config/systemd/user

cp grad-agent-daily.service grad-agent-daily.timer ~/.config/systemd/user/ systemctl --user daemon-reload systemctl --user enable --now grad-agent-daily.timer

Windows (Task Scheduler):

# In an elevated PowerShell prompt:

schtasks /Create /TN "grad-agent-daily" /XML .\grad-agent-daily.xml

Or import the XML via the Task Scheduler GUI (Action → Import Task).

Every morning: replies auto-detected via IMAP, follow-up nudges drafted for silent profs, 3 identity-verified faculty leads with fit scores (best fit first), hooks fact-checked against paper abstracts, freshness warnings when a prof's S2 record and live homepage disagree, plus program and scholarship deadline warnings — all in one review email. Skipped leads are persisted with the specific mismatch reason (view with skipped_log_view or open the skipped sheet).

What each MCP tool does

ToolPurpose
run_daily_batch(n, area)Full pipeline: verify → recruiting → hook + verify → draft → log
outreach_log_view(limit)Show last N rows of the outreach xlsx
outreach_mark_sent(prof, uni)Flag a row as actually sent to the prof
sync_catalog(source)Pull projects from github, hf, or local
list_projects_in_catalog()Show every project the matcher can see
list_programs()Your target programs
upcoming_deadlines(days)Any program deadline in the next N days
lor_add / lor_outstanding / lor_markRecommendation-letter tracker
run_program_batch(program_id, n)Deadline-driven batch: draft for one program's faculty
skipped_log_view(limit)Audit trail of skipped leads with the specific stage + mismatch reason
s2_cache_invalidate(query, all)Selectively purge Semantic Scholar cache entries by author name or id
followups_due(days)Drafted nudges for profs silent 10+ days
outreach_mark_followup(prof)Record a sent nudge (never nudged twice)
outreach_mark_response(prof, outcome)Tag replies; feeds the matcher's learning loop
outcome_report()Response rates by area and project
ingest_replies()Read-only IMAP scan; auto-tags replies in the log
interview_prep(prof)One-page brief: their papers, likely questions, your talking points
list_scholarships(region)External scholarships filtered by eligibility region
upcoming_scholarship_deadlines(days)Scholarship deadlines approaching
draft_cold_email(...)Manual per-prof draft
draft_sop(...)Compile a Columbia-style SOP PDF (versioned: sop_v1, v2, ...)
send_draft_to_me(path)Ship any draft file to your review inbox
discover_profs(area)arXiv + OpenReview scan (raw candidates, no verification)

Blog publishing tools (publish_article, update_article, ...) are gated behind blog.enabled: true in profile.yaml and are specific to the author's Turso-backed Next.js portfolio. Most users can ignore them.

What the agent will not do

  • Send any email to a professor. Every send is manual, from your Gmail, after you read the draft.
  • Touch your inbox beyond reading. IMAP access is read-only: it never sends, deletes, or marks messages.
  • Fabricate a paper claim. The hook goes through a second Claude call that rejects any claim not present in the abstracts, and rewrites.
  • Email the same professor twice. Deduplication is keyed on the Semantic Scholar authorId, with a name fallback for legacy rows, and follow-ups are marked so no prof is nudged more than once.
  • Draft for the wrong person when two profs share a name. Identity is resolved from the trigger paper's authorId, not the name string; ambiguous cases are refused and logged.
  • Hide why a lead was reject

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