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DeepContext MCP – Large Codebase Search for Codex and Claude Code

Hacker News

DeepContext MCP – Large Codebase Search for Codex and Claude Code

Hey HN! We’re open-sourcing DeepContext MCP, a semantic search tool for Claude Code, Codex CLI, and any coding agent that supports MCP. It lets agents query for meaningfully relevant code instead of relying only on keyword-based matches. You can try it here: https://wild-card.ai/deepcontext We originally built this at Wildcard while working on background coding agents that take a ticket and return a PR. One of the persistent challenges was surfacing the right code context to steer the agent, especially for tickets that were less explicitly described. Most coding agents today rely on ripgrep or keyword-based search to pull in files. This works fine on small vibe-coded projects, but breaks down on real-world repos with deep folder structures and inconsistent naming. We kept seeing two failure modes: 1. The query pulls in too many loosely matched files and overwhelms the context window. 2. The actual code snippets the agent needs are missed or buried. DeepContext is our solution to this. The agent automatically generates semantic queries based on the user’s prompt, and calls DeepContext as a tool to retrieve relevant code chunks designed to fit in context. We currently support TypeScript and Python symbols. We saw that existing AI coding tools like Claude Code and Codex CLI also rely on ripgrep, and thought this might be useful for devs using these tools too. So we pulled it out as a standalone MCP server and open-sourced it. We’d love for you to try it out and share any feedback. You can browse the code here: https://github.com/Wildcard-Official/deepcontext-mcp And install the MCP here: https://wild-card.ai/deepcontext

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor
99%99% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, io · Missing: https docs, excited, just released
36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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