Vexor Semantic Search

Vexor Semantic Search is a vector-powered CLI built for developers and AI agents who need to find relevant code and documentation without knowing the exact filename, function, or keyword in advance. Instead of grep-style matching, Vexor embeds your codebase and docs into vector space and answers natural language queries like "where do we handle refund retries" or "which module validates webhook signatures" with ranked, semantically relevant results. For teams drowning in sprawling monorepos, legacy documentation, or multi-service architectures, this kind of semantic file search collapses what used to be a multi-minute manual hunt into a single query.

When hosted through BusinessMCP.com, Vexor Semantic Search becomes a shared capability across your entire AI agent stack rather than a one-off CLI tool installed on individual laptops. Connect it once to your unified MCP server, and any model — Claude, GPT, Gemini, or a custom agent — can call it through the same hosted /api/mcp endpoint using a Bearer mcph_* key. That means your coding assistant, your internal support bot, and your documentation-writing agent can all query the same vector index of code and docs consistently, without each one needing separate credentials, separate integrations, or separate maintenance overhead. This is the core of BusinessMCP's model-agnostic promise: connect your tools, databases, and knowledge sources once, and expose them everywhere.

Because semantic code search naturally intersects with other developer workflows, Vexor is most powerful when paired with skills that also touch the codebase and its documentation. Teams often chain a natural-language code search step before generating changelogs, before running TDD-style implementation, or before onboarding a new AI coding assistant to a repository. Instead of an agent guessing at file structure or hallucinating function names, it can first query Vexor for grounded, relevant context, then act on real code with far fewer errors — a pattern especially valuable for large legacy codebases or acquired repositories where tribal knowledge has eroded.

On the business-intelligence side, every query routed through the hosted MCP endpoint is visible in BusinessMCP's dashboard, giving engineering leads insight into which parts of the codebase agents reference most, where documentation gaps might be surfacing repeatedly in queries, and how semantic search usage trends across teams over time. This turns a developer utility into an observable, governable part of your AI infrastructure rather than a black-box CLI running unmonitored on someone's machine.

Vexor Semantic Search fits naturally into onboarding new engineers or AI agents to unfamiliar repositories, powering RAG-style code assistants, accelerating security or compliance audits that require locating specific logic patterns across a codebase, supporting documentation search for support and DevRel teams, and enabling AI agents to ground code generation in real, existing implementations rather than inventing them from scratch. Because it's cookieless and GDPR-friendly by design, it can be deployed in regulated environments without the usual privacy overhead that comes with third-party search tooling, making it a practical, low-friction addition to any hosted MCP + BI setup for teams serious about AI-assisted software development.