Server data from the Official MCP Registry
Search current startup jobs and hiring companies through RocketList's public read-only data.
About
Search current startup jobs and hiring companies through RocketList's public read-only data.
Remote endpoints: streamable-http: https://rocketlist.ai/mcp
Security Report
This is a data aggregation and export utility for public startup job listings. The codebase is well-structured with proper input validation, secure data handling practices, and an explicit allowlist/audit mechanism to prevent sensitive data leakage. Network access is appropriate for the stated purpose (fetching from public APIs), and there are no authentication bypass vulnerabilities or malicious patterns. Minor code quality observations exist but do not raise security concerns. Supply chain analysis found 1 known vulnerability in dependencies (0 critical, 1 high severity).
7 files analyzed · 4 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Required
This plugin requests these system permissions. Most are normal for its category.
How to Install & Connect
Available as Local & Remote
This plugin can run on your machine or connect to a hosted endpoint. during install.
Documentation
View on GitHubFrom the project's GitHub README.
RocketList Startup Jobs
The open data layer for startup hiring. Updated every day by RocketList.
Live roles from funded startups around the world, normalized into one documented dataset for job seekers, researchers, developers, and AI agents.
98,336 active jobs · 7,195 companies · generated 2026-09-20 23:31:05 UTC
Top locations in this snapshot: AB: 1 · AL: 1 · AU: 18 · Albania: 2 · Algeria: 8.
Download
| Dataset | Parquet | JSONL | CSV |
|---|---|---|---|
| Jobs | jobs.parquet | jobs.jsonl.gz | jobs.csv.gz |
| Companies | companies.parquet | companies.jsonl.gz | companies.csv.gz |
The full snapshot lives in the stable latest release, not Git history. SHA-256 checksums, generation metadata, and the complete audit result ship beside every snapshot.
wget https://github.com/rocketlist-ai/startup-jobs/releases/latest/download/jobs.parquet
import pandas as pd
jobs = pd.read_parquet(
"https://github.com/rocketlist-ai/startup-jobs/releases/latest/download/jobs.parquet"
)
berlin_ai = jobs[
jobs["city"].fillna("").str.contains("Berlin", case=False)
& jobs["category"].fillna("").str.contains("AI|Data|Engineering", case=False)
]
print(berlin_ai[["company_name", "title", "url"]].head(20))
What is included
The jobs dataset contains factual discovery metadata: company, title, normalized role and seniority, location, compensation when explicitly available, skills, canonical application URL, source platform, and first/last-seen timestamps. The companies dataset adds stage, funding, investors, industry, headquarters, and careers URLs where available.
Schemas are versioned in schema/jobs.schema.json and schema/companies.schema.json. A browsable 100-record sample is committed under sample/, while daily aggregate changes live under changes/. See the public methodology and quality checks for provenance, denominators, audit guarantees, and limitations.
What is deliberately excluded
- Full job descriptions or copied HTML
- Raw ATS responses and crawler payloads
- Embeddings, prompts, traces, or enrichment internals
- Candidate, account, saved-job, application, or matching data
- RocketList's ranking and recommendation logic
The exporter uses an explicit allowlist and the audit fails if a forbidden field appears.
Use it with agents
Install the user-facing RocketList skill in Claude Code, Codex, Cursor, or another compatible agent:
npx skills add rocketlist-ai/startup-jobs --skill rocketlist-job-search
It searches and filters the current snapshot, handles CV-to-role matching, and links users directly to applications. The bundle is also readable at skills/rocketlist-job-search, so any agent can follow it without an installer.
The accompanying distribution loop explains how the dataset, skill, search pages, and recurring data stories compound into discoverability and traffic.
For bulk analysis, give an agent the Parquet URL and the relevant schema. For lower-latency conversational search, connect the RocketList MCP when available:
https://rocketlist.ai/mcp
Its official MCP Registry manifest is versioned in server.json.
Example prompt:
Use the RocketList dataset to find active Series A–C companies in Berlin hiring product managers. Return the canonical application links and explain the filters you applied.
Update model
The workflow runs daily at 04:17 UTC:
- Fetch all current public catalog rows through paginated API reads.
- Normalize join keys, field types, lists, and stable public IDs.
- Retain active, non-duplicate jobs and active or referenced companies.
- Cross-foot totals, reconcile API counts, verify unique IDs and URLs, test company references, and reject forbidden fields.
- Replace the full assets on the stable
latestrelease. - Commit only small samples, statistics, and daily change summaries.
No credentials are required to reproduce the export:
python -m pip install -r requirements.txt
python scripts/export_dataset.py
python scripts/audit_dataset.py
Accuracy and limitations
RocketList aggregates company career pages and ATS sources. A listed role can close between daily refreshes; the canonical application page is authoritative. Coverage varies by employer, country, and field, and missing values are never imputed for public statistics. See stats/latest.json for field-level denominators.
License and attribution
The dataset is available under ODC-BY 1.0; code is MIT licensed. Attribute RocketList with links to https://rocketlist.ai and this repository. Employer names and trademarks belong to their respective owners, and source postings remain subject to their publishers' terms.
Corrections
Open an issue for a missing company, stale role, broken URL, or schema problem. See CONTRIBUTING.md for the data-safety rules.
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