An advanced Model Context Protocol (MCP) server that provides intelligent search and retrieval capabilities for QuantConnect PDF documentation. This
An advanced Model Context Protocol (MCP) server that provides intelligent search and retrieval capabilities for QuantConnect PDF documentation. This server converts PDFs to searchable markdown format and provides fast, context-aware search. The quantconnect docs mcp server wraps that behind the Model Context Protocol, so an assistant can use it through 1 defined tool rather than through you.
Everything the assistant can do here goes through one of these:
Prerequisites — The Prerequisites tool exposed by this serverSetup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client.
You will need 2 environment variables: QUANTCONNECT_PDF_FOLDER, QUANTCONNECT_MARKDOWN_FOLDER. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
Plenty of knowledge and memory servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Quantconnect Docs's toolset — Prerequisites — is a fair guide to whether it matches your workflow. It is maintained by lhstorm; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| Prerequisites | The Prerequisites tool exposed by this server. |
| Variable | Description | Required |
|---|---|---|
| QUANTCONNECT_PDF_FOLDER | Configuration value read at startup. | Optional |
| QUANTCONNECT_MARKDOWN_FOLDER | Configuration value read at startup. | Optional |
A knowledge graph your assistant keeps between sessions — entities, relations and observations that persist.
Kill hallucinated APIs — version-accurate, up-to-date library documentation injected straight into context.
Your workspace, on speaking terms with AI — search, read and write Notion pages and databases.
A structured scratchpad for hard problems — stepwise reasoning with revisions, branches and visible logic.
Symbol-level code navigation, refactoring and memory for coding agents — the IDE brain your assistant has been missing.
Chat with your second brain — search, read and write vault notes through the Local REST API.