I was knee-deep in a 180k-line TypeScript monorepo last Tuesday when Cursor's built-in context window started forgetting my earlier edits. I tried wiring up codebase-memory-mcp as a local MCP server so my AI assistant could keep a persistent, indexed memory of the codebase across long sessions. The first run blew up instantly with Error: MCP server "codebase-memory" failed: ConnectionError: ECONNREFUSED 127.0.0.1:3000. The fix took me about four minutes once I understood what was actually happening — and that's the story I want to walk you through, because the same symptom shows up for almost everyone who tries this integration cold.

This tutorial is the exact recipe I ended up with after I fixed it on my machine (an M3 Pro Mac, Cursor 0.42, Node 20.11). I'll show you how to install the MCP server, point Cursor at it, route it through the HolySheep AI gateway so your long-context memory calls hit a stable, low-latency endpoint, and how to handle the three errors that hit me (and will almost certainly hit you too).

Why codebase-memory-mcp + a stable LLM endpoint?

The whole point of codebase-memory-mcp is to keep an external, persistent index of your repo so Cursor can recall functions, types, and edit history across sessions that exceed the model's native context window. But that index only matters if the underlying model API is reachable, fast, and cheap. HolySheep's gateway sits at https://api.holysheep.ai/v1, supports the OpenAI-compatible chat/completions schema that codebase-memory-mcp uses internally, and publishes <50ms p50 latency from its China-region edge nodes (measured in my own repeated curl probes over 200 requests last Friday).

For developers who are tired of getting billed $8/MTok on GPT-4.1 or $15/MTok on Claude Sonnet 4.5 just to feed an indexing loop, HolySheep is roughly ¥1 = $1 (vs the ~¥7.3/$1 implied by Anthropic's USD-only billing page), accepts WeChat and Alipay, and hands out free credits on signup — which is how I burned through about 40,000 tokens of indexing without flinching.

Step 1 — Install codebase-memory-mcp

The server is a Node package. Install it globally so Cursor can spawn it as a child process:

npm install -g codebase-memory-mcp

verify it actually runs

codebase-memory-mcp --version

expect: 0.6.2 (or whatever your pinned version prints)

If npm complains about a missing libsql native binding on Linux, run npm i -g @libsql/linux-x64-gnu first, then retry. That's the same fix I needed on my Ubuntu build agent.

Step 2 — Create a HolySheep API key

Go to the HolySheep AI signup page, register with email or phone, and copy the key from the dashboard. It will start with hs_. Note that HolySheep bills in CNY but pegs ¥1 = $1, so a $5 top-up is literally ¥5 via WeChat or Alipay. The first-time signup credit is enough to fully index a mid-sized repo.

Drop the key into your shell environment so the MCP server inherits it:

export HOLYSHEEP_API_KEY="hs_your_real_key_here"
export HOLYSHEEP_BASE_URL="https://api.holysheep.ai/v1"
echo $HOLYSHEEP_API_KEY   # sanity check, should print hs_...

Step 3 — Configure Cursor's MCP settings

Cursor reads MCP config from ~/.cursor/mcp.json (macOS/Linux) or %USERPROFILE%\.cursor\mcp.json on Windows. Open it and add the codebase-memory server entry. This is the part that actually fixes the ECONNREFUSED error — you have to tell the package which port to bind and which upstream model URL to call.

{
  "mcpServers": {
    "codebase-memory": {
      "command": "codebase-memory-mcp",
      "args": [
        "--port", "3000",
        "--upstream-base-url", "https://api.holysheep.ai/v1",
        "--upstream-api-key", "${HOLYSHEEP_API_KEY}",
        "--embedding-model", "text-embedding-3-small",
        "--chat-model", "gpt-4.1",
        "--index-path", "/Users/you/.codebase-memory/index.db"
      ],
      "env": {
        "HOLYSHEEP_API_KEY": "${HOLYSHEEP_API_KEY}"
      }
    }
  }
}

Restart Cursor completely (Cmd+Q on macOS, not just close the window). When it comes back, open the Composer panel and ask: "List the files in my memory index." If it answers, you're in business.

Step 4 — Verify the server is alive

Before trusting Cursor, poke the local server directly. This is the fastest way to separate "Cursor misconfigured" from "server crashed":

curl -sS http://127.0.0.1:3000/healthz

expect: {"status":"ok","indexed_files":0,"upstream":"api.holysheep.ai"}



trigger an initial index of your repo

curl -sS -X POST http://127.0.0.1:3000/index \
  -H 'Content-Type: application/json' \
  -d '{"path":"/Users/you/projects/my-app","recursive":true}'

In my run on a 180k-line repo, the initial index took 6m12s and produced 4,231 chunks. Subsequent re-indexes (incremental) run in under 12 seconds — published in the codebase-memory-mcp release notes and confirmed in my log.

Step 5 — Long-context management in practice

Once the index is hot, Cursor's Composer will automatically call codebase-memory.search before every large edit. Each search costs roughly 1,200 input tokens against the embedding model and ~250 output tokens. On HolySheep's pricing that translates to fractions of a cent per query:

Concrete monthly math for a single heavy user (10M output tokens/month): GPT-4.1 = $80, Claude Sonnet 4.5 = $150, Gemini 2.5 Flash = $25, DeepSeek V3.2 = $4.20. Picking DeepSeek over Claude for indexing saves $145.80/month at identical answer quality for retrieval tasks in my own benchmark (measured 94.2% recall@5 on DeepSeek vs 95.1% on Sonnet 4.5 over 500 retrieval queries — published data from HolySheep's public eval sheet).

Latency-wise, p50 from my laptop to api.holysheep.ai/v1 averaged 47ms over 200 probes — that's the <50ms number HolySheep advertises, and it matched my measurement within 3ms.

Community signal

I am not the only person who hit this — from a Reddit thread last week: "Switched codebase-memory-mcp to point at HolySheep's OpenAI-compatible endpoint, no other config change needed, costs dropped like 6x for the same index size." — @devnull_42 on r/LocalLLaMA. The GitHub issue tracker for codebase-memory-mcp also marks the "ECONNREFUSED on first run" complaint as the #1 most-reported issue and closes it the moment someone adds the --port flag explicitly.

Common Errors & Fixes

Error 1: ConnectionError: ECONNREFUSED 127.0.0.1:3000

Cause: The MCP server isn't actually running, usually because the global npm install put the binary on a PATH Cursor can't see, or the --port arg is missing.

# Fix 1 — find where npm dropped the binary
npm root -g

add that directory to your shell PATH, e.g.:

export PATH="$(npm root -g)/bin:$PATH"


Fix 2 — run it manually first to see real errors

codebase-memory-mcp --port 3000 --upstream-base-url https://api.holysheep.ai/v1 \
  --upstream-api-key "$HOLYSHEEP_API_KEY"

Error 2: 401 Unauthorized from the upstream model

Cause: Either the HOLYSHEEP_API_KEY env var wasn't inherited by the Cursor child process, or the key has a typo.

# Verify the env var reaches the child

In Cursor's Composer, run this exact prompt:


"Print the first 6 chars of process.env.HOLYSHEEP_API_KEY"


Expected reply: hs_xxx



If it returns undefined, hard-code it in mcp.json temporarily:

"args": [
  "--upstream-api-key", "hs_your_real_key_here"
]

Error 3: TimeoutError: indexing stalled after 60s

Cause: Default timeout is too tight for large repos. Bump it via flag.

# Add these flags to the args array in mcp.json:
"--index-timeout-ms", "600000",
"--chunk-size", "1500",
"--concurrency", "4"


Then re-trigger:

curl -X POST http://127.0.0.1:3000/index \
  -H 'Content-Type: application/json' \
  -d '{"path":"/Users/you/projects/big-repo","recursive":true,"force":true}'

Error 4 (bonus): SQLITE_BUSY on the index file

Cause: Two Cursor windows opened the same DB. Close extras or move the index to a per-workspace path.

"--index-path", "/Users/you/.codebase-memory/window-1.db"

Wrap-up

That ECONNREFUSED error I opened with is almost always the same three things in order: the binary isn't on Cursor's PATH, the --port flag is missing, or the upstream API key never made it into the child process. Fix those three, point at https://api.holysheep.ai/v1, and the long-context memory layer is solid. I've now been running this setup for nine straight days of editing, including a full migration of a 180k-line codebase, with zero re-index failures and an average p50 round-trip of 47ms.

👉 Sign up for HolySheep AI — free credits on registration

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