This MCP Server Analyzes Stock Data and Generates Charts in Seconds
- Nikhil Adithyan
- Oct 6
- 8 min read
A comprehensive introduction to Alpha Vantage’s MCP Server
You prompt Claude with this:
“Compare Apple and Microsoft on valuation, profitability, and price trends. Use live data. Keep it concise.”
A few seconds later, it responds with earnings yield, profit margins, trailing P/E, and a two-line summary on which company looks overvalued. No scraping. No custom pipeline. No wrapper.
That’s not just model magic. That’s the Alpha Vantage MCP server doing its job.
Over the past few months, MCP has quietly become one of the most interesting standards in the LLM world. It’s a protocol that bridges real-world data to LLMs in a way they can understand natively; no LangChain, no agents, no orchestration. Just schema-defined JSON, structured responses, and a clean feedback loop.
Alpha Vantage’s implementation caught my eye for one reason: it’s fast, and it’s complete. You get equities, fundamentals, forex, crypto, and economic indicators, all behind a simple schema that even Claude 3 can understand out of the box.
So I wanted to put it to the test. Not with a Hello World prompt or a “What is the PE ratio of Tesla?” kind of query. But by pushing it with real tasks. Real analysis. Some purely numbers-driven, some with visuals. The kind of stuff you’d expect from a finance analyst or quant-lite co-pilot.
And most importantly, I wanted to see if the model actually reasons with the data, or just regurgitates numbers.
Here’s how it went.
What exactly is Alpha Vantage’s MCP server?
You might be wondering what’s actually going on under the hood. How is a model like Claude able to pull structured market data on demand?
The answer is MCP or Model Context Protocol.
It’s a schema-based way of letting LLMs interact with external tools. Each MCP server defines its own set of functions, along with the input/output schema in a format the model understands. You don’t need to wrap anything in code. You just list the tool in your API call, and the model knows how to use it.
Alpha Vantage’s server exposes everything from daily OHLCV data to fundamentals, forex pairs, crypto pricing, and macroeconomic indicators. You can fetch time series, slice by symbols, choose frequency, or even sort by market cap, all through natural language.
What makes it efficient isn’t just the coverage. It’s the formatting.
The data comes clean. No nested junk, no parsing headaches. Even metrics like earnings yield or free cash flow margin are returned in normalized formats, so the model doesn’t waste tokens trying to make sense of them.
And when the response size gets large, it’s streamed, token by token, which avoids overwhelming the model’s context window. You’re not hitting a wall at 20 rows of JSON.
It’s smooth. Clean. LLM-native.
But all of that sounds great on paper. So I decided to run an actual agent through it and see what it’s like to work with this server in real time.
Setting up Alpha Vantage MCP Server
Getting started with the Alpha Vantage MCP server doesn’t take more than a few minutes, but there are a few specific steps to follow, especially if you’re using Claude or a custom agent.
Let’s walk through the process.
1. Add Custom Connector
Head over to the Connectors tab in the settings page of Claude and select Add custom connector. It will open a pop-up which looks like this:
Type in the name as ‘Alpha Vantage’ and insert this URL into the server URL field:
Make sure to replace YOUR_API_KEY with your actual Alpha Vahage API key. Once added, click ‘Add’.
2. Verify the connection
Now that you have added the custom connector, you can easily verify it by first starting a new chat on Claude. Then select the ‘Search and tools’ icon, which will show the added custom connector in the dropdown, like this:
Running Market Analysis via Prompts
Once the Alpha Vantage MCP server was connected to Claude, I wanted to test its actual utility.
Not with surface-level questions or one-line summaries, but with real analysis.
Could the agent retrieve structured financial data? Could it reason through metrics, compare companies, and highlight trends? More importantly, could it go beyond numbers and generate charts that reveal actual market dynamics?
To answer that, I broke the analysis into two blocks:
Numbers-based prompts: focusing on fundamentals, ratios, and trend reasoning
Visual prompts: focused on generating meaningful charts and drawing insights from them
Let’s start with the numbers.
1. Numbers-Based Analysis
Let’s begin with what the agent does best, pull raw financial data and reason through it.
Unlike typical language models that rely on stale knowledge, our setup connects directly to Alpha Vantage’s MCP server. This allows the agent to fetch real-time and historical financials, do multi-year comparisons, run valuation logic, and even explain trends.
To test the depth and accuracy, I gave it a sequence of prompts centered on Apple. The goal wasn’t just to extract numbers, but to see if the model could interpret them in a meaningful way.
Prompt 1: “Get me Apple’s income statement”
The agent returned Apple’s full FY 2024 income statement, neatly formatted and segmented into key highlights, cost breakdown, and tax information.
What impressed me:
Data was clean and updated (FY 2024 ending September 30)
Sections like EBITDA and R&D spending were broken out explicitly
The response wasn’t just copy-pasted; it felt structured for decision-making
Prompt 2: “How much did revenue grow last year?”
Here, the agent ran a year-on-year comparison of FY 2024 vs FY 2023.
It not only calculated the +2.0% YoY growth, but also contextualized it: Apple’s growth was modest, hinting at market saturation and mature phase dynamics.
The commentary was on point: short, realistic, and economically grounded.
Prompt 3: “Is their gross margin improving?”
Instead of giving a single year’s margin, the agent computed margins for FY 2022 to FY 2024 and tracked the trend:
2022: 43.3%
2023: 44.1%
2024: 46.2%
It then broke down the drivers behind the margin expansion: pricing power, shift to services, and cost efficiency.
This felt like something you’d see in a real earnings report breakdown.
Prompt 4: Valuation Comparison — AAPL vs MSFT vs GOOGL
The agent fetched valuation metrics for Apple, Microsoft, and Google, including P/E, PEG, Price-to-Book, EV/EBITDA, and then gave a direct opinion:
Apple was labeled Overvalued: stretched P/E and PEG with slower growth
Google came out Undervalued: best growth-adjusted multiples
Microsoft was deemed Fairly Valued
What stood out was the quality of interpretation. The response was clean, logical, and structured like a proper buy-side valuation memo.
Prompt 5: “How has Apple’s operating margin changed over the last 5 years?”
This was another highlight. The agent built a 5-year table from 2020 to 2024 and calculated the operating margin each year. It then computed the net improvement (+7.4 percentage points) and broke down each year’s delta.
But it didn’t stop there; it added insight:
Apple is becoming more efficient year after year
Scale and pricing power are kicking in
Still, its margin (31.5%) trails Microsoft’s 44.9%
It wasn’t just a number dump. It was analysis with perspective.
Prompt 6: “Show me companies with net income growth and low debt-to-equity”
Here, I wanted to see how well the agent can filter for compounders, companies with high growth and clean balance sheets.
The agent responded with a leaderboard-style answer:
NVIDIA came on top. 145% net income growth, D/E of 0.13
Alphabet and Microsoft also ranked high
Apple was specifically flagged for weak income growth (+2%) and high leverage (1.74 D/E)
This prompt showed the agent’s ability to rank, compare, and evaluate across dimensions, something that would take hours to do manually across multiple APIs.
Visual-Based Analysis
While the previous prompts focused on extracting and comparing raw numbers, I also wanted to test how well the agent could handle visual reasoning. Instead of just returning data, could it generate meaningful charts, and better yet, explain what they show?
This part matters because charts aren’t just decoration. They help surface patterns that aren’t obvious from a table. Lagging fundamentals, valuation spikes, post-event reactions; those are the kinds of things a trader or analyst looks for visually.
So I ran a set of prompts focused entirely on building charts and interpreting them. Here’s how that went.
Prompt 1: “Plot Apple’s quarterly net income on the same chart as its stock price for the last 5 years.”
This was a great overlay chart: net income and stock price, quarter by quarter.
The trend was clear:
From 2020 to 2023, the two lines moved tightly in sync
Every spike in net income echoed in the stock price
But in late 2024, something changed. Net income spiked, price didn’t
The agent flagged this divergence and suggested external factors might be at play: macroeconomic conditions, interest rate shifts, or lagging investor sentiment.
It wasn’t a canned chart. It was a reflection of fundamental drift.
Prompt 2: “Plot the stock price of Google in the 30 days following each of its past 4 earnings reports.”
Here, I wanted to test memory and comparative reasoning.
The agent pulled four distinct earnings dates and generated four separate post-earnings price curves, all on the same chart.
The result:
Q2 2025 (Jul 23) had a clear breakout, up nearly 20% in 30 days
The other three, Q1 2025, Q4 2024, and Q3 2024, were flat or mildly negative
You could see the inflection. One quarter stood apart
This wasn’t just a line chart. It was earnings momentum in visual form.
Prompt 3: “Plot Apple’s P/E ratio and stock price together for the last 3 years.”
This was a different kind of overlay, valuation vs price.
The chart showed a sharp divergence:
From early 2023 to mid-2024, P/E and stock price moved in tandem
But after that, P/E kept rising while the price plateaued
That disconnect hinted at something important: the valuation multiple was expanding faster than earnings growth.
This set up the next question naturally.
Prompt 4: “Based on this chart, what insights can you draw?”
This was the real test, could the agent interpret the visual?
It broke the answer down across four levels:
Valuation Expansion: The P/E ratio jumped ~60% while earnings stayed flat, suggesting multiple expansion, not earnings growth, was driving the stock.
Divergence Warning: The price dipped in mid-2024, but the P/E held steady. That’s usually a red flag. Either sentiment is decoupled from fundamentals, or a correction is coming.
Premium Valuation Risk: At a trailing P/E of 39, Apple is priced for perfection. Any earnings miss could trigger a multiple reset.
Investor Sentiment Shift: The agent speculated this might reflect Apple’s positioning as a “safe haven” tech stock, or investors chasing growth despite weak fundamentals.
It was the most nuanced answer of the entire test. Not just reading a chart, but reasoning from it.
Final Thoughts
This walkthrough set out to explore a simple question: What can an AI agent do when it’s paired with structured, high-quality market data?
The results speak for themselves. In the numbers section, the agent didn’t just relay values from the income statement. It calculated deltas, highlighted trends, and contextualized metrics like margins and valuation multiples. In the visual section, it went beyond charting by interpreting price-fundamentals divergence and event-driven patterns with clarity.
But this is still an early-stage capability. The charts can be improved, responses may sometimes lack depth, and not every filter or metric is supported yet. That said, none of these are dead ends. Every limitation is a prompt engineering or data expansion opportunity.
The real value here is the foundation. The MCP server brings real-time market context into the agent loop, unlocking workflows that were previously scattered across terminals, APIs, and Excel files.
As agents become more context-aware and Alpha Vantage continues refining the data interface, the future is clear: faster research, tighter feedback loops, and a shift from static analysis to interactive reasoning.
This is just the beginning and it’s already usable.