Your bounded relationship tracker 506

The most practical way to understand this project is to start with what it is trying to avoid. A lot of entity resolution tools overwhelm the calling system with too many candidates, too many hidden heuristics, or too much confidence. If you are trying to connect a local record to a Wikidata item, that kind of behavior is risky. A near match can look convincing right up until it contaminates a catalog, a content pipeline, or a reporting layer. The open source project oft

Anyone who has tried to link messy real-world records to a knowledge base learns the same lesson sooner or later: the hard part is rarely getting a candidate. The hard part is trusting it. That is the center of gravity for this project. The open-source server and CLI known as Wikidata + Google Knowledge Graph MCP is built to help agents search Wikidata, inspect selected facts, and resolve local records to Wikidata QIDs. What stands out is not just that it can search, but

The most interesting data tools are often the ones that do less, not more. That sounds backwards until you have spent time cleaning entity matches, reviewing false positives, or tracing where a supposedly obvious identification went wrong. In that kind of work, restraint matters. A search interface that floods you with fifty vaguely related entities may look powerful at first glance, but it rarely helps with the last mile, the part where someone has to decide whether a reco

Entity work looks simple until you try to do it at scale. A person name appears in three spellings, a company record has no stable external identifier, and two places share the same label but not the same geography. At that point, “just search it” stops being a serious strategy. You need a repeatable way to query public knowledge sources, inspect what came back, and make linking decisions that can be reviewed later. That is where MCP becomes useful. In the case of MCP fo

Evidence review usually breaks down in ordinary places, not exotic ones. A researcher finds three entities with nearly identical names. An analyst pulls a fact from a knowledge base without checking rank or reference context. A product team links a local record to the wrong identifier because the top search hit looked plausible enough. Those are not dramatic failures, but they are expensive. They create quiet data debt that spreads into search, reporting, enrichment, and do

People often treat search and resolution as if they were the same operation. In practice, they solve different problems, and confusing them usually creates messy downstream data. That distinction matters a lot in the Wikidata + Google Knowledge Graph MCP server, where kg_search and kg_resolve sit close together conceptually but behave very differently. If you work with MCP for Wikidata, or more specifically with MCP for google knowledge graph and wikidata, the easiest mi