css-code-search-mcp

name	css-code-search-mcp
description	Deploy a CSS/OpenSearch code-search MCP server on Huawei Cloud ECS from scratch, so that Claude Code (claude-glm) can search a GitHub repo's code and docs as native MCP tools. Use when the task is to provision a new Huawei Cloud CSS cluster and ECS, index a code repository into CSS, and expose it as a searchable MCP tool for AI coding agents.

CSS Code Search MCP on Huawei Cloud ECS

Provision a Huawei Cloud CSS (OpenSearch) cluster, index a GitHub repository into it, and expose a FastMCP search server so that claude-glm (or any MCP client) can call search_code, list_skills, and get_file as native tools — all from a single skill in two phases.

When to Use This Skill

• A team wants their Cloud Adoption Skills repo (or any code repo) searchable by AI coding agents without cloning the full repo locally.
• An AI coding agent needs fast, server-side code search across hundreds of files with metadata filtering (category, skill name, file extension).
• You are setting up a new ECS + CSS environment from zero and want search-as-a-tool from day one.
• You already have an ECS + CSS and want to add or re-index a code search MCP.

Architecture

claude-glm (local laptop)
  └── mcpServers.css-search (type: http)
        ↓ HTTP/SSE
      nginx :8788/css/mcp          ← SG port, proxies both MCPs
        ↓
      FastMCP :8789                ← CSS Code Search MCP (bearer-auth)
        ↓
      CSS/OpenSearch :9200         ← Huawei Cloud CSS cluster
        └── index: <repo-name>     ← chunked repo files

ECS host
  ├── nginx :8788                  ← reverse proxy (SearXNG /mcp + CSS /css/mcp)
  ├── SearXNG MCP :18788          ← existing web search MCP (moved behind nginx)
  ├── CSS Code Search MCP :8789   ← this skill's FastMCP server
  └── CSS cluster                 ← 192.168.0.x:9200 (private subnet)

Required Inputs

Confirm before starting:

• Huawei Cloud AK/SK with ECS, EVS, EIP, VPC, CSS create/list scope in the target region.
• Huawei Cloud Region and Project ID (e.g. la-north-2 / afc631438d8941d0b10aaa2cee2bf94c).
• GitHub repo URL to index (e.g. https://github.com/binrogithub/1-3-Cloud-Adoption-Skills).
• Allow-list CIDR for the laptop calling the MCP (auto-detected if not provided).
• MCP bearer token — generated automatically by the deploy script.

Core Rules

• Never write AK/SK, MaaS keys, or bearer tokens into committed files. Use env files with 0600 permissions or $VAR_NAME placeholders.
• All exposed ports are CIDR-locked to the operator's current outbound IP. Never widen to 0.0.0.0/0.
• Security group ports: 22 (SSH), 4000 (LiteLLM), 8788 (nginx MCP proxy). Do not open 9200 externally.
• Reuse the existing VPC/subnet when the project is at the router/VPC quota.
• CSS disk type: prefer COMMON; fall back to HIGH if CSS.0065 The disk has been sold out.
• Ubuntu ECS images often log in as root, not ubuntu. Test both.
• CSS creation is asynchronous: poll cluster details until status is 200 and an endpoint is returned.
• Chunk size: 8000 chars with 500-char overlap balances context window usage and search precision.
• nginx co-hosting: if an existing MCP server (e.g. SearXNG) already uses port 8788, move it behind nginx on an internal port and route both by path prefix.

Deployment Workflow

Phase 1: Provision CSS + ECS

1. Install SDK dependencies

pip install huaweicloudsdkcore huaweicloudsdkcss huaweicloudsdkvpc \
            huaweicloudsdkecs huaweicloudsdkiam

2. Run the provisioner

export HWC_ACCESS_KEY_ID="..."
export HWC_SECRET_ACCESS_KEY="..."
export HWC_PROJECT_ID="afc631438d8941d0b10aaa2cee2bf94c"
export HWC_REGION="la-north-2"
export HWC_REPO_URL="https://github.com/binrogithub/1-3-Cloud-Adoption-Skills"

python3 scripts/provision_css_search.py

This creates:

• VPC/subnet/security group (or reuses existing if HWC_VPC_ID/HWC_SUBNET_ID set)
• CSS cluster (COMMON disk, auto-fallback to HIGH on CSS.0065)
• ECS with EIP (s6.xlarge.2, Ubuntu 22.04, 100GB SSD)
• SSH keypair
• State JSON: css-search-<timestamp>-state.json

3. Poll for readiness

python3 scripts/poll_readiness.py css-search-<timestamp>-state.json

This waits for:

• CSS cluster status = 200 (available, typically 10-15 min)
• ECS status = ACTIVE (typically 2-3 min)

Then discovers and writes to state:

• css_endpoint: e.g. http://192.168.0.57:9200
• ecs_public_ip: e.g. 101.44.184.244

Phase 2: Index + MCP

4. Deploy code search MCP

bash scripts/deploy_css_mcp.sh css-search-<timestamp>-state.json

This single script (reads state JSON):

• Verifies CSS connectivity from ECS
• Clones and indexes the repo into CSS
• Deploys the FastMCP server on port 8789
• Configures nginx reverse proxy on port 8788
• Prints the claude-glm MCP config

To index a different repo than the one in state:

bash scripts/deploy_css_mcp.sh css-search-*-state.json https://github.com/org/other-repo

5. Register MCP in claude-glm

Add the printed config to ~/.claude-glm-config/.claude.json:

{
  "mcpServers": {
    "css-search": {
      "type": "http",
      "url": "http://<ECS_PUBLIC_IP>:8788/css/mcp",
      "headers": {
        "Authorization": "Bearer <MCP_TOKEN>"
      }
    }
  }
}

Restart claude-glm. The three tools are now available:

Tool	Description
mcp__css-search__search_code	Search indexed code/docs by query, with optional category/skill filter
mcp__css-search__list_skills	List all categories and skills with document counts
mcp__css-search__get_file	Retrieve full file content by repo path

6. Validate

# Test the MCP endpoint from the laptop:
curl -s -H "Authorization: Bearer $MCP_TOKEN" \
     -H "Accept: application/json, text/event-stream" \
     http://$ECS_PUBLIC_IP:8788/css/mcp

# Or use the MCP SDK:
python3 -c "
import asyncio
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession

async def test():
    async with streamablehttp_client(
        url='http://$ECS_PUBLIC_IP:8788/css/mcp',
        headers={'Authorization': 'Bearer $MCP_TOKEN'},
    ) as (read, write, _):
        async with ClientSession(read, write) as session:
            await session.initialize()
            tools = await session.list_tools()
            print(f'{len(tools.tools)} tools:', [t.name for t in tools.tools])
            result = await session.call_tool('search_code', {'query': 'test', 'size': 1})
            print('Search works:', len(result.content) > 0)

asyncio.run(test())
"

Re-indexing After Repo Updates

ssh -i $SSH_KEY root@$ECS_IP "
  cd /tmp && rm -rf $REPO_NAME
  git clone --depth 1 $REPO_URL
  python3 /opt/index_repo_to_css.py /tmp/$REPO_NAME http://<css_private_ip>:9200
"

Troubleshooting

Symptom	Cause	Fix
CSS.0065 The disk has been sold out	COMMON disk unavailable in AZ	provision_css_search.py auto-retries with HIGH
VPC.0114 Quota exceeded for resources: ['router']	VPC quota exhausted	Set HWC_VPC_ID and HWC_SUBNET_ID to reuse existing
CSS polling times out after 20 min	CSS creation failed (status 303)	Check cluster status in Huawei Cloud console; look for failed_reason
ECS polling times out	ECS creation failed	Check ECS status in console; verify flavor available in AZ
curl :9200 returns connection refused from ECS	CSS not ready or IP changed	Scan subnet for port 9200; poll CSS cluster status
MCP returns 401 Unauthorized	Wrong or missing bearer token	Verify MCP_TOKEN matches between server and client config
MCP returns Missing session ID	Using curl without SSE transport	This is normal; MCP clients (Claude Code) handle SSE natively
nginx 502 Bad Gateway	MCP server crashed	Check /var/log/css_mcp_server.log; restart with systemd
SSH Permission denied	Wrong user or key	Try both root and ubuntu; verify key matches Huawei keypair
Search returns 0 results	Index not created or empty	Check curl http://<css_ip>:9200/<index>/_count; re-run indexer

Resources

• scripts/provision_css_search.py: Phase 1 — create CSS + ECS from scratch
• scripts/poll_readiness.py: Phase 1 — wait for CSS/ECS readiness, discover endpoints
• scripts/index_repo_to_css.py: Phase 2 — repo → CSS bulk indexer
• scripts/deploy_css_mcp.sh: Phase 2 — one-shot deploy (index + MCP + nginx)
• assets/config/css_mcp_server.py: FastMCP HTTP server with 3 tools
• assets/config/css-mcp.service.example: systemd unit
• references/css-indexing.md: indexing strategy, chunking, and mapping details
• references/mcp-transport.md: FastMCP HTTP/SSE transport and nginx proxy notes