DeepContext MCP
PulseGate's liveness check found it on 4 Oct 2026; it is registered on GitHub and has been in the index since 4 Sep 2026. How this is checked
DeepContext MCP indexes TypeScript and Python codebases using Tree-sitter AST parsing, hybrid search, and incremental indexing. It connects to Codex, Claude Code, Cursor-like workflows, and other MCP coding agents to improve code retrieval across large repositories.
Inferred · not functionally tested
Overview
6 featuresPurpose: Finding relevant code in large TypeScript and Python codebases without relying on slow, token-heavy searches.
Inferred · not functionally tested
Audience: developers using AI coding agents
Inferred · not functionally tested
Functions: rag, code_generation
Inferred · not functionally tested
Interfaces: API: indicated (inferred, not tested) · MCP: indicated (inferred, not tested) · CLI: indicated (inferred, not tested) · Self-hosting: unknown
Recorded constraints: pricing: unknown · license: Apache-2.0 · platforms: WEB · deployment: browser, cli, api_only, cloud_managed
Constraint provenance is unknown; confirm requirements with the publisher.
Record sources: wild-card.ai · github.com. These links do not verify the individual claims.
In the RAG, search & retrieval space, DeepContext MCP takes a focused approach. Inferred · not functionally tested: It focuses on finding relevant code in large TypeScript and Python codebases without relying on slow, token-heavy searches. Inferred · not functionally tested: It is built as a B2B product for developers using AI coding agents. Basis unknown · not verified: It ships for the web, the command line, and API.
It is developed by Wildcard (United States), and it first shipped in 2025. Development happens publicly on GitHub with 276 stars. Inferred · not functionally tested: Key capabilities include AST Parsing, Hybrid Search, and Incremental Indexing. Inferred · not functionally tested: Catalogued interfaces include an MCP server.
Summary written by a language model from the project’s public pages.
Tasks: Inferred · not functionally tested
- AST Parsing
- Hybrid Search
- Incremental Indexing
- Semantic Search
- Large Codebases
- MCP Integration
Topics: Inferred · not functionally tested
Built with & integrations
- Next.js
- /_next/static/ in the HTML · __next_f in the HTML
Trust & compliance
Indexing history
1What PulseGate has recorded for this listing
- Indexed4 Sep · 01:31 UTCWildcard-Official/deepcontext-mcp seen via GitHub Search (Smart)Source: GitHub Search (Smart) · Open
Frequently asked questions about DeepContext MCP
- What is DeepContext MCP?
- Inferred · not functionally tested: DeepContext MCP focuses on finding relevant code in large TypeScript and Python codebases without relying on slow, token-heavy searches. It is catalogued under RAG, search & retrieval on PulseGate.
- Who should use DeepContext MCP?
- Inferred · not functionally tested: DeepContext MCP is a B2B product built for developers using AI coding agents.
- What platforms does DeepContext MCP run on?
- Basis unknown · not verified: DeepContext MCP runs on the web, the command line, and API.
- Is DeepContext MCP still active?
- PulseGate's liveness check found it on 4 Oct 2026.
- What are alternatives to DeepContext MCP?
- Similar projects tracked by PulseGate include ContextMCP, contextl-mcp, and deepset-mcp.ContextMCPcontextl-mcpdeepset-mcp
- Who makes DeepContext MCP?
- DeepContext MCP is developed by Wildcard, based in the United States.
- How long has DeepContext MCP been around?
- DeepContext MCP first shipped in 2025.
- Is DeepContext MCP open source?
- Basis unknown · not verified: DeepContext MCP has a public GitHub repository.
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