scrapely-mcp
npm
v1.4.0
Published by deepblue6 — no publish provenance, so origin is unverified, but the source is public: the repository link below is self-declared yet readable, so you can inspect the code before adopting it.
The grade answers one question — how safe is this server for you to adopt — so it is computed in two auditable stages. Nothing below is an opinion or an LLM's guess; every line is a real term the deterministic engine applied, and the same input always yields the same number.
1. Threat score — 100 − 0 = 100. What the published surface and source actually contain:
The deterministic scan raised no scored threat in the surface it inspected — the threat score stayed at 100. Capability observations and advisory notes are recorded but never lower it.
2. Client adoption risk — 100 − 4 = 96. Three small, subtract-only factors that reflect your risk in adopting it — a clean scan proves less on a powerful, unverified or barely-inspectable package, so the grade says so plainly:
| Points | Adoption-risk factor |
|---|---|
| −3 | capability blast radius (moderate) — client exposure if the model is manipulated |
| −1 | publisher verification (public source) — no provenance, but the source is public and inspectable |
| ✓ | inspection depth (source) — how much of the target the scan could see |
Capability observations and info notes are shown under Findings but never scored.
Open any row's finding below for the file, line and evidence behind a deduction.
Untrusted-input tools ([get_scraping_source_leads, fetch_conversations]) co-exist with external-action tools ([schedule_tweet, cancel_scheduled_tweet]). A prompt injection could cause unwanted external actions, though no direct sensitive-data leak path was found.
Evidence: untrusted [get_scraping_source_leads, fetch_conversations] → sinks [schedule_tweet, cancel_scheduled_tweet]
Fix: Require confirmation for state-changing/egress actions triggered after processing untrusted content.
Location: flow get_scraping_source_leads → schedule_tweet
Each tool and what it can reach — statically extracted from the published source.
cancel_scheduled_tweetnetwork egressfetch_conversationsingests untrusted inputget_scraping_source_leadsingests untrusted inputschedule_tweetnetwork egressadd_accountno sensitive capabilitycheck_scraping_statusno sensitive capabilitycreate_scraping_sourceno sensitive capabilityenrich_emailno sensitive capabilityenrich_twitterno sensitive capabilityget_account_tagsno sensitive capabilityget_campaign_analyticsno sensitive capabilityget_campaign_detailno sensitive capabilityget_crm_datano sensitive capabilityget_enrichment_creditsno sensitive capabilityget_webhook_infono sensitive capabilitylaunch_campaignno sensitive capabilitylist_accountsno sensitive capabilitylist_campaignsno sensitive capabilitylist_scheduled_tweetsno sensitive capabilityremove_accountno sensitive capabilitysend_dmno sensitive capabilityset_account_tagsno sensitive capabilitytoggle_followups_pauseno sensitive capabilitytoggle_pauseno sensitive capabilitytoggle_warmupno sensitive capabilityupdate_account_tagsno sensitive capabilityupdate_cookiesno sensitive capabilityupdate_crmno sensitive capabilityupdate_proxyno sensitive capabilityCross-tool combinations that form a data-exfiltration primitive (untrusted input → sensitive source → external sink).
Scan history per published version. The engine is deterministic — the same version always yields the same score, so a changed score means the package itself changed.
| Version | Score | Findings | Engine | Scanned |
|---|---|---|---|---|
v1.4.0 latest |
A 96/100 | 1 | 1.8.0 | 2026-07-23 |
Show this server's live Trust Score in your README, docs or website. The badge is served straight from the registry and updates automatically after every rescan — no API key needed. It links back to this page, so anyone who sees the grade can also read the findings behind it instead of taking a number on faith.
The score above is reproducible: the same package version always yields the same result. Run it locally or over the free API — no account, no LLM, fully deterministic.
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