Dependency Freshness Checker
About
**Dependency Freshness Checker** tells any AI coding agent whether an npm or PyPI package is
Details
- Author
- Armigerous
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- Citations-first output with source URLs and access dates
- Supports both npm and PyPI ecosystems
- Detects deprecation status for any package or assumed version
- Counts how many versions behind your agent is
- Generates a dated "what changed since your version" change summary
- Pay-Per-Event pricing at $0.005 per package checked
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Dependency Freshness CheckerCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Pass a list of packages with ecosystem, name, and optional currentVersion to the MCP tool check_dependency_freshness(packages). Three ways to reach it: via Apify's hosted MCP server at https://mcp.apify.com, as its own MCP server at the Actor's /mcp endpoint, or locally via stdio with a compiled entrypoint.
check_dependency_freshness
For each npm or PyPI package, determine whether it is outdated (out of date) and return its current version, release dates, deprecation status, how many stable versions you are behind, and a DATED, CITED "what changed since your version" breaking-change summary. Built for time-blind AI agents whose training cutoff makes them emit deprecated dependency code. Every answer carries source URLs + access dates.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"dependency freshness checker": {
"dependency-freshness": {
"command": "npx",
"args": [
"-y",
"dependency-freshness-mcp"
]
}
}
}
}
McpServers
{
"dependency-freshness": {
"command": "npx",
"args": [
"-y",
"dependency-freshness-mcp"
]
}
}
Dependency Freshness Checker — is your npm or PyPI dependency outdated? Latest version, deprecation & breaking-change MCP for AI agents
Dependency Freshness Checker tells any AI coding agent whether an npm or PyPI package is
outdated (out of date) — and gives the cited facts to prove it: the latest version, release
dates, deprecation status, how many versions behind you are, and a dated "what changed
since your version" breaking-change diff. It is MCP-native, reads only public registries and
GitHub releases (no scraping, no ToS risk), and is priced Pay-Per-Event for pay-as-you-go agent use.
What does Dependency Freshness Checker do?
Give it any npm or PyPI package — optionally with the version your agent is assuming — and the
Dependency Freshness Checker returns, citations-first:
- the latest published version and its release date;
- whether the package (or your assumed version) is deprecated;
- how many versions behind you are; and
- a dated, cited changeSummary[] of what changed between your version and latest.
Every field carries a source URL and access date, so an agent can trust — and quote — the result.
Why check npm & PyPI dependency freshness?
LLM coding agents are time-blind: their training is frozen, so they emit 70–90% deprecated
package code. Coding agents are the largest agent population, and they all share this blind spot.
The Dependency Freshness Checker fixes the frozen-training-cutoff problem at call time. It lives
in the proven-demand "freshness for agents" lane but stays narrow and legally clean — it reads
only public npm, PyPI, and
GitHub releases — so it does not fight first-party RAG
browsers or funded incumbents.
How to use the Dependency Freshness Checker
Pass a small list of packages. Defaults are kept low so a first run is cheap, fast, and succeeds:
{
"packages": [
{ "ecosystem": "npm", "name": "zod", "currentVersion": "3.22.0" },
{ "ecosystem": "pypi", "name": "fastapi" }
]
}
currentVersion is optional — omit it to just ask "what's latest?". See
.actor/input_schema.json for the full schema.
Output — dated, cited freshness results
Each result includes isOutdated, versionsBehind, latest + latestPublishedAt, a dated
changeSummary[], and field-level citations[]. The same shape is returned by a batch run and by
the MCP tool, so agents and dashboards consume one format.
Using Dependency Freshness Checker as an MCP tool for AI agents
This Actor exposes one MCP tool, check_dependency_freshness(packages). Three ways to reach it
(mechanics cited in docs/research/50-mcp.md):
- A — Hosted, no setup. Every public Apify Actor is callable through Apify's hosted MCP server
at https://mcp.apify.com — no extra wiring.
- B — The Actor as its own MCP server (Standby). The deployed Actor serves a Streamable-HTTP
MCP endpoint at /mcp on its own stable URL:
{
"mcpServers": {
"dependency-freshness": {
"url": "https://<user>--dependency-freshness-mcp.apify.actor/mcp",
"headers": { "Authorization": "Bearer <APIFY_TOKEN>" }
}
}
}
- C — Local stdio (dev / MCP Inspector). Build, then point any
MCP client at the compiled entrypoint:
{
"mcpServers": {
"dependency-freshness": {
"command": "node",
"args": ["/abs/path/dependency-freshness-mcp/dist/mcp/stdio.js"],
"env": { "GITHUB_TOKEN": "ghp_… (optional, raises GitHub rate limit)" }
}
}
}
Inspect locally with npx @modelcontextprotocol/inspector node dist/mcp/stdio.js.
Pricing
The Dependency Freshness Checker uses Pay-Per-Event pricing, built for pay-as-you-go agent
use: a small flat fee per run start plus $0.005 per package checked ($5 / 1,000) — inside
Apify's recommended $1–10 / 1,000-results band. You only pay for packages actually checked.
FAQ
Does it scrape websites? No. It reads only public registry APIs and GitHub releases — no
scraping, no terms-of-service risk.
Which ecosystems are supported? npm and PyPI today.
Do I need a GitHub token? No — it is optional and only raises the GitHub rate limit for
heavier batches.
Can an agent call it directly? Yes — that is the point. Use the check_dependency_freshness
MCP tool (option A or B above).
Other Actors
More agent-native data Actors are on the way on the
Apify Store. Follow the author profile to see new
freshness-for-agents tools as they ship.
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