Supercharge your AI with Keywords AI. See what your LLMs actually cost and how fast they run.

Q: How do I find out what models are available using listmodels?

listmodels gives you a catalog of all LLMs supported by the gateway. This is useful before writing code because it confirms which providers and versions your agent can actually connect to.

Q: What's the difference between getusagestats and getanalytics?

getusagestats gives you current, raw numbers—like total calls this month. getanalytics, however, provides deeper trends over time, showing cost growth curves and success rate averages.

Q: I need to check if my API key is active; what tool should I use?

Use checkkeywordsaistatus. It's the fastest way to verify connectivity. If that call fails, you know your problem isn't in your code; it's an authentication issue.

Q: Can I see which users are connected to my account?

Yes, run listusers. This provides a list of all team members associated with the API key and helps you manage access permissions across your organization.

Q: How do I check my remaining budget or credit balance using getcredits?

Use getcredits. This tool immediately shows your current available credits, as well as the total usage tracked for the current billing cycle.

Q: I have a specific API call ID; how do I get full details using getrequest?

Use getrequest. This pulls the entire context for any single request, including its input parameters and final execution outcome, which is great for debugging.

Q: How do I manage or review my configured performance thresholds using listalerts?

Use listalerts. This displays all active monitoring rules. You can view the set thresholds for cost limits, latency spikes, and error rates, and then adjust them as needed.

Q: I need to review every API usage log; should I use listrequests?

Use listrequests. This function provides a comprehensive history of all API calls made through the gateway, allowing you to filter logs by date range or other criteria.

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

Connect to your AI in seconds.

Keywords AI monitors your entire LLM API usage pipeline. It tracks costs, latency, and model performance across any provider using a unified gateway.

You get visibility into credit consumption and request logs so you know exactly how much every call is costing you.

What your AI can do

Check keywordsai status

Verifies if your API connection to Keywords AI is active and working.

Get analytics

Pulls the overall system dashboard, showing cost trends, success rates, and latency data.

Get credits

Retrieves your remaining API credit balance for immediate spending awareness.

+ 8 more capabilities included

View Usage Statistics

Get detailed metrics on total requests and consumed resources across your LLM models.

Track Credit Balance

Check your current credit balance to prevent unexpected service cutoffs.

Analyze API Logs

List and filter all past LLM API calls to understand patterns in your usage.

Get Performance Analytics

View cost trends, latency averages, and success rates for deep system analysis.

List Available Models

See a catalog of all LLM models you can connect to and use.

Ask an AI about this

Compatible AI Apps

OAuth 2.0 Compatible

Claude

ChatGPT

Cursor

Gemini

VS Code

JetBrains

Vercel

Zendesk

+ any other MCP app

Included with Plan

Waiting for input…

AI Agent

Keywords AI: 11 Tools for API Observability

These tools give you granular control over monitoring your LLM stack. You can list models, check credits, analyze logs, and pull deep performance metrics.

Make your AI actually useful.

Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.

Start using Keywords AI on Vinkius

Check Keywordsai Status

Verifies if your API connection to Keywords AI is active and working.

Get Analytics

Pulls the overall system dashboard, showing cost trends, success rates, and latency...

Get Credits

Retrieves your remaining API credit balance for immediate spending awareness.

Get Request

Gathers specific details about a single, recorded API request.

Get Usage Stats

Provides current and historical usage statistics for your LLM services.

Get User

Fetches details about the user account associated with this API key.

List Alerts

Reviews predefined monitoring thresholds and active system alerts.

List Models

Lists all LLM models that are currently available for connection and use.

List Requests By Model

Filters and lists all requests, showing logs specific to one chosen LLM model.

List Requests

Retrieves a general log of recent API calls made through the gateway.

List Users

Lists the team members who have access or are connected to this API account.

Connect to your AI in seconds. Security and governance baked right in.

Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.

Claude AI

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

Start a conversation

Open a new chat. The Keywords AI integration is available immediately — no restart needed.

Antigravity

Configure Agent Environment

Open your Antigravity agent's workspace configuration or mcp-servers.json file.

Bind the Endpoint

Add the Vinkius endpoint URL to your agent's MCP connections list:

"mcp_servers": {
  "keywords-ai": {
    "serverUrl": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
  }
}

Provide your secure token in place of [YOUR_TOKEN_HERE] to ensure your agent requests are authenticated.

Execute

Start your Antigravity session. The agent will autonomously discover and utilize the Keywords AI tools with full Vinkius guardrails applied.

VS Code Copilot

⚡

One-Click Install (Recommended)

In your Vinkius Dashboard, simply click the Add to VS Code button for this server. We'll automatically configure your local workspace.

Or configure manually

Open MCP Settings

Open VS Code, press Ctrl/Cmd + Shift + P, and search for GitHub Copilot: MCP Servers.

Add Server Config

Add the Vinkius endpoint configuration to your mcp-servers.json file:

"keywords-ai": {
  "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}

Ensure you replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.

LangChain

Install Dependencies

Install the LangChain MCP adapters for your environment:

pip install langchain-mcp-adapters

Connect the Server

Use the SSEClient in LangChain to connect to the Vinkius managed endpoint:

from langchain_mcp_adapters.client import SSEClient

# Connect to Vinkius
client = SSEClient(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
tools = client.get_tools()

CrewAI

Define the Tool

Load the Vinkius MCP tools into your CrewAI agents:

from crewai import Agent
from mcp_crewai import MCPTool

# Connect securely to Vinkius
vinkius_tools = MCPTool(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

# Assign to Agent
researcher = Agent(
    role='Data Researcher',
    tools=vinkius_tools.get_all()
)

Execute Task

Run your CrewAI process. The agent will autonomously route tasks to the Vinkius managed server.

Choose How to Get Started

Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.

Build Your Own

Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.

Import from OpenAPI, Swagger, or YAML specs
Create Agent Skills with progressive disclosure
Deploy to edge with MCPFusion framework
Built in DLP, auth, and compliance on every call
Real time usage dashboard and cost metering
Publish to catalog or keep private

Start building

Make Your AI Do More

Start with Keywords AI, then connect any of our 5,000+ other servers whenever your AI needs more. One click, no limits.

Use this MCP plus 5,000+ others, all in one place
Add new capabilities to your AI anytime you want
Every connection is secured and compliant automatically
Track usage and costs across all your servers
Works with Claude, ChatGPT, Cursor, and more
New servers added to the catalog every week

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Keywords AI. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

VINKIUS INFRASTRUCTURE

Cloud Hosted

Managed infra

V8 Isolated

Sandboxed per request

Zero-Trust Proxy

No stored credentials

DLP Enforced

Policy on every call

GDPR Compliant

EU data residency

Token Compression

~60% cost reduction

Your data is protected. See how we built it.

Works with Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This connection provides 11 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.

The headache of tracking API spending across multiple AI services.

Right now, every time your agent makes an LLM call, you're dealing with a scattershot mess. You have to log into Provider A's dashboard for latency metrics, jump over to Provider B's billing page to check credits, and then download a CSV from Provider C just to count the tokens used. It's painful, time-consuming, and frankly, unreliable.

With this MCP, you don't do any of that. Your agent uses the unified gateway to collect all that data—cost, latency, usage counts—and presents it instantly. You get a single report showing exactly which model is draining your budget and why.

Keywords AI: Centralized Visibility

You stop manually checking `get_credits` in one place, then running separate queries for usage on another. You also skip the tedious step of having to check if your API status is okay before even trying a test call.

Now, you simply ask for the stats, and it's there. The system handles all the aggregation, giving you immediate insight into performance without ever leaving your chat interface.

Support 24/7 support@vinkius.com ↗

Security Vinkius Trust Center ↗

SLA Service Level Agreement ↗

Report Listing Send Report ↗

What your AI can actually do with this

When running multiple large language models, tracking everything becomes a headache. Do you know which model is slow? Which one's chewing up your budget? This MCP connects to Keywords AI, giving your agent the necessary visibility into your LLM API stack. It acts as a central reporting hub for all your calls, letting you check usage statistics and view cost trends in one place.

You can list models available or review historical request logs to pinpoint performance bottlenecks immediately. If you're working with an array of different AI tools, connecting this MCP via Vinkius gives you a single pane of glass to monitor credit balances and overall API health.

Built · Hosted · Managed by Vinkius Keywords AI - Track LLM Costs & Performance

Server ID 019dd111-0170-7172-9d5d-208cedea6396

Vinkius Inspector

Compliance Grade A+

Score 98.33/100

Report View Report ↗

Here's how it actually works

The bottom line is that instead of jumping between provider dashboards, you get one place to monitor every dollar spent on LLM calls.

Connect your agent to this MCP using your Keywords AI account credentials.

Call tools like get_usage_stats or list_requests_by_model to pull the monitoring data.

The system aggregates and presents a clear report showing costs, latency metrics, and usage counts.

Who is this actually for?

This MCP is for the infrastructure architect or machine learning engineer who gets paid to keep things running. If your job involves managing multiple API endpoints and worrying about monthly cloud bills, you need this.

ML Engineer

They use list_models to evaluate which LLMs perform best for a specific task, comparing latency against cost.

Platform Architect

They run reports using get_analytics to forecast future spending and plan resource scaling.

DevOps Engineer

They monitor system health by checking the status with check_keywordsai_status before deploying new features that rely on AI.

What Changes When You Connect

Stop guessing about costs. Use get_credits to instantly check your remaining API balance before making a call, preventing service interruptions.

Pinpoint performance issues by running list_requests_by_model. This lets you compare the latency of GPT-4 versus Claude on the same task.

Avoid debugging manual dashboards. The get_analytics tool consolidates cost trends and success rates into one report, saving hours of spreadsheet work.

Know who's using what. Run list_users to see exactly which team members are connected to this API key and reviewing activity logs.

Scale confidently. By viewing the full model catalog with list_models, you can easily test new LLMs without knowing their pricing structure upfront.

See it in action

01 01

The Budget Overrun Discovery

A platform architect notices unexpected bills. They ask the agent to run get_usage_stats, which immediately highlights that a specific, older model is responsible for 70% of token usage and needs deprecation.

02 02

The Slow Feature Flag

A developer adds a new feature using an LLM. They ask the agent to run list_requests and find that every request is failing with a high error rate, pointing them straight to a needed credential update.

03 03

Model Performance Comparison

The ML engineer needs to decide between two models. They instruct the agent to list_requests_by_model for both candidates and compare the average latency and cost side-by-side before committing to one.

04 04

Security Audit

A manager needs to know who has access. Running list_users provides an immediate roster, ensuring only authorized personnel are using the API keys.

The honest tradeoffs

Ignoring Cost Metrics

Anti-pattern

You deploy a new agent assuming it's cheap. Then you get a massive bill because no one checked for high token usage or poor latency on the chosen provider.

The Fix

Before deployment, check get_usage_stats and review cost trends using get_analytics. This ensures your spend matches your expectations.

Manual Log Hunting

Anti-pattern

You have to manually export logs from three different provider dashboards just to see how many requests used Model X last week.

The Fix

Use list_requests_by_model to filter all necessary records for a specific model in one API call, saving immense time.

Assuming Connectivity

Anti-pattern

You write code that calls the LLM API without checking if your key is active or if there are current alerts, leading to runtime failures.

The Fix

Always start by calling check_keywordsai_status. This confirms connectivity and shows you any immediate system alerts via list_alerts.

When It Fits, When It Doesn't

Use this MCP when the primary pain point is visibility, cost accountability, or performance benchmarking across multiple LLM endpoints. If your process requires knowing how much an API call costs, or if you need to compare latency between Model A and Model B, this is your tool. Don't use it if you just need a simple list of users; for that, list_users works fine on its own. However, never rely on one single log view; always cross-reference the detailed logs from get_request with the aggregated trends from get_analytics. This combination gives you a complete picture: what happened, how much it cost, and if it was fast enough.

Questions you might have

How do I find out what models are available using `list_models`? +

list_models gives you a catalog of all LLMs supported by the gateway. This is useful before writing code because it confirms which providers and versions your agent can actually connect to.

What's the difference between `get_usage_stats` and `get_analytics`? +

get_usage_stats gives you current, raw numbers—like total calls this month. get_analytics, however, provides deeper trends over time, showing cost growth curves and success rate averages.

I need to check if my API key is active; what tool should I use? +

Use check_keywordsai_status. It's the fastest way to verify connectivity. If that call fails, you know your problem isn't in your code; it's an authentication issue.

Can I see which users are connected to my account? +

Yes, run list_users. This provides a list of all team members associated with the API key and helps you manage access permissions across your organization.

How do I check my remaining budget or credit balance using `get_credits`? +

Use get_credits. This tool immediately shows your current available credits, as well as the total usage tracked for the current billing cycle.

I have a specific API call ID; how do I get full details using `get_request`? +

Use get_request. This pulls the entire context for any single request, including its input parameters and final execution outcome, which is great for debugging.

How do I manage or review my configured performance thresholds using `list_alerts`? +

Use list_alerts. This displays all active monitoring rules. You can view the set thresholds for cost limits, latency spikes, and error rates, and then adjust them as needed.

I need to review every API usage log; should I use `list_requests`? +

Use list_requests. This function provides a comprehensive history of all API calls made through the gateway, allowing you to filter logs by date range or other criteria.

Can my AI track LLM costs? +

Yes. get_credits shows your balance, get_usage_stats breaks down costs by model and time period.

Can I filter request logs by model? +

Yes. list_requests_by_model returns only requests made to a specific LLM.

What analytics are available? +

get_analytics provides cost trends, latency percentiles, error rates, and token usage over time.

Connect to your AI in seconds.

Check keywordsai status

Get analytics

Get credits

Keywords AI: 11 Tools for API Observability

Make your AI actually useful.

Check Keywordsai Status

Get Analytics

Get Credits

Get Request

Get Usage Stats

Get User

List Alerts

List Models

List Requests By Model

List Requests

List Users

Connect to your AI in seconds. Security and governance baked right in.

Claude AI

Open Claude Settings

Add Custom Connector

Start a conversation

Claude Code

Open your terminal

Add the MCP Server

Start coding

Cursor

One-Click Install (Recommended)

Open Cursor Settings

Add New Server

Use in Composer

Antigravity

Configure Agent Environment

Bind the Endpoint

Execute

VS Code Copilot

One-Click Install (Recommended)

Open MCP Settings

Add Server Config

Windsurf

One-Click Install (Recommended)

Open Windsurf Settings

Add Server Endpoint

LangChain

Install Dependencies

Connect the Server

CrewAI

Define the Tool

Execute Task

Choose How to Get Started

Build Your Own

Make Your AI Do More

Works with Claude, ChatGPT, Cursor, and more

The headache of tracking API spending across multiple AI services.

Keywords AI: Centralized Visibility

What your AI can actually do with this

Here's how it actually works

Who is this actually for?

What Changes When You Connect

See it in action

The Budget Overrun Discovery

The Slow Feature Flag

Model Performance Comparison

Security Audit

The honest tradeoffs

Ignoring Cost Metrics

Manual Log Hunting

Assuming Connectivity

When It Fits, When It Doesn't

Questions you might have