Grad Agent
БесплатноНе проверенAn autonomous MCP agent that helps you apply to fully funded MS and PhD programs by discovering professors, verifying faculty status, matching projects, and dra
Описание
An autonomous MCP agent that helps you apply to fully funded MS and PhD programs by discovering professors, verifying faculty status, matching projects, and drafting cold emails.
README
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 askippedsheet 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
MISMATCHif 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_reportshows 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
Runs as a stdio MCP server for Claude Code / Claude Desktop / any MCP client, or as a plain CLI.
Published on:
- PyPI: grad-agent
- MCP Registry: io.github.i-ninte/grad-agent
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
[email protected]
SMTP_PASSWORD=<gmail app password>
[email protected]
# 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,portfoliocv_path,transcript_path(absolute paths)degree_status: bachelors | masters(drives PhD eligibility gating)target_term,target_degreeresearch_areas: [nlp, ai4health, ...]target_regions: [US, Canada]— only draft for profs in these regions (empty = anywhere)seed_projects:3 to 10 flagship projects withname,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
| Tool | Purpose |
|---|---|
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_mark |
Recommendation-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 rejected. Every skip is persisted to the
skippedsheet inoutreach_log.xlsxwith the stage (identity, faculty-gate, region, dedup, no-papers) and the exact mismatch — not buried in old review emails. - Exceed Semantic Scholar's rate limit. Client-side throttle enforces ≥1.1s between requests, with exponential backoff on any 429 or 5xx.
- Draft for programs you are ineligible for. If your
degree_statusisbachelors, PhD programs that require an MSc first are filtered out. Same gate for scholarships outside your eligibility region.
Requirements
- Python 3.10+
- macOS, Linux, or Windows (all three tested in CI on 3.10 / 3.11 / 3.12)
pdflatexon PATH if you want SOP PDFs (macOS: MacTeX; Ubuntu:texlive-latex-recommended; Windows: MiKTeX)- Anthropic API key
- Gmail (or another SMTP) for the review-mailer
Where your data lives
Everything is under ~/.grad-agent/ by default, or $GRAD_AGENT_HOME if set:
~/.grad-agent/
profile.yaml identity + preferences (regions, seed projects, ...)
programs.yaml target programs with eligibility rules + deadlines
scholarships.yaml external scholarships with region eligibility + deadlines
.env secrets (gitignored)
data/
outreach_log.xlsx outreach sheet (drafts + outcomes) + skipped sheet (audit trail)
lor_log.xlsx recommendation-letter tracker
catalog.json synced projects (GitHub + HF + local)
s2_cache.json Semantic Scholar lookups (14 day TTL)
region_cache.json LLM-inferred regions for unusual affiliations
db.sqlite drafts + status
drafts/ per-school SOP versions + email drafts
prep/ interview prep one-pagers
Contributing
MIT licensed. PRs welcome for: more program templates, non-Gmail SMTP presets, non-arXiv source adapters, and better prof-verification heuristics.
Author
Kwabena Obeng · i-ninte.github.io/portfolio/
Установка Grad Agent
У этого сервера нет опубликованного пакета — он собирается из исходников. Открой репозиторий и следуй инструкции в README.
▸ github.com/i-ninte/grad-agentFAQ
Grad Agent MCP бесплатный?
Да, Grad Agent MCP бесплатный — установка в пару кликов через Unyly без оплаты.
Нужен ли API-ключ для Grad Agent?
Нет, Grad Agent работает без API-ключей и переменных окружения.
Grad Agent — hosted или self-hosted?
Self-hosted: сервер запускается локально на твоей машине командой из раздела установки.
Как установить Grad Agent в Claude Desktop, Claude Code или Cursor?
Открой Grad Agent на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.
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