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Hyperresearch MCP Server

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A deep research agent for Claude Code that turns every page it reads into a persistent, searchable vault.
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★ 1.9k Stars Category · Other Very popular
59FMRS · C

Hyperresearch is an MIT-licensed individual open-source project that persists research into a searchable markdown vault and ships a stdio MCP server so Claude Desktop, Cursor, and other clients can reuse that vault. Its strength is durability and portability: markdown is the source of truth and SQLite is only a rebuildable cache. Be aware that the project's self-reported benchmark results are pending third-party validation and that the README explicitly does not guarantee factual accuracy, leaving source judgment to the user.

Reliability
9/20
Security and permissions
12/20
Maintenance
11/20
Documentation
15/20
Setup experience
12/20
Read the FMRS scoring method →

Hyperresearch is an agent-driven research knowledge base. According to its README, it turns Claude Code into a deep research agent built around a tier-adaptive 16-step pipeline that produces an adversarially-audited report with full source provenance from a single prompt. Every source it reads lands in a persistent, searchable markdown-plus-SQLite vault, and each new session searches that vault before fetching anything new. The vault is reachable outside Claude Code through a built-in MCP server: install hyperresearch[mcp] and run hyperresearch mcp to speak stdio, exposing thirteen tools (search_notes, read_note, read_many, list_notes, get_backlinks, get_hubs, vault_status, lint_vault, check_source, list_sources, fetch_url, create_note, update_note) so Claude Desktop, Cursor, or any other MCP client can work the same vault. It is authored by jordan-gibbs as an individual open-source project under the MIT license, requires Python 3.11+ (3.11–3.13; 3.14 is not yet supported), and keeps notes as plain markdown with YAML frontmatter so the SQLite index is fully rebuildable and users are not locked into the tool.

Tools

search_notes
Full-text search across the persistent research vault, optionally including note bodies or semantic search.
read_note
Read the contents of a single note.
read_many
Batch-read multiple notes in one call.
list_notes
List notes held in the vault.
get_backlinks
Return the reverse links pointing at a given note.
get_hubs
Return the most-connected notes in the vault.
vault_status
Report status information about the vault.
lint_vault
Run a vault health check such as broken links and missing tags.
check_source
Check information about a given source.
list_sources
List the sources recorded or cited in the vault.
fetch_url
Fetch the content of a given URL.
create_note
Create a new note in the vault.
update_note
Update an existing note.

Setup

1) Make sure Python 3.11–3.13 is available (3.14 is not yet supported). 2) Run pip install hyperresearch[mcp] to install the MCP-enabled build. 3) Run hyperresearch mcp, which starts the MCP server over stdio. 4) In Claude Desktop, Cursor, or another MCP-speaking client, configure hyperresearch mcp as the server command. The README does not provide a ready-to-copy client config snippet, so follow your client's MCP configuration format.

claude_desktop_config.json
{
  "mcpServers": {
    "hyperresearch": {
      "command": "hyperresearch",
      "args": ["mcp"]
    }
  }
}

Fit and risk

Best for

  • Users who already maintain a local Hyperresearch markdown vault and want other MCP clients to reuse the same notes
  • Researchers who want their desktop AI client to read existing local material instead of re-searching the web every time
  • Individual researchers or small teams who care about portability (plain markdown, git-friendly, JSON exportable)

Not for

  • Users who want zero setup and only the built-in Claude Code skill without maintaining a local vault
  • Users who cannot install Python 3.11+ or run local command-line tools
  • Users expecting the server to guarantee factual accuracy — the README states the lint gate catches structural failures only and factual accuracy remains the user's call
  • Users who need paywalled content but have not completed a local login

Required permissions

  • Read and write the vault directory in the working tree (such as research/notes markdown files)
  • Create and rebuild the SQLite index file; hyperresearch sync reconstructs it from markdown
  • Make outbound network requests to fetch external URLs via tools such as fetch_url
  • If embeddings are enabled, reach the configured embedding provider (voyage, openai; the default none needs no API keys)
  • Local file-write permission required to create and update notes

Risks and side effects

  • Fetched web content is untrusted input: the README says fetched bodies are served inside <untrusted-source> fences and treated as data, and clients should keep the same stance to avoid being steered by embedded instructions
  • Write tools (create_note, update_note) modify local vault files directly, so misuse can corrupt the note collection; keeping the vault under git is advisable for rollback
  • Fetching external URLs exposes requests to third-party sites, potentially revealing the research topic or IP
  • Scraping paywalled or login-gated sites is subject to those sites' terms, and login session credentials are the user's own responsibility
  • Claims in the README such as the DeepResearch-Bench leaderboard position and 250+ sources are the project's own statements and a forward-looking projection; third-party validation is pending and should not be treated as proof of quality

Troubleshooting

  1. Confirm Python is 3.11–3.13; 3.14 is currently unsupported
  2. Confirm pip install hyperresearch[mcp] was run, otherwise the hyperresearch mcp subcommand is unavailable
  3. If the client cannot connect, check that the server is started over stdio and the command is on the client's PATH
  4. If vault search looks wrong, run hyperresearch sync to rebuild the SQLite index from markdown (the index is a rebuildable cache)
  5. Use lint_vault or hyperresearch lint to check broken links, missing tags, and source connectivity issues
  6. If semantic search is unavailable, check the embedding provider configuration; the default none performs no vector search

Use cases

Let Claude Desktop or Cursor search and read notes from an existing Hyperresearch vault
Pull a batch of previously archived sources and summaries with search_notes and read_many before writing a report
Keep accumulating research notes inside an MCP client via create_note and update_note
Audit sources and vault structure with list_sources, check_source, and lint_vault

Supported clients

Claude DesktopFull support
CursorPartial support