EssenceScholar MCP Server
MCP server exposing the EssenceScholar deep research pipeline and paper workflows as agent tools.
Tools
Research pipeline
- deep_research — full literature review pipeline (2–8 min, searches EconPapers/arXiv/SSRN, enriches top papers, generates a structured draft)
- deep_research_chat — read-only follow-up on an existing session (ask questions, dig deeper)
- deep_research_expand — fetch more papers into an existing session via new searches (2–5 min)
- list_deep_researches — list all past research sessions
- get_deep_research — retrieve any past draft by ID
Search & library
- search_econpapers — raw EconPapers search, returns JSON paper objects
- search_ssrn — raw SSRN search, returns JSON paper objects
- download_paper — download a found paper (EconPapers/SSRN/arXiv/DOI URL) into your library and get its
paper_id. SSRN refuses servers outright, so those come back{"status": "parked"}and wait for the reader's own browser — not an error, and not billed - upload_paper — upload a local PDF into your library and get its
paper_id - register_paper_text — register a local PDF by extracting its text client-side (no server OCR);
paper_idderived from the sha256 of the PDF bytes, so re-registering is a no-op - list_user_papers — list papers in your library (resolve
paper_ids; optional title filter) - search_paper_content — server-grade RAG retrieval over one of your papers (the same hybrid BM25+semantic ranking a workflow step uses); returns ranked chunks + joined text
- get_paper_context — the server-held
{{variable}}dict a workflow run would substitute (research_interests, missing/top/trending lit, author profiles, …) plus the paper's section inventory
The whole product, one door (new in 1.0.0)
- capabilities — what Essence Scholar can do, with prices, read from the app's own catalog. Free
- route — classify a sentence the way the app does and see what it WOULD do, with nothing run. Free
- quote — price one capability over an exact payload and mint a ten-minute confirm token. Free
- act — run what was quoted, once the user has agreed. The same consent gate the app uses
Long jobs — start, then poll (new in 1.0.0) A deep submission check takes 30–40 minutes and is billed when it starts. These make a paid run recoverable instead of trapping the report inside the call that launched it.
- get_submission_check — the report if it has finished, the live status if not
- get_deep_review — a Verified Deep Review by run id
- get_run / list_runs — durable handles for anything the app or
actstarted
Notebooks, conferences, authors (new in 1.0.0)
- list_notebooks, create_notebook, add_papers_to_notebook, notebook_overview — a notebook is the product's answer to "several papers at once": shared retrieval, one overview, one chat
- list_conferences, get_conference, import_conference — conference programmes as agendas
- paper_authors, author_profile, author_deep_analysis — the evidence-linked researcher profiles
- park_paper — hand a paper the server may not fetch (SSRN) to the reader's own browser, where the extension imports it
Workflows
- list_paper_workflows — list your routines, every scope (paper, notebook, conference)
- run_paper_workflow — run a workflow (by ID) on a paper in your library (server-executed)
- run_workflow_on_pdf — upload a local PDF and run a workflow on it in one call
- get_workflow_definition — fetch a workflow's raw declarative DAG (steps/prompts/deps) as JSON
- compile_workflow_skill — convert a workflow into a portable skill (
target=claude|codex|gemini) your own agent runs, instead of executing it server-side - create_workflow — author a workflow FROM your agent: design the step DAG in conversation, push it to Agent Studio (returns validation warnings before any run)
- update_workflow — refine an existing workflow in place (steps/prompts/synthesis)
Typical flow: search_econpapers / search_ssrn → download_paper → run_paper_workflow.
Or, to reuse a workflow as an agent skill: list_paper_workflows → compile_workflow_skill → save the returned SKILL.md.
Compiled skills run client-side (Mode B): register_paper_text (local PDF) or list_user_papers → get_paper_context for the {{variables}} → search_paper_content per step for evidence.
Setup
Add to your Claude config (~/.claude/settings.json):
{
"mcpServers": {
"essencescholar": {
"command": "uvx",
"args": ["essencescholar-mcp"],
"env": {
"ESSENCESCHOLAR_API_KEY": "sk_live_..."
}
}
}
}
Get your API key from essencescholar.com.
To install as a Claude Desktop extension instead, download the packaged .mcpb bundle from a release and open it with Claude Desktop — it will prompt you for your API key via manifest.json's user_config.
Privacy Policy
This connector sends the arguments of whichever tool you or Claude explicitly invoke (search queries, paper content/text, workflow definitions, research topics) to the EssenceScholar backend, associated with your EssenceScholar account. It has no access to Claude's conversation history, memory, or files beyond what a tool call explicitly names, and nothing runs unless a tool is called. See the full policy, including the Claude / MCP Connector section, at:
Metadata
Release files for essencescholar-mcp 1.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| essencescholar_mcp-1.1.0.tar.gz | 131.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| essencescholar_mcp-1.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 177.3 kB
Release files / essencescholar_mcp-1.1.0.tar.gz
| Download URL | essencescholar_mcp-1.1.0.tar.gz |
|---|---|
| Size | 131.6 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
uv/0.8.15
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Release files / essencescholar_mcp-1.1.0-py3-none-any.whl
| Download URL | essencescholar_mcp-1.1.0-py3-none-any.whl |
|---|---|
| Size | 45.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
uv/0.8.15
|