Disclosure: this tutorial is written by the ScholarMuse team and includes our own tool. The diagrams are original teaching schematics, not product screenshots, live query results or a new benchmark.
The most useful output from an AI literature search is not a polished paragraph. It is a set of records you can trace back to original papers, plus a clear account of what the assistant did and did not read.
A literature matrix makes that boundary visible. Use an agent for retrieval and organization, while keeping inclusion decisions and scientific interpretation under researcher review.
1. Define the search contract
Before connecting tools, state the research question, time window, study types, population and exclusions. Decide what counts as a useful candidate and whether preprints belong in a separate group.
A request for “the best papers” gives an assistant too much room to improvise. A request for source-linked candidates under explicit rules can be checked.
Agent workflow: define the question, retrieve records, extract and review.
2. Connect a source, not just a conversational model
ScholarMuse exposes research tools through MCP at https://scholarmuse.ai/mcp. Follow the current connection guide for your Claude or ChatGPT client, authorize the account, and confirm the connection with a small metadata query.
Client support can depend on plan, version and administrator policy. Do not assume that every account has the same menus. Nor should you replace an entire existing configuration with an example snippet.
Agent access requires an account and follows credit rules separate from the free web lookup tools. Check the current paper-search capability and applicable costs before a large search. A successful connection does not itself prove that all later answers are sourced.
3. Ask for records and gaps explicitly
Adapt this prompt to your own question:
Find candidate papers about [question] within [date range], prioritizing [study designs and population]. Use the connected source. For every candidate, return the exact title, authors, year, DOI when available, original record URL and publication type. Keep preprints separate. Report missing fields as missing; never invent an identifier. Explain relevance and possible exclusions. State whether you accessed only metadata, an abstract or full text.
Start with a small batch and inspect several source links before scaling. Save queries and dates. If a tool call fails, the agent should report it—not silently replace retrieval with a plausible list from memory.
Literature matrix: identity, methods, evidence and inclusion decisions.
4. Build the matrix without manufacturing evidence
Use one row per distinct paper and deduplicate by identifier, checking versions when necessary. The minimum columns are:
- Record ID, title, DOI and original URL.
- Query, retrieval date and publication status.
- Study design, population and sample, when stated.
- Relevant finding with a page or section reference.
- Evidence access: metadata, abstract or full text.
- Limitations and include/exclude reason.
If the agent only retrieved metadata, the finding and sample fields may remain empty. Do not infer them from the title. If an abstract supplies a result, label it abstract-based; this is not the same as extracting verified details from the full paper.
5. Review claims before writing the synthesis
Open the records you intend to use, confirm bibliographic fields and inspect publication-status notices. For every substantive statement, locate the source passage and check its population and conditions.
Cite the original paper, not ScholarMuse or the assistant. Keep the matrix and search log with the project. A limited multi-source search is not automatically a systematic review: reproducible coverage, screening and reporting require a separately designed method.
The workflow here is an instructional template, not a claim that a new search was executed or evaluated. Its value is simple: another researcher should be able to see which record supported each statement and where uncertainty was left unresolved.
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