Consensus

@Consensus find peer-reviewed studies on the effectiveness of intermittent fasting for weight loss published in the last 5 years

This illustrative example shows Consensus returning a curated list of 8-10 research papers, each with title, authors, publication year, journal name, DOI, and a brief abstract excerpt. The response might include studies from journals like *Obesity Reviews* and *Nutrients*, with publication dates between 2019-2024. Each entry would note the study type (meta-analysis, RCT, cohort study) and provide a direct link to the paper. The MCP filters results to peer-reviewed sources matching your time and topic constraints.

This example demonstrates Consensus's core strength: filtering academic literature by recency and peer-review status. Each search call consumes API credits, so broad queries can be expensive. The MCP returns one page at a time; if you need comprehensive coverage of a topic, you'll need to paginate through results using continuation cursors, incurring additional billing per page.

@Consensus search for meta-analyses comparing cognitive behavioral therapy versus medication for treating major depression, limit to open-access papers

In this illustrative scenario, Consensus returns 5-7 meta-analyses and systematic reviews, each tagged as open-access. Results would include papers from sources like *PLOS ONE* and *Frontiers in Psychiatry*, with each entry showing sample size, effect size data where available, and a summary of findings. The response clarifies which papers are freely accessible versus requiring institutional login. A continuation cursor indicates more results exist beyond this first page.

This showcases Consensus's ability to filter by study type (meta-analyses) and access level (open-access only). Useful when you need high-quality synthesis studies without paywall friction. Be aware that restricting to open-access significantly narrows result sets—some fields have limited OA meta-analyses. The access filter requires appropriate API scope configuration.

@Consensus find recent studies on microplastic contamination in drinking water, then summarize the consensus view on health risks and detection methods across the top 10 results

This illustrative example pairs Consensus's search capability with AI synthesis. The MCP first retrieves 10 recent papers on microplastic contamination, returning titles, abstracts, and key findings from journals like *Environmental Science & Technology* and *Water Research*. The AI then analyzes these results to produce a 150-word summary identifying: (1) emerging consensus that microplastics are ubiquitous in tap water globally, (2) conflicting evidence on human health impacts due to limited longitudinal studies, and (3) three dominant detection methods (spectroscopy, microscopy, chromatography) with varying sensitivity thresholds.

This example demonstrates how Consensus excels when combined with AI reasoning—the MCP provides vetted academic sources, while the AI synthesizes patterns across studies. Note that the synthesis quality depends on abstract completeness; full-text analysis isn't available through the API. Each search and pagination step bills separately, so requesting 10+ results for synthesis can accumulate costs quickly.

When a 3-person startup needs to back health claims with real studies

A wellness app startup writing landing page copy needs to cite actual research behind their feature claims. The Consensus MCP is the right call here because it surfaces peer-reviewed papers in seconds without leaving the workspace. One team member drafts the claim, another runs the search through Switchy, and they paste the citation directly into the copy doc. The trade-off: search calls are billed per query, so this works when you're validating 5-10 claims per campaign, not running exploratory lit reviews. If your team is writing a whitepaper that needs 40+ citations, you'll hit budget friction fast. For targeted claim-checking at small scale, this MCP saves the context-switch to Google Scholar and keeps the work in one thread.

When support needs to answer 'does the science support this?' questions

A 6-person customer success team at a nutrition coaching platform fields questions like 'does intermittent fasting actually work for weight loss?' multiple times a week. The Consensus MCP lets them pull study summaries into the support ticket thread without switching to a research database. One agent searches, another reviews the abstract, and they send a cited answer in under two minutes. The boundary: this works when the question maps cleanly to a search term and you need one or two studies to answer it. If the customer is asking a nuanced question that requires synthesizing five conflicting papers, the single-tool MCP won't replace a real literature review. For high-frequency, straightforward evidence questions, it closes tickets faster and keeps the team's credibility high.

When founders need to verify market research claims before a pitch

A 2-person founding team is prepping a Series A deck and needs to validate three market-size claims with published research. The Consensus MCP is perfect for this one-off sprint because it returns study metadata and access links in a single query. They run the searches in Switchy, screenshot the results for the appendix slide, and move on. The constraint: pagination beyond the first result page costs additional search credits, so this MCP is overkill if you're doing a deep competitive analysis that needs 30+ papers per topic. If you're fact-checking fewer than 10 claims and need the work done in an afternoon, the API key setup and per-call billing are worth it. For ongoing research workflows, consider a broader academic database integration.