Does it work with Oracle Autonomous Database?

Yes. Oracle Autonomous Database on OCI has ORDS enabled by default. Use the ORDS URL from your ADB instance (e.g., https://xxxxx.adb.us-ashburn-1.oraclecloudapps.com/ords), your schema name (typically ADMIN), and the admin credentials. The VECTOR type is available on all 23ai-compatible instances.

Can I run arbitrary SQL — not just vector searches?

Yes. The execute_sql_query tool accepts any valid Oracle SQL statement and returns results through ORDS. Add FETCH FIRST N ROWS ONLY to keep payloads manageable. This makes the agent useful for relational queries too, not just vector operations.

Which distance metrics are available for vector search?

Oracle 23ai supports COSINE and EUCLIDEAN (L2) distance metrics natively via VECTOR_DISTANCE. Specify the metric when running a search — cosine is recommended for most text embedding use cases, while L2 works better for image or audio embeddings.

Oracle Vector DB MCP Server for CrewAI 7 tools — connect in under 2 minutes

Name: Oracle Vector DB
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 7 Tools Framework

Connect your CrewAI agents to Oracle Vector DB through the Vinkius — pass the Edge URL in the `mcps` parameter and every Oracle Vector DB tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Oracle Vector DB Specialist",
    goal="Help users interact with Oracle Vector DB effectively",
    backstory=(
        "You are an expert at leveraging Oracle Vector DB tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token — get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Oracle Vector DB "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 7 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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About Oracle Vector DB MCP Server

Bring your Oracle Database 23ai vector capabilities directly into your AI agent workflow. Run VECTOR_DISTANCE similarity searches, inspect table schemas, execute SQL queries, and manage vector indexes — all through natural conversation.

When paired with CrewAI, Oracle Vector DB becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Oracle Vector DB tools autonomously — one agent queries data, another analyzes results, a third compiles reports — all orchestrated through the Vinkius with zero configuration overhead.

What you can do

Vector Similarity Search — Execute native Oracle 23ai VECTOR_DISTANCE queries with cosine or Euclidean metrics against any table with VECTOR columns
Schema Inspection — List all tables in your schema and describe column types, spotting VECTOR-enabled columns for embedding storage
SQL Execution — Run arbitrary SQL queries against Oracle via ORDS for ad-hoc analysis and data retrieval
Vector Index Management — List all HNSW and IVF vector indexes instantiated across your tables
Table Statistics — Get row counts and optimizer stats for capacity planning and query performance tuning
Version Check — Verify your Oracle runtime version to confirm 23ai vector feature compatibility

The Oracle Vector DB MCP Server exposes 7 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Oracle Vector DB to CrewAI via MCP

Follow these steps to integrate the Oracle Vector DB MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py — CrewAI auto-discovers 7 tools from Oracle Vector DB

Why Use CrewAI with the Oracle Vector DB MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Oracle Vector DB through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles — one agent researches, another analyzes, a third generates reports — each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass the Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Oracle Vector DB + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Oracle Vector DB MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Oracle Vector DB for raw data, then a second analyst agent cross-references findings and flags anomalies — all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Oracle Vector DB, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Oracle Vector DB tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Oracle Vector DB against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Oracle Vector DB MCP Tools for CrewAI (7)

These 7 tools become available when you connect Oracle Vector DB to CrewAI via MCP:

describe_table

Describe table columns and explicit data types including VECTORs

execute_sql_query

WARNING: Output payload size is inherently limited, restrict rows fetched (FETCH FIRST 100 ROWS ONLY) to ensure stability. Execute arbitrary SQL query against the Oracle runtime via ORDS

get_database_version

Get exact Oracle DB Runtime version banner

list_tables

List accessible tables in the current Oracle schema

list_vector_indexes

List specialized AI Vector search indexes (HNSW, IVF) instantiated

table_stats

Get table cardinality and optimizer statistics

vector_search

1, -0.4, 0.5]` against a strict `VECTOR` column natively inside Oracle DB, sorting and fetching the nearest neighbors. Execute Vector similarity search via Oracle 23ai native VECTOR_DISTANCE

Example Prompts for Oracle Vector DB in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Oracle Vector DB immediately.

"Show me all tables in my schema that have VECTOR columns."

"Find the 5 most similar documents to this embedding using cosine distance."

"What version of Oracle is running and does it support vectors?"

Troubleshooting Oracle Vector DB MCP Server with CrewAI

Common issues when connecting Oracle Vector DB to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts — check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

The Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Oracle Vector DB + CrewAI FAQ

Common questions about integrating Oracle Vector DB MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily — when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.