Pre-computed market data that improves agent reasoning, reduces token usage, and replaces pipelines.
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com.tickerdb/mcp-server is an MCP server that pre-computed market data that improves agent reasoning, reduces token usage, and replaces pipelines. Its tool list has not been published yet over stdio, sse and http, requires no API key, and scores 86/100 on MCPpedia's security, maintenance and efficiency rubric.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"tickerdb": {
"env": {
"TICKERDB_KEY": "tdb_your_api_key_here"
},
"args": [
"tickerdb-mcp"
],
"command": "npx"
}
}
}Are you the author?
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Connect your agent to pre-computed market context that improves reasoning and reduces token usage.
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
npx -y 'wrangler' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
Five weighted categories — click any category to see the underlying evidence.
Wrangler affected by OS Command Injection in `wrangler pages deploy`
**Summary** A command injection vulnerability (CWE-78) has been found to exist in the `wrangler pages deploy` command. The issue occurs because the `--commit-hash` parameter is passed directly to a shell command without proper validation or sanitization, allowing an attacker with control of `--commit-hash` to execute arbitrary commands on the system running Wrangler. **Root cause** The `commitHash` variable, derived from user input via the `--commit-hash` CLI argument, is interpolated directl
Arbitrary remote code execution within `wrangler dev` Workers sandbox
### Impact The V8 inspector intentionally allows arbitrary code execution within the Workers sandbox for debugging. `wrangler dev` would previously start an inspector server listening on all network interfaces. This would allow an attacker on the local network to connect to the inspector and run arbitrary code. Additionally, the inspector server did not validate `Origin`/`Host` headers, granting an attacker that can trick any user on the local network into opening a malicious website the ability
Arbitrary remote file read in Wrangler dev server
### Impact Sending specially crafted HTTP requests and inspector messages to Wrangler's dev server could result in any file on the user's computer being accessible over the local network. An attacker that could trick any user on the local network into opening a malicious website could also read any file. ### Patches This issue was fixed in `wrangler@3.19.0`. Wrangler will now only serve files that are part of your bundle, or referenced by your bundle's source maps. ### Workarounds Configure Wr
Cloudflare Wrangler directory traversal vulnerability
### Impact The Wrangler command line tool (<=wrangler@3.1.0 or <=wrangler@2.20.1) was affected by a directory traversal vulnerability when running a local development server for Pages (wrangler pages dev command). This vulnerability enabled an attacker in the same network as the victim to connect to the local development server and access the victim's files present outside of the directory for the development server. ### Patches Wrangler2: Upgrade to v2.20.1 or higher. Wrangler3: Upgrade to v3
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Pre-computed stock market data for AI agents. TickerDB returns indicators like trend_direction, support_level, and analyst_consensus as named states — plus what changed and what usually happens next.
10,000+ US stocks, ETFs, and crypto pairs · 182 indicators across trend, momentum, volatility, volume, patterns, support/resistance, fundamentals, and sector context · 7 years of history · tickerdb.com
| Tool | Description |
|---|---|
get_summary | Technical + fundamental snapshot for a ticker. Historical lookups, state transition history, and what usually happens after |
get_ohlcv | Daily or weekly EOD candles for returns, charts, and backtests |
get_search | Screen assets by categorical state or rank by fields like market_cap or pe_ratio |
get_schema | Discover all 182 fields and their valid band values |
get_watchlist | Full analytical summary for every ticker on your saved watchlist |
get_watchlist_changes | What changed on your watchlist — day-over-day or week-over-week |
add_to_watchlist | Add tickers to your watchlist |
remove_from_watchlist | Remove tickers from your watchlist |
get_account | Account details, plan tier, and usage |
All tools are available on every tier (Free, Plus, Pro). Tiers differ by credit limits, history depth, number of filters, and watchlist size. See tickerdb.com/pricing.
Connect TickerDB to Claude, ChatGPT, or another MCP client (see Setup below), then try:
"Show me oversold large-cap stocks near support"
The agent calls get_search with filters for momentum_rsi_zone = oversold and market_cap_tier in [large, mega], then follows up with get_summary on individual results. No raw number crunching — the agent reads categorical states and reasons over them directly.
"What usually happens when AAPL goes oversold?"
get_summary with field=momentum_rsi_zone, band=oversold, stats=true returns aggregate aftermath distributions: how the stock performed 5, 10, 20, 50, and 100 days after each oversold entry over 7 years of history.
"What changed on my watchlist?"
get_watchlist_changes returns only the field-level state transitions since the last pipeline run — band entries, exits, and shifts — so the agent reports what moved without pulling full summaries for every ticker.
A model can compute RSI from raw bars. But ask "Does AAPL look bullish?" with raw OHLCV and it burns its context on arithmetic — computing indicators one by one — instead of doing what you actually asked: noticing that RSI just hit oversold while institutions are accumulating, that the pullback is sharp but the 200-day uptrend is intact, that insiders have been selling all quarter. That's the analysis. Raw bars bury it under computation.
With TickerDB, the model sees "oversold", "accumulation", "strong_uptrend" and connects them immediately.
State transitions go further. "What happened the last time BTC was this oversold?" means computing RSI across 7 years of daily bars, finding every oversold entry, and calculating what happened after each one. With TickerDB it's one call: get_summary with field=momentum_rsi_zone, band=oversold, stats=true.
The remote server at https://mcp.tickerdb.com/mcp supports OAuth 2.1 and Bearer token auth. Use Streamable HTTP transport (not legacy SSE).
| Client | How |
|---|---|
| Claude.ai | Settings → Connectors → Add → https://mcp.tickerdb.com/mcp → Authorize |
| Claude Code | claude mcp add --transport http --scope user tickerdb https://mcp.tickerdb.com/mcp |
| ChatGPT | Plugins → + → https://mcp.tickerdb.com/mcp → Create → Authorize |
| Cursor | .cursor/mcp.json → {"tickerdb": {"url": "https://mcp.tickerdb.com/mcp"}} |
| Any MCP client | Streamable HTTP to `https://mcp.ticke |