Buzzberg MCP

Private Beta: the MCP contract can change before broader beta. See CHANGELOG.md for breaking changes.

Buzzberg MCP connects Claude, Cursor, Cline, Continue.dev, and other MCP clients to Buzzberg market intelligence: trade ideas, sentiment, speakers, prices, and watchlist actions for your own Buzzberg account.

Connect Your AI Agent

1. Get Your Buzzberg Key

1. Open Buzzberg.
2. Go to Profile -> MCP Access.
3. Click New Key.
4. Copy the key that starts with bzb_.

Keep this key private. Treat it like a password.

2. Connect Your Client

Claude Desktop

Fast path:

pip install buzzberg-mcp
buzzberg-mcp setup --client claude-desktop

Paste your bzb_... key when setup asks for it, then fully quit and reopen Claude Desktop.

No pip / manual path:

1. Open your Claude Desktop config file:
   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Windows: %APPDATA%\Claude\claude_desktop_config.json
2. Add:

{
  "mcpServers": {
    "buzzberg": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote@0.1.38",
        "https://mcp.buzzberg.ai/mcp",
        "--transport",
        "http-only",
        "--header",
        "X-API-Key:bzb_YOUR_KEY_HERE"
      ]
    }
  }
}

3. Fully quit and reopen Claude Desktop.

Replace only bzb_YOUR_KEY_HERE with your key. Claude Desktop's local config supports stdio servers, so mcp-remote bridges Claude Desktop to Buzzberg's Streamable HTTP endpoint. Do not share screenshots of this config after adding your key. The bridge version is pinned and restricted to Streamable HTTP so it cannot fall back to the legacy SSE endpoint. Keep X-API-Key: exactly as shown, with no space after the colon. If Claude reports a timeout or disconnect, create a new key and replace only the bzb_... value; revoked keys cannot reconnect. Claude's Settings -> Connectors -> Add custom connector flow is coming after Buzzberg adds OAuth.

Claude Code

Ask Claude Code:

Add Buzzberg as an MCP server.
Use SSE URL https://mcp.buzzberg.ai/sse.
Use Authorization: Bearer from my BUZZBERG_MCP_API_KEY environment variable.

Or set it up manually:

export BUZZBERG_MCP_API_KEY="bzb_YOUR_KEY_HERE"
claude mcp add --transport sse buzzberg https://mcp.buzzberg.ai/sse \
  --header "Authorization: Bearer $BUZZBERG_MCP_API_KEY"

Codex

Codex uses Streamable HTTP.

Ask Codex:

Add Buzzberg MCP to my Codex config.
Use Streamable HTTP URL https://mcp.buzzberg.ai/mcp.
Use bearer_token_env_var = "BUZZBERG_MCP_API_KEY".
Do not paste the secret key into the config file.

Or edit ~/.codex/config.toml manually:

[mcp_servers.buzzberg]
url = "https://mcp.buzzberg.ai/mcp"
bearer_token_env_var = "BUZZBERG_MCP_API_KEY"

Then start Codex from a shell where the key is available:

export BUZZBERG_MCP_API_KEY="bzb_YOUR_KEY_HERE"
codex

OpenClaw

Ask OpenClaw:

Add Buzzberg as a remote MCP server.
Use Streamable HTTP URL https://mcp.buzzberg.ai/mcp.
Send Authorization: Bearer bzb_YOUR_KEY_HERE.

Or run this manually:

openclaw mcp set buzzberg '{"url":"https://mcp.buzzberg.ai/mcp","transport":"streamable-http","headers":{"Authorization":"Bearer bzb_YOUR_KEY_HERE"}}'

Cursor, Cline, Continue.dev

The helper can write the config for these clients too:

pip install buzzberg-mcp
buzzberg-mcp setup --client cursor      # or: cline / continue

More client-by-client setup options are in INSTALL.md.

What You Can Do With It

Buzzberg is useful when you want your AI agent to read the market conversation, not just fetch a price. Ask one plain-English question; Claude, Codex, or another MCP client chains the right Buzzberg tools and turns bounded market context into a usable research read.

In supported clients, Buzzberg also exposes these workflows through MCP prompts/list and context through resources/list, similar to Kaito-style MCP servers. Ask your agent to "list Buzzberg workflows" or use the manual prompts below.

Discoverable MCP prompts include:

• daily_alpha_brief
• top_speaker_market_tldr
• ticker_deep_dive
• narrative_map
• research_posts_alpha
• stock_list_candidates
• portfolio_update_tracker
• speaker_story
• sentiment_price_chart
• contrarian_scan
• keyword_mining

Discoverable MCP resources include:

• buzzberg://workflows
• buzzberg://data-boundaries
• buzzberg://top-speakers
• buzzberg://market/leaderboards

Start manually with these prompts:

Use Buzzberg to deep dive SIVE.
Who is talking about it, what is the core bull thesis,
what are the strongest bear risks or missing arguments,
and is this early discovery, building momentum, or crowded?

Use Buzzberg to give me today's top-speaker market brief.
What are the main themes, crowded trades, new tickers, and disagreements?
Quote examples.

Use Buzzberg to build a 7-day newsletter thesis map.
Show the strongest ticker narratives, key evidence, weak claims,
and what changed this week. Separate hard data from vibes.

Use Buzzberg to find first-order and second-order effects from this week's
YouTube market discussions.
Which tickers benefit directly, which suppliers or competitors are second-order
plays, and what risks are speakers worried about?

Use Buzzberg to map the SIVE narrative.
Combine top-speaker trade ideas, YouTube TLDRs, newsletter TLDRs,
sentiment, and recent source snippets.
Separate catalysts, evidence, repeated claims, and open questions.

Use Buzzberg research posts from the last 24h.
Find the strongest new alpha ideas, second-order beneficiaries,
repeated evidence, weak assumptions, and tickers worth a deeper dive.
Quote short examples.

Use Buzzberg stock-list posts from the last 7 days.
Which tickers appear across multiple lists, what theme links them,
which are fresh vs crowded, and which 10 should I research next?

Use Buzzberg portfolio-update posts from the last 7 days.
What did speakers add, trim, close, or size up?
Separate actual portfolio moves from generic commentary.

Use Buzzberg Twitter data from top-50 speakers.
How many times did they mention "bottleneck", "power", "AI capex", and "memory"?
Quote examples and point to the tickers each theme is about.

Use Buzzberg to build a 90d mentions vs price read for SIVE.
Show daily mention spikes, sentiment on those days, and price reaction.
Which days look like narrative ignition or narrative exhaustion?

Use Buzzberg to show Serenity's all-time trade ideas with thesis.
Limit it to 100 ideas and keep at most 5 ideas per day.
Which tickers did she mention most, what was her first idea,
where did she flip direction, and how have her views changed?

Use Buzzberg to analyze all trade ideas from Serenity about SIVE.
Show the first mention, latest mention, direction changes, thesis evolution,
and whether confidence increased or faded.

Use Buzzberg to rank the tickers most mentioned by top-50 speakers today.
Show mentions, sentiment, direction mix, source mix, and whether each story
looks fresh or already crowded.

What Buzzberg sends to the AI agent:

• Substack/newsletters: Buzzberg TLDRs, public previews where available, and extracted trade ideas for the last 7 days. Raw article bodies are not returned through MCP.
• YouTube: Buzzberg TLDRs and extracted trade ideas for the last 7 days. Raw YouTube source text and timestamped segment dumps are not returned through MCP.
• Twitter/X: top-speaker tweets from the last 24 hours where Buzzberg found ticker ideas, including the full tweet text, speaker, tickers, and direction: LONG, SHORT, WATCH, AVOID, or NEUTRAL. This is not every tweet. For multi-day research, use structured trade ideas rather than raw Twitter source text.
• Post-kind filters: source context can be narrowed to research, portfolio_update, stock_recommendation_list, news, or other, so an agent can ask specifically for research posts, stock lists, or portfolio moves.
• Ticker research: mentions, sentiment, trade ideas, speaker profiles, price snapshots, and daily sentiment/mention history for chart-style reads.
• Narrative research: ticker deep dives that combine trade ideas, source TLDRs, top-speaker tweets, sentiment, mentions, and price context.
• Speaker research: bounded trade-idea history for one author, plus speaker/ticker daily history for charts of changing views over time. Speaker history requires one speaker name, caps output at 200 ideas, and defaults to at most 10 returned ideas per calendar day.

Beta Rate Limits And Agent Etiquette

Buzzberg MCP is rate-limited during private beta. The current default limits are about 120 tool calls per minute per user and 2,000 tool calls per day per user, with an additional shared server-wide safety cap. These limits may change during beta as we tune capacity.

If your client receives 429 Too Many Requests, read the Retry-After header or retry_after_seconds field and wait that many seconds before continuing. Do not retry in a tight loop.

For best results, ask your agent to work in a bounded, staged way:

• Start with one broad scan or leaderboard, then do targeted follow-ups.
• Use limit, days, top_n, and max_per_day instead of unbounded scans.
• For author history, provide a speaker_name; Buzzberg does not expose a "dump all speakers' ideas" endpoint.
• Use batch tools such as get_tickers_overview for multi-ticker screens.
• Keep concurrency small; avoid dozens of parallel calls.
• Use write tools only when you explicitly want to change your watchlist or saved ideas.
• Use dry_run=True on write tools when you want your agent to verify the action without changing your account.
• Keep your bzb_... key in an environment variable or local config; do not paste it into shared prompts, code, or logs.

Contract notes:

• Trade-idea tools include idea_id values. Use those IDs with save_trade_idea.
• Price outputs include currency/namespace context where available. Do not assume every displayed number is USD; foreign listings such as SIVE can be native-currency prices.
• get_ticker_mentions uses ingestion time, meaning when Buzzberg added the mention. get_ticker_timeseries uses source publication date for chart rows, so totals may differ.
• Mention counts include visible LONG, SHORT, WATCH, NEUTRAL, AVOID, and CLOSE rows. Chart CSVs expose watch separately so early-interest signals do not disappear into neutral.
• get_speaker_profile separates alpha rank from credibility. Alpha rank is historical idea performance; credibility is a profile/source quality score.

Ready-made workflows:

• Daily source TLDRs — summarize the last 24 hours of top-50 speaker ticker-idea tweets, plus up to 7 days of Substack/newsletter and YouTube TLDRs; ask for themes, crowded trades, repeated words, disagreements, and quoted examples.
• Ticker leaderboards — most buzzed tickers, strongest bullish/bearish sentiment, and daily historical buzz for 1d / 7d / 30d windows.
• Morning briefing — AI portfolio state + fresh calls from top speakers + sentiment divergence radar in one read.
• Narrative ticker deep dive — what Buzzberg uniquely knows about a ticker: who is pushing it, what the bull narrative is, what is missing, and whether the setup is early or crowded.
• Speaker story / author history — first idea, recent ideas, stance changes, and speaker/ticker history for one author.
• Sentiment vs price chart — daily sentiment, mentions, and cached close prices so Claude can explain whether Buzzberg sentiment leads, confirms, or lags price.
• Mentions vs price chart — attention spikes vs price moves, useful for spotting narrative ignition, exhaustion, and post-move crowding.
• Contrarian scan — tickers where smart-money disagrees the most, ranked by sentiment spread. High-volatility setups where the camps are obvious.
• Build a watchlist from top-speaker signals — auto-curate first-time mentions and direction flips from the top-30 speakers in the last 24 hours.

Using It From Your Own Code

If you don't use Claude Desktop / Cursor / Cline / Continue, you can talk to the MCP server directly with the official Python SDK over Streamable HTTP:

import asyncio
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client

async def main():
    headers = {"Authorization": "Bearer bzb_YOUR_KEY_HERE"}
    async with streamablehttp_client(
        "https://mcp.buzzberg.ai/mcp",
        headers=headers,
    ) as (read, write, _):
        async with ClientSession(read, write) as session:
            await session.initialize()
            tools = await session.list_tools()
            print([t.name for t in tools.tools])  # 27 tools

            result = await session.call_tool(
                "get_sentiment",
                arguments={"ticker": "NVDA", "days": 7},
            )
            print(result.content[0].text)

asyncio.run(main())

If your agent framework only supports legacy SSE, use https://mcp.buzzberg.ai/sse with the same Authorization: Bearer ... header. Full walkthrough including Windows PowerShell and corporate-laptop ("no install allowed") paths is in INSTALL.md.

What Your Key Can Do

*Buzzberg receives only MCP requests and tool arguments selected by your client, not your full Claude transcript. Avoid putting sensitive private text into tool arguments.

Supported Clients

Buzzberg exposes two MCP transports:

• Streamable HTTP: https://mcp.buzzberg.ai/mcp for Claude Desktop, Codex, OpenClaw, and newer agents.
• Legacy SSE: https://mcp.buzzberg.ai/sse for Claude Code, Cursor, Cline, and older clients.

Tools, Prompts, And Resources

Buzzberg exposes 27 tools — read (search_trade_ideas, get_top_speakers, get_sentiment, get_ticker_timeseries, get_most_mentioned_tickers, get_top_sentiment_tickers, get_recent_source_text, get_tickers_overview, get_speaker_trade_ideas, get_speaker_ticker_history, get_portfolio, get_price, ...) and write (add_to_watchlist, save_trade_idea, ...). See TOOLS.md for signatures and per-tool examples in examples/.

Buzzberg also exposes MCP prompts and resources:

• prompts/list shows ready-made research workflows such as ticker deep dive, daily alpha brief, research-post extraction, stock-list candidates, speaker story, and sentiment/price reads.
• resources/list exposes lightweight context such as buzzberg://workflows, buzzberg://data-boundaries, buzzberg://top-speakers, and buzzberg://market/leaderboards.

Clients that do not support prompts/resources can still use all tools normally; these are additive capabilities, not a replacement for existing tools.

Trust And Verification

The normal path is pip install buzzberg-mcp. pip uses HTTPS and package hashes from the index for download integrity, but pip does not automatically verify Sigstore attestations.

Buzzberg releases use PyPI Trusted Publishing through GitHub OIDC. Attestations are available for manual verification — see SECURITY.md for the current verification status and threat model. (The exact pypi-attestations verify command will be published once the Test PyPI smoke confirms working syntax.)

Links

• INSTALL.md — simple setup by AI client
• TOOLS.md — full tool reference
• SECURITY.md — threat model, disclosure, attestations
• sessions/ — ready-made research workflows
• examples/ — per-tool example prompts
• CHANGELOG.md — breaking changes during private beta