MCP query tools: single-word seeding crosses domains, default budget truncates most results #3810

graphifyy 0.9.67, graphify.serve MCP, graph of 167k nodes / 889 communities built from a mixed book corpus.

1. Seeding matches single words from the question.
query_graph("incidence algebra derivations poset") seeds on Poset, Incidence, Derivations, Algebra. Incidence resolves to a node from a biostatistics book, so the BFS pulls in an unrelated community. The same happens with "quantum", which matches a quantum machine learning book for a question about quantum Grassmannians. Related to #2384 and #2381, which cover other seed-selection failures; this one is about cross-domain single-token matches. Suggestion: try multi-word phrase matches against labels first, and fall back to single tokens only when no phrase matches.

2. The default token budget drops 85-90% of nodes.
With the default budget, the two queries above showed 35 of 240 and 41 of 400 nodes. The truncation warning is clear, but this is the effect #2530 describes, measured on a 167k-node graph. A larger default (or ranking nodes by relevance to the question before cutting) would help agents that do not retry.

3. shortest_path resolves fuzzy labels to whole-document nodes.
shortest_path("quantum Grassmannian", "incidence algebra") resolved the source to a book-title node. Returning the resolved endpoints, or several candidate matches, would make the result easier to interpret.