Metricool MCP Server
mcp-metricool
PyPI
v1.1.9
Published by an unidentified publisher — no publish provenance and no public repository, so the publisher could not be verified and the source cannot be independently located.
This is a Model Context Protocol (MCP) server for interacting with the Metricool API. It allows AI agents to access and analyze social media metrics and campaign data from your Metricool account.
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 − 2 = 98. 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 |
|---|---|
| −2 | publisher verification (unlinked) — no provenance/repo link, but the shipped source was fully read |
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.
Findings 0
Tools 28
Each tool and what it can reach — statically extracted from the published source.
get_analyticsno sensitive capabilityget_best_time_to_postno sensitive capabilityget_bluesky_postsno sensitive capabilityget_brandsno sensitive capabilityget_brands_completeno sensitive capabilityget_facebook_postsno sensitive capabilityget_facebook_reelsno sensitive capabilityget_facebook_storiesno sensitive capabilityget_facebookads_campaignsno sensitive capabilityget_googleads_campaignsno sensitive capability
Show 18 more tools ↓
get_instagram_postsno sensitive capabilityget_instagram_reelsno sensitive capabilityget_instagram_storiesno sensitive capabilityget_linkedin_postsno sensitive capabilityget_metricsno sensitive capabilityget_network_competitorsno sensitive capabilityget_network_competitors_postsno sensitive capabilityget_pinterest_boardsno sensitive capabilityget_pinterest_pinsno sensitive capabilityget_scheduled_postsno sensitive capabilityget_thread_postsno sensitive capabilityget_tiktok_videosno sensitive capabilityget_tiktokads_campaignsno sensitive capabilityget_twitch_videosno sensitive capabilityget_x_postsno sensitive capabilityget_youtube_videosno sensitive capabilitypost_schedule_postno sensitive capabilityupdate_schedule_postno sensitive capability
What this scan could not see
- Tools were statically extracted from the published source (28 recovered), not enumerated from a running server. Tool-poisoning, Unicode-smuggling, capability and toxic-flow analysis ran on this inferred surface, but a mis-parsed registration could be missed or mis-attributed, so tool-derived findings are capped below “confirmed”. To grade the real runtime surface, scan the running server: --command "npx -y <package>".
Versions 1
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 |
|---|---|---|---|---|
v1.1.9 latest |
A 98/100 | 0 | 1.13.0 | 2026-08-25 |
Embed this score
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.
Verify this score yourself
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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