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Enables AI agents to query curated Cartesi developer resources, documentation, and repository metadata through the Model Context Protocol. Provides read-only kn
Enables AI agents to query curated Cartesi developer resources, documentation, and repository metadata through the Model Context Protocol. Provides read-only knowledge access and host-side workflow guidance for building Cartesi blockchain applications via streamable HTTP.
Production-minded Model Context Protocol server that exposes curated Cartesi developer resources from PostgreSQL to AI agents over streamable HTTP.
mcp[cli] 1.26.x) with streamable_http_app() — use FastMCP’s Starlette app directly in production so session lifespan runs correctly (see create_app() in src/main.py).src/core/), DB session and models (src/db/), repositories, domain service (src/domain/resource_service.py), schemas, formatters, and server modules under src/server/.allowed_hosts / allowed_origins in src/server/server.py (extend for your deployment hostname).GET /healthz returns {"status":"ok"} alongside the MCP route.Knowledge responses are metadata and links (titles, URIs, canonical_url, doc routes). They do not include full fetched page bodies; agents should fetch external URLs when they need raw HTML or markdown.
Workflow tools (prepare_cartesi_*, send_input_to_application, prepare_*_deposit_instructions, get_cartesi_app_logic_guidance) only return instructions and command templates for the user’s machine. They do not run the Cartesi CLI, cast, or chain RPC from this server.
pyproject.toml; the included Dockerfile uses Python 3.12).src/db/models.py and ResourceService.Copy .env.example to .env and adjust. Defaults and field names are defined in src/core/config.py (notably DATABASE_URL, APP_HOST, APP_PORT, MCP_BASE_URL, pagination limits).
Using uv (recommended):
uv sync
Using pip:
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python -m src.main
uv run python -m src.main
uv run uvicorn src.main:create_app --factory --host 0.0.0.0 --port 8000
The MCP endpoint is streamable HTTP at:
http://<host>:<port>/mcp (default: http://0.0.0.0:8000/mcp)The repository includes a multi-stage Dockerfile that installs dependencies with uv and runs python -m src.main. Set DATABASE_URL and other env vars at runtime (for example via -e or your orchestrator).
Use MCP Inspector or any MCP-compatible client and connect to:
http://localhost:8000/mcp
| URI | Purpose |
|---|---|
cartesi://health |
Server name, environment, MCP_BASE_URL, read-only flag, capabilities, content policy |
cartesi://resources |
Catalog: index of resource URIs, tool names, prompts, and suggested agent flow |
cartesi://resources/{resource_id} |
Normalized resource metadata |
cartesi://docs/{resource_id} |
Documentation resource view (same shape; non-doc IDs error) |
cartesi://docs/routes/{route_id} |
Single doc route with parent context |
cartesi://repositories/{resource_id} |
Repository sync / freshness metadata |
cartesi://collections/tag/{tag} |
Resources grouped by tag |
cartesi://collections/source/{source} |
Resources grouped by source |
These are the name= values clients see (Python handler names may differ).
Knowledge
summarize_knowledge_base — coverage, counts, orientationget_knowledge_taxonomy — known tag and source titlessearch_knowledge_resources — search by query, tag, source, kindget_resource_detail — one resource by ID, optional routeslist_resource_doc_routes — routes for a documentation resourcesearch_documentation_routes — search routes across resourceslist_resources_for_tag / list_resources_for_sourceget_repository_sync_statusbuild_debugging_context — issue-focused bundle of resources and routesHost-side Cartesi workflow (instructions only)
prepare_cartesi_create_command — stable v1.5.x vs alpha v2.0 create guidanceprepare_cartesi_build_commandprepare_cartesi_run_commandsend_input_to_application — InputBox + cast templatesprepare_erc20_deposit_instructions — ERC20Portal flowprepare_erc721_deposit_instructions — ERC721Portal flowprepare_erc1155_deposit_instructions — ERC1155SinglePortal flowget_cartesi_app_logic_guidance — address-book, portals, vouchers, notices, reportsdebug_cartesi_issue — structured debugging using curated knowledgefind_cartesi_docs — doc route discovery for a topicexplain_repository_context — repository resource + status summaryДобавь это в claude_desktop_config.json и перезапусти Claude Desktop.
{
"mcpServers": {
"cartesi-knowledge-mcp-server": {
"command": "npx",
"args": []
}
}
}Web content fetching and conversion for efficient LLM usage.
Retrieval from AWS Knowledge Base using Bedrock Agent Runtime.
Provides auto-configuration for setting up an MCP server in Spring Boot applications.
A very streamlined mcp client that supports calling and monitoring stdio/sse/streamableHttp, and can also view request responses through the /logs page. It also