Tool retrieval is a necessary first step for tool-augmented LLMs once the set of available tools grows to thousands or more, and itscentral difficulty stems from two gaps. A user query states what the user wants to accomplish in task-centric language, whereas tooldocumentation describes functionality, parameters, and calling conditions in function-centric terms. This low lexical overlap produces aQuery-Documentation Mismatch. Complex queries also bundle several functional needs at once, while a single tool usually covers only one,producing a Multi-Intent Query Mismatch. We present ToolFuse, a training-free framework that addresses each gap on a different side ofretrieval. On the tool side, Complementarity-based Semantic Fusion treats tool documentation and LLM-generated synthetic intent as adocumentation view and an intent view of the same tool, encodes them separately, and assigns each tool a fusion weight from the geometriccomplementarity between its two views in vector space, without fine-tuning the frozen dense encoder. On the query side, QueryDecomposition splits a multi-intent query into focused sub-queries, retrieves for each, and aggregates the results with rank-aware round-robin. On ToolBench, the full ToolFuse raises the Per-Split average NDCG@5 of Qwen3-Embedding-0.6B from 64.72 to 75.49, with consistentgains across three additional dense encoder backbones.