Three terms show up together constantly, and it's easy to blur them. Here's what each one actually is, how they depend on each other, and where a MCP-enabled search engine like SeekStorm fits in.
An AI agent is a large language model (LLM) wired up with tools and a loop. Instead of producing one reply and stopping, an agent decides which action to take next, calls a tool to take it, reads the result, and decides again — repeating until the task is done. That loop is what turns a chat model into something that can search the web, query a database, read a file, call an API, or run code on its own initiative.
Plans the next step from the goal and what it has learned so far.
Calls a tool — search, a database, an API, code execution — to gather information or make a change.
Reads the tool's output, updates its plan, and loops until the task is complete.
Two things make agents different from a plain chatbot: they can take multiple steps without a person prompting each one, and every one of those steps is only as good as the tools available to them. A coding agent is only as good as its access to the repo and a shell. A research agent is only as good as its access to accurate, up-to-date information — which is exactly where retrieval and search come in.
An LLM only knows what was in its training data, up to its cutoff date — it has no built-in access to your documents, your product catalog, or this morning's news. RAG closes that gap. Before the model generates an answer, a retrieval step searches an external knowledge base for the passages most relevant to the question, and hands those passages to the model as context. The model then writes its answer grounded in that retrieved text instead of guessing from memory alone.
User or agent asks a question
Search engine finds the relevant passages
Passages are added to the model's context
Model answers, grounded in that text
The quality of a RAG system is capped by the quality of its retrieval step. If the search misses the relevant document, or buries it below irrelevant ones, the model never sees it — no amount of prompting fixes that afterwards. This is why RAG is fundamentally a search problem wearing an AI hat: it needs a retrieval engine that finds both the right keywords (lexical / BM25 search) and the right meaning (vector / semantic search), fast enough to sit in an interactive loop.
MCP is an open standard for connecting AI applications — chat clients, IDEs, coding agents — to external tools and data sources, through one common protocol instead of a custom integration per pair. Before MCP, giving an agent access to your search index, your database, and your ticketing system meant writing and maintaining a bespoke connector for each model and each tool. MCP defines a single client/server interface: any MCP-compatible client (Claude, Cursor, and others) can talk to any MCP server (a search engine, a file system, a CRM) without either side needing to know the other's internals in advance.
Exposes a tool or data source — e.g. a search index — as a set of callable, discoverable capabilities.
Lives inside the AI application or agent, discovers available servers, and calls their tools on the model's behalf.
In short: an AI agent is the actor, RAG is one of the things it can do, and MCP is the wiring that lets it do it against real, external systems without one-off integration code.
Put together, the three layer on top of each other:
An agent can run this cycle — decide, retrieve via MCP, augment its context, reason, decide again — many times over the course of a single task, often issuing several retrieval calls in parallel or in quick succession. That's a fundamentally different load pattern than a person typing one query into a search box: it's bursty, machine-speed, and unforgiving of slow or shallow retrieval.
SeekStorm is built specifically to be the retrieval layer underneath agents, RAG pipelines, and MCP — not retrofitted after the fact.
Lexical (BM25) and vector search run natively over the same index — no separate vector database to sync, which is exactly the retrieval quality RAG depends on: exact keyword matches and semantic matches, combined.
Plug SeekStorm into Claude, Cursor, or any MCP-compatible agent in minutes — the agent gets a search tool without custom integration code.
Documents are searchable the same millisecond they're indexed, so an agent's retrieved context never lags behind the underlying data.
Built in Rust for the request patterns agents actually generate — many queries, issued in parallel, with low and predictable latency.
In practice, that means the same SeekStorm index can back a human-facing search box and serve as the retrieval tool an AI agent calls through MCP — one portable index, no separate infrastructure for the "AI version" of your search.
The MCP server can be pointed at an index in one of two ways, and picking the right one is really the main architectural decision when wiring SeekStorm into an agent:
The MCP server links the seekstorm library directly and owns its own local index — no separate seekstorm_server process needed. Single binary, zero network hop, launched over stdio as a child process the same way most local MCP servers work today. A natural fit for agentic search over local docs: a coding agent indexing a repo, or a RAG pipeline indexing a knowledge base on the same machine.
The MCP server instead talks to an existing seekstorm_server over its REST API (or, in future, a native RPC), using the Rust REST client crate internally. This is what you want for shared or multi-tenant deployments, or anywhere the index already lives on a separate server.
Run the MCP server embedded with a local index for agentic search over your own docs or repo, or point it at a remote seekstorm_server for shared, multi-tenant deployments — either way, your agent gets real, grounded retrieval in minutes.