MCP and REST retrieval
Research retrieval for citation-grounded AI agents
Let an agent discover sources, inspect compact evidence, fetch bounded full text, and retain canonical identifiers instead of relying on model memory alone.
A practical retrieval workflow
01
Search broadly
Send a natural-language question to document search and retrieve a compact, reranked candidate set.
02
Inspect evidence
Use titles, authors, dates, snippets, abstracts, and source URIs to decide which documents deserve deeper reading.
03
Fetch selectively
Fetch bounded document text or search within a long document for passages relevant to the question.
04
Preserve citations
Carry DOI, PubMed, ISBN, patent, or source-page identifiers into the answer and downstream audit trail.
What the retrieval layer returns
- Compact reranked document and social search results.
- Matched passages and abstract previews for evidence triage.
- Bounded document text and references on explicit fetch.
- Canonical source URIs that an application can retain.
Important boundaries
- Retrieved text is evidence for review, not proof that a generated claim is correct.
- Coverage and full-text availability vary by source.
- Agents should cite the source record and expose uncertainty.
- High-stakes conclusions still require qualified human review.
Implementation paths
Start with the interface that matches your prototype. The source identifiers and document model stay consistent as you move from manual exploration to an agent or application.