readeck-mcp
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
Readeck MCP
An MCP server that turns a Readeck library into a read/write knowledge base for AI agents: agents can push the HTML they generate straight into Readeck (auto-tagged by an LLM) and later retrieve and read those documents back. Built with FastMCP — the same code runs as a local stdio server or a remote HTTP server behind a reverse proxy.
Unlike existing Readeck MCPs (which are read-only reading assistants), this one ingests agent-generated HTML and auto-labels it with an LLM from a fixed taxonomy.
Tools
Tool | Purpose |
| Save raw HTML as a readable bookmark; auto-labels when no labels are given. |
| Retrieval: find the best-matching document(s) and return their full readable text. |
| Search the library (metadata only). |
| List existing labels with counts. |
readeck_save uses Readeck's JSON create API with the raw-HTML html field
(base64/data-URI do not work). It sends a browser User-Agent so a WAF (e.g.
Cloudflare error 1010) doesn't block it, and polls briefly to return the new
bookmark id, word_count and reading_url.
Related MCP server: eigma-mcp
Auto-labeling
When you call readeck_save without labels, an LLM classifies the document.
It's injection-resistant by design: the model may only pick from a fixed
taxonomy (LABEL_TAXONOMY), and the result is intersected with that list
server-side, so out-of-vocabulary labels are impossible. An origin label
(ORIGIN_LABEL, default propio) is always added by code, never the LLM.
Provider-agnostic (OpenAI-compatible or Anthropic). Works with free tiers such
as Groq — for reasoning models like gpt-oss set a generous LLM_MAX_TOKENS
and the server sends reasoning_effort: low automatically.
Guardrails
Fail-closed HTTP auth: over HTTP transport every tool call requires
Authorization: Bearer $MCP_AUTH_TOKEN(constant-time compare).Input validation: HTML size cap, non-HTML rejection, URL-scheme allowlist, label sanitization + cap.
Token-bucket rate limiting per process.
LLM calls retry on 429/5xx with backoff and degrade gracefully (labeling is best-effort; a failure never blocks the save).
Configuration (environment)
Variable | Default | Notes |
|
| Readeck base URL. |
| — | Required. Readeck → Settings → API tokens. |
|
|
|
|
| http mode only. |
| — | Required in http mode (fail-closed bearer). |
|
| Enable LLM auto-labeling. |
|
| Always-added origin label. |
| see | Comma-separated closed vocabulary. |
|
| Max LLM topics per doc. |
|
|
|
|
| Include the version prefix for openai (e.g. |
|
| Any chat model on the chosen provider. |
| — | Key for the LLM provider. |
|
| Output budget / input text sent. |
Local (stdio)
READECK_TOKEN=... uv run --script server.py --selftest # smoke testRegister with an MCP client (mcpServers), e.g.:
{
"mcpServers": {
"readeck": {
"command": "uv",
"args": ["run", "--script", "/path/to/readeck-mcp/server.py"],
"env": { "READECK_URL": "https://readeck.example.com", "READECK_TOKEN": "..." }
}
}
}Remote (HTTP / Docker)
Run with READECK_MCP_TRANSPORT=http (see Dockerfile). Put it behind your
reverse proxy / tunnel at e.g. https://mcp-readeck.example.com. When
co-located with Readeck, set READECK_URL=http://readeck:8000 to skip the public
round-trip. Point clients at the endpoint:
{
"mcpServers": {
"readeck": {
"type": "http",
"url": "https://mcp-readeck.example.com/mcp",
"headers": { "Authorization": "Bearer <MCP_AUTH_TOKEN>" }
}
}
}Note: use the endpoint path without a trailing slash (
/mcp, not/mcp/) — some proxies turn the trailing-slash redirect into a 307 that breaks streamable-HTTP clients.
Always set a strong MCP_AUTH_TOKEN before exposing the endpoint — the token
grants write access to your library.
License
MIT
This server cannot be deployed
Maintenance
Related MCP Connectors
All your bookmarks in one library. Share them with your AI agents. Read-only search over your saves.
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Persistent docs and memory for AI agents — read, write, organize & search a shared workspace.
Read, edit and improve shared documents and knowledge with people and agents.
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