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Search worklore stories and x-ray any skill or story capability tier (T0-T4) before you run it.
About
Search worklore stories and x-ray any skill or story capability tier (T0-T4) before you run it.
Remote endpoints: streamable-http: https://worklore.dev/mcp
Security Report
Remote MCP endpoint verified (871ms response). 3 trust signals: valid MCP protocol, requires auth, registry import. No security issues detected.
Endpoint verified · Requires authentication · 2 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Remote servers are capped at 8.0 because source code is not available for review. The score reflects endpoint verification only.
Permissions Required
This plugin requests these system permissions. Most are normal for its category.
How to Connect
Remote Plugin
No local installation needed. Your AI client connects to the remote endpoint directly.
Add this to your MCP configuration to connect:
{
"mcpServers": {
"io-github-worklore-worklore": {
"url": "https://worklore.dev/mcp"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
worklore-mcp
An MCP (Model Context Protocol) connector for worklore.dev — it lets any MCP-capable agent (Claude, ChatGPT, Cursor, …) search worklore's developer stories, read one with its capability disclosure attached, x-ray any skill or story before running it, and — once you've signed in — record how a reproduction went or publish a story of your own.
- Endpoint:
https://worklore.dev/mcp(Streamable HTTP, JSON-RPC over POST) - Auth: OAuth 2.1 (PKCE + dynamic client registration). Sign-in is with your GitHub public profile only — worklore sees your handle and avatar, no scopes, no email, no repositories.
- Listed on: the official MCP Registry
as
io.github.worklore/worklore, and Smithery - Roadmap / progress: Project #1 (a "Relay Board" — the human does the site actions, the agent writes the code)
What it's for
worklore stories are text your agent executes. This connector brings them — and their capability tier — into your agent, so you can find relevant work and see what it can touch before you run it. Tiers describe reach (blast radius), not virtue, and this is never a "safe" verdict. See the write-up: Stop asking "is this skill safe?" — ask "what can it do?" and the tool it wraps, skill-xray.
Capability tiers: T0 inert · T1 local · T2 network · T3 elevated (secrets / persistence / privilege) · T4 opaque (fetches/runs code at runtime).
Tools
Read-only — no sign-in needed beyond connecting:
| Tool | Arguments | Returns |
|---|---|---|
check_capability | text or url | tier (T0–T4) + sha256 + findings (file:line) + endpoints, plus behavioral red flags — capability disclosure |
get_story | slug | the story's full markdown (narrative + "Reproduce this" contract) with its capability tier + findings |
search_stories | query (optional) | matching stories (title/summary/tags/stack), each with its capability string |
suggest_for_project | context | up to 3 stories worth reproducing here, with why + tier |
Write — these act as you, and need your authenticated session:
| Tool | Arguments | Returns |
|---|---|---|
report_reproduction | slug, result (worked/partial/failed), note (optional) | the recorded reproduction. failed is a useful report and must never be inflated |
publish_story | markdown, or the parts (title, narrative, reproduce, tags, type, stack) | the published story's slug + URL. Only ever call this with the author's explicit approval of the full draft |
All six carry MCP annotations (readOnlyHint / openWorldHint / idempotentHint)
so a client can tell the user what a call will do before they approve it, and all
six declare an outputSchema and return structuredContent, so a consuming agent
can rely on shape instead of parsing prose.
Every result carries a human capability string (e.g. T0 · inert — touches nothing) so a tier code is never shown bare.
Resources
Published stories are also exposed as MCP resources under worklore://story/,
paginated, so a client that browses resources sees the library without calling a
tool. To find a specific story, use search_stories / suggest_for_project
rather than walking every page.
Add the connector
In Claude Code:
claude mcp add --transport http worklore https://worklore.dev/mcp
On claude.ai (web): Settings → Connectors → Add custom connector → name
worklore, URL https://worklore.dev/mcp. (Custom connectors need a paid plan.)
Via Smithery:
npx -y smithery mcp add worklore/worklore
Any other MCP client: point it at https://worklore.dev/mcp (Streamable
HTTP). Your client will be walked through OAuth on first connect — it registers
itself, so there is nothing to paste and no API key to manage.
Example prompts
- "Use worklore to check what this skill can do before I install it:
<paste a URL or the skill text>." - "Search worklore for stories about Flutter golden tests, and tell me each one's capability tier."
- "Here's my project: a Python AWS-Lambda backend with DynamoDB. Suggest 3 worklore stories worth reproducing here, and read me the top one's Reproduce-this contract."
Notes
- The connector is a route on worklore's existing backend Lambda; it reuses the vendored skill-xray scanner.
- Origin is validated (native clients send none and are allowed; unknown web origins are rejected); HTTPS enforced.
- How this server was built and shipped, end to end, including what went wrong: Build and ship your own MCP server.
MIT-licensed.
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