
CNN Fear & Greed Index
io.github.wildsurferPublished by GitHub account wildsurferv2.2.0Updated Oct 24, 2026
CNN's Fear & Greed index for the US stock market: score, rating, indicators and daily history.
Overview
Lets an assistant fetch CNN's Fear & Greed Index for the US stock market, including the current score, rating, component indicators and daily history.
- What you can do
- Runs a local MCP server over stdio that exposes one tool, get_fear_and_greed, with an optional include_history argument. It returns the current 0-100 score and rating, values for the previous close, week, month and year, roughly a year of daily history, and the seven component indicators (market momentum, stock price strength, breadth, put/call options, volatility, junk bond demand, safe haven demand), each with its own score, rating and raw series. The same Go module also ships a CLI and a library.
- When to use it
- Useful when an assistant needs current or historical US market sentiment readings, for example to summarize market mood, compare today's score with past periods, or inspect which component indicators are driving the index. It is a read-only data source, not a trading or analysis engine.
- Requirements
- A local runtime: the cnnfag binary (installed via Go, or the published container image) started with the mcp argument. The MCP client must be able to locate the binary, so a full path may be needed if PATH is not inherited. Outbound network access to CNN's data endpoint is required; no accounts, API keys or environment variables are declared.
Installation
In SourceWeft
- Open CNN Fear & Greed Index in the dashboard and add it to a workspace.
- Enable the server for the chats that should use its tools.
Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.
Other MCP clients
Follow the launch instructions in the repository.
README
cnn-fear-and-greed-parse
[Go Reference] [CI] [Coverage Status] [License: MIT] [Glama score]
Go package for CNN's Fear & Greed Index, a 0–100 gauge of US stock market sentiment. It returns the current score and rating, the values for the previous close, week, month and year, about a year of daily history, and the seven component indicators (market momentum, stock price strength, stock price breadth, put/call options, market volatility, junk bond demand, safe haven demand), each with its own score, rating and history of raw values. The module also ships a CLI and an MCP server, and has no dependencies outside the Go standard library.
Install
Usage
Output:
An Indicator's History holds the raw underlying series (the S&P 500 level for momentum, the VIX level for volatility, ratios and spreads for the rest), and its Score is CNN's 0–100 normalization. To use your own http.Client (timeout, proxy), replace cnnfag.HTTPClient.
CLI
For cron jobs and shell pipelines, without writing Go:
-json prints the full result, including the daily history. -timeout changes the request timeout (default 15s).
MCP server
cnnfag mcp runs a Model Context Protocol server over stdio, so AI assistants can query the index. It exposes one tool, get_fear_and_greed, with an optional include_history argument. Configuration for MCP clients:
The client must be able to find the binary: use the full path (usually ~/go/bin/cnnfag) if your MCP client does not inherit your shell's PATH. Like the rest of the module, the server is built on the standard library only.
The server is also published to the MCP Registry as io.github.wildsurfer/cnnfag, with a container image at ghcr.io/wildsurfer/cnnfag for clients that prefer Docker over a local binary.
How it works
CNN does not offer a documented public API. This package requests the JSON endpoint that the Fear & Greed page itself uses:
The endpoint rejects requests that do not look like they come from a browser, so the package sends browser-like User-Agent and Referer headers. This is the same data source used by the known wrappers in other languages.
A scheduled CI job runs the test suite against the real endpoint once a week, so a change on CNN's side is detected within days.
Migrating from v1
v2 is a full rewrite: CNN removed the HTML page that v1 parsed, so v1 stopped working and its data model no longer matches what CNN publishes. Update the import path first:
Then adjust the calls:
Scores changed from rounded integers to the exact floats CNN serves, so 44 in v1 corresponds to something like 43.71 in v2. Round with %.0f or math.Round if you need the old look.
Project history
v1 (2021) parsed the HTML of money.cnn.com/data/fear-and-greed with goquery and could also download the index needle image. CNN removed that page, which broke parsing, and the image no longer exists. v2 (2026) is a rewrite on the JSON endpoint with a smaller API, historical data and zero dependencies. The last v1 release is tagged v1.2.0.
Data disclaimer
This package is not affiliated with or endorsed by CNN. The Fear & Greed Index and its values belong to CNN (Warner Bros. Discovery). The MIT license covers only the code in this repository and gives you no rights to CNN's data. CNN's Terms of Use permit personal use of site content and restrict commercial exploitation. If you use this data in a product, compliance is your responsibility, and the endpoint can change or disappear at any time.
License
Source: README.md at commit e1955e7
Tools
0Version history
1- v2.2.0LatestSep 16, 2026
Score
50% of the applicable checks met
How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.
- Reached in the last 30 days25 pts
Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.
- Auth declared10 pts
Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.
- Tools listed15 pts
Answer tools/list without a credential, or ship a package that starts in a sandbox without one.
- DescriptionRequired15 pts
- Logo10 pts
- Documentation15 pts
- License10 pts