Vertex MCP Search (Gemini Deep Research)

A Model Context Protocol (MCP) server for Kilo Code, Hermes, and other MCP-compatible environments. This server leverages Google Vertex AI (GCP) and the Gemini 3.5 Flash model to perform intelligent deep research with high thinking capability and live Google Search grounding.

It exposes a single, highly capable tool:

deep_research(query: str, file_paths: list[str] | None = None)

Why Vertex MCP Search?

• Unmatched Quality: While typical search/research MCPs often struggle to deliver deep, context-aware answers, vertex-mcp-search utilizes Vertex Gemini 3.5 Flash with thinking_level="high" and native Google Search grounding. This allows the model to search across multiple websites, cross-reference sources, and compile highly accurate, reliable, and grounded answers.

• Cost-Effective (Free with GCP Credits): Running advanced search/research workflows can sometimes be costly. However, by running on Google Cloud Vertex AI, you can take advantage of GCP's $300 free trial credits, making this enterprise-grade deep research completely free to run.

Architectural Design

┌────────────────────────────────────────────────────────┐
│                      IDE / Client                      │
│                (Kilo Code, Hermes, etc.)               │
└──────────────────────────┬─────────────────────────────┘
                           │
                 Stdio (stdin / stdout)
                           │
                           ▼
┌────────────────────────────────────────────────────────┐
│                       server.py                        │
│                 (Lightweight FastMCP)                  │
└──────────────────────────┬─────────────────────────────┘
                           │
                     HTTP (localhost)
                           │
                           ▼
┌────────────────────────────────────────────────────────┐
│                       worker.py                        │
│         (Local Background Threaded HTTP Server)        │
└──────────────────────────┬─────────────────────────────┘
                           │
                    HTTPS / gRPC
                           │
                           ▼
┌────────────────────────────────────────────────────────┐
│               Google Cloud Platform (GCP)              │
│                Vertex AI (Gemini 3.5)                  │
├────────────────────────────────────────────────────────┤
│  ┌───────────────────────┐   ┌───────────────────────┐ │
│  │   Gemini 3.5 Flash    │   │ Google Search Ground  │ │
│  │ (high thinking mode)  │ ──►  (multiple websites)  │ │
│  └───────────────────────┘   └───────────────────────┘ │
└────────────────────────────────────────────────────────┘

Why This Dual-Process Architecture Works (And Why It's Better)

1. Immunity to stdio Hangs/Deadlocks: Standard search/research MCPs often execute heavy HTTP network calls, file reads, and multi-second AI generation tasks directly within the main stdio loop (server.py). On Windows systems especially, this often blocks or deadlocks the standard input/output channels, causing the MCP server or host IDE to freeze.

   By spinning up a separate, threaded local HTTP process (worker.py), the main stdio loop is kept completely clear. It only handles fast HTTP serialization to the local worker, preventing any possibility of stdio blockages.

2. Deeper & More Reliable Grounding: Instead of performing a single Google search query and fetching snippets (which typical search MCP servers do, often getting rate-limited or returning incomplete context), this implementation utilizes Vertex Gemini's native search grounding. The model itself can analyze, query, and cross-reference results from multiple websites concurrently inside a unified high-thinking session to synthesize a truly reliable, complete response.

What It Does

• Model locked to gemini-3.5-flash

• Uses Vertex/GCP auth so usage bills to the Google Cloud project

• Uses Google Search grounding for research/current facts

• Uses thinking_level="high"

• Accepts local file paths and gs:///HTTP(S) URIs

• Supports PDFs, images, code files, text, JSON, CSV, Markdown, and similar files

• Treats uncommon local text/config files like .jsonc, .env, .lock, .vue, .svelte, and extensionless config files as text/plain

• Returns structured JSON with answer, confidence, uncertainty notes, sources, search queries, and attached file metadata

• Returns named JSON errors for quota exhaustion, Vertex timeouts, unsupported file types, and worker startup problems

Related MCP server: Gemini Data Analysis & Research MCP Server

Setup

Install dependencies:

cd C:\Users\silen\Downloads\aweffes
pip install -r requirements.txt

Set up Google Cloud auth once:

gcloud auth application-default login
gcloud config set project funding-vikas

The configured project is currently:

funding-vikas

The server expects these environment variables from the MCP host:

GEMINI_AUTH_MODE=vertex
GOOGLE_CLOUD_PROJECT=funding-vikas
GOOGLE_CLOUD_LOCATION=global
GOOGLE_APPLICATION_CREDENTIALS=C:\Users\silen\AppData\Roaming\gcloud\application_default_credentials.json
GEMINI_WORKER_REQUEST_TIMEOUT_SECONDS=180

Optional tuning:

GEMINI_MODEL_REQUEST_TIMEOUT_MS=170000
GEMINI_HTTP_RETRY_ATTEMPTS=1

The default retry count is intentionally low. Vertex quota errors usually do not recover inside one MCP call, and long hidden retries make the host look frozen.

MCP Host Config

Kilo/Hermes should point at:

C:\Users\silen\Downloads\aweffes\server.py

with:

C:\Python313\python.exe

server.py automatically starts worker.py; you do not need to run the worker manually.

Usage

Simple research:

Use deep_research to research the latest MCP Python SDK setup with sources.

File coworker mode:

{
  "query": "Inspect this PDF and summarize the key claims. Cross-check anything current with web sources.",
  "file_paths": ["C:\\Users\\silen\\Downloads\\paper.pdf"]
}

Code file mode:

{
  "query": "Review this file and explain what the main function does.",
  "file_paths": ["C:\\path\\to\\script.py"]
}

For large code reviews, start narrow. A single 30-40 KB file can summarize well, but full multi-file review prompts may exceed the practical MCP/Vertex timeout, especially when quota is under pressure.

Output Shape

{
  "ok": true,
  "result": {
    "answer": "...",
    "confidence_score": 0.82,
    "uncertainty_notes": ["..."],
    "sources": [{"title": "...", "url": "https://..."}],
    "web_search_queries": ["..."],
    "attached_files": [{"path": "...", "mime_type": "application/pdf"}]
  }
}

Errors are returned as JSON instead of crashing stdio:

{
  "ok": false,
  "error": {
    "type": "worker_unavailable",
    "message": "Could not reach Gemini worker..."
  }
}

Common errors:

{
  "ok": false,
  "error": {
    "type": "quota_exhausted",
    "message": "Vertex AI returned 429 RESOURCE_EXHAUSTED..."
  }
}

This means the Google Cloud project/location is out of available Gemini quota for now. Wait, reduce request frequency/size, switch location/project, or request more Vertex quota.

{
  "ok": false,
  "error": {
    "type": "worker_timeout",
    "message": "Gemini worker did not return before the MCP timeout..."
  }
}

This usually means the prompt was too heavy for one call or Vertex was slow/rate limited. Try one file at a time, ask a smaller question, or retry after quota recovers.

Worker logs are written to:

C:\Users\silen\Downloads\aweffes\logs