CoderSwap MCP Server

Model Context Protocol (MCP) server that lets Claude (and any MCP-aware agent) stand up a topic-specific knowledge base end-to-end—project creation, ingestion, progress tracking, search validation, and lightweight session notes—without exposing low-level APIs.

Features

• 🚀 Create and list vector-search projects

• 📚 Ingest research summaries + URLs with auto-crawling, chunking, and embedding

• 🧠 Auto-ingest curated sources (crawl → chunk → embed) with relevance tuning handled by the CoderSwap platform team

• 🔍 Execute hybrid semantic search with intent-aware ranking

• 📊 Monitor ingestion jobs, capture blocked sources, and run quick search-quality spot checks

• ✨ Rich, formatted output optimized for AI agents

Installation

Configuration

Set the following environment variables before launching the server:

• CODERSWAP_BASE_URL (default: http://localhost:8000)

• CODERSWAP_API_KEY (required)

• DEBUG (optional: set to true for detailed logging)

Running

Development (Local Backend)

Claude Desktop Configuration

Update your Claude Desktop config file:

macOS/Linux: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

Local Development:

Production:

Available Tools

Project Management

• coderswap_create_project – Create a new vector search project

• coderswap_list_projects – List accessible projects with document counts

• coderswap_get_project_stats – Pull basic stats (created_at, document totals)

Research & Ingestion

• coderswap_research_ingest – Crawl, chunk, and embed vetted URLs (advanced tuning is managed by the platform team)

• coderswap_get_job_status – Poll ingestion job progress, crawl counts, blocked domains

Search & Validation

• coderswap_search – Execute hybrid semantic search with ranked snippets

• coderswap_test_search_quality – Run quick multi-query smoke tests (or a predefined suite) to gauge relevance

Session Continuity

• coderswap_log_session_note – Record lightweight summaries (job_id, ingestion metrics, follow-ups) so humans stay in the loop

Guardrails & Security

• The server loads mcp_starter_prompt.yaml at startup and injects it as a non-removable system prompt.

• Startup fails if the prompt is missing, invalid, or tampered with (hash mismatch).

• Advanced tuning endpoints are intentionally omitted; when deeper adjustments are required, Claude guides users to loop in the CoderSwap platform team.

• All operations must go through the MCP tools; direct HTTP/DB access is disallowed.

Each tool:

• ✅ Validates inputs with Zod schemas

• ✅ Returns both structured data and AI-friendly text summaries

• ✅ Includes comprehensive error handling

• ✅ Logs operations for debugging (when DEBUG=true)

Example Usage

Autonomous Research Workflow

Claude can execute this workflow autonomously:

1. Create a project:

   Use coderswap_create_project with name "AI Research"

2. Ingest research content:

   Use coderswap_research_ingest with URLs: - https://arxiv.org/abs/2103.00020 - https://openai.com/research/gpt-4

3. Monitor progress (Claude keeps polling until complete):

   Use coderswap_get_job_status to check ingestion

4. Search the knowledge base:

   Use coderswap_search with query "transformer architecture"

5. Optional: run a quick multi-query smoke test:

   Use coderswap_test_search_quality with test queries or run_full_suite: true

6. Leave yourself a handoff note (e.g., sources blocked, next steps):

   Use coderswap_log_session_note with project_id "proj_123", summary_text "Ingested 9/10 sources; FDA site blocked by robots.txt. Run follow-up after manual download." job_id "job_456" ingestion_metrics {"sources_succeeded": 9, "sources_failed": 1}

Output Format

Search results are formatted with rich details:

Debugging

Enable debug logging:

Logs are written to stderr and include:

• Timestamps

• Operation details

• Error messages with context

Development

Architecture

With the MCP server, Claude can autonomously build, test, and optimize vector knowledge bases in minutes! 🚀