delegate-mcp

An MCP server that delegates reading — files or long context — to a cheap model, so your expensive orchestrator gets a tight answer instead of spending its own context window on raw text.

What it does

delegate-mcp exposes three Model Context Protocol tools — analyze, query, and resume — that hand a reading task to a cheap, high-context "reader" model behind an OpenAI-compatible API. Point analyze at a directory and it packs the files, asks the reader your question, and returns a short structured answer. The orchestrator never sees the raw files; it sees the conclusion.

Related MCP server: Middleman

Why

A capable orchestrator model is expensive per token. Reading is not the expensive part — reasoning is. When you make the orchestrator itself slurp a large directory or a long document into its context just to answer "where is X handled?" or "summarise this", you pay top-tier prices for bulk reading and you eat into context you'd rather keep for the actual task.

delegate-mcp splits the two: a cheap model does the bulk reading, the orchestrator keeps its context for reasoning. A directory that would cost tens of thousands of tokens to load comes back as a few hundred tokens of answer. You choose any OpenAI-compatible provider and model, so you set the price/quality trade-off yourself.

Install & Quick start

Requires Node.js ≥ 20.

# 1. Write a config file (see Configuration) with at least one provider.
# 2. Run the server (stdio transport):
npx -y delegate-mcp --config ./config.json

The server speaks MCP over stdio — you normally don't run it by hand; you register it with an MCP client and the client launches it. Register it like any stdio MCP server:

{
  "mcpServers": {
    "delegate": {
      "command": "npx",
      "args": ["-y", "delegate-mcp", "--config", "/absolute/path/to/config.json"]
    }
  }
}

(Works with any MCP-capable client — Claude Code, Claude Desktop, or your own host. Use an absolute config path, since the client sets its own working directory.) Once connected, the client lists three tools: analyze, query, resume.

On startup the server is fail-loud: a missing or invalid config, or a provider whose api_key env var is unset, aborts the launch with a clear message on stderr and a non-zero exit — it never serves in a half-configured state. stdout is reserved for the MCP JSON-RPC stream; all diagnostics go to stderr.

Configuration

The config is a single JSON file. Its path is resolved from three sources, first found wins:

1. CLI flag — --config <path> (also -c <path> or --config=<path>).

2. Environment — DELEGATE_MCP_CONFIG=<path>.

3. Home convention — the first of these that exists:

   • $XDG_CONFIG_HOME/delegate-mcp/config.json (default ~/.config/delegate-mcp/config.json)

   • ~/.delegate-mcp/config.json

   • ~/.delegate-mcp.json

If none is found, the server exits and prints every path it checked, in priority order. A leading ~ in any path field is expanded to your home directory.

config.example.json

{
  "providers": [
    {
      "name": "openai",
      "base_url": "https://api.openai.com/v1",
      "api_key": "env:OPENAI_API_KEY",
      "default_model": "gpt-4o-mini",
      "weight": 1,
      "timeout_ms": 60000,
      "max_input_tokens": 128000,
      "headers": {}
    }
  ],
  "disabled_providers": [],
  "session_dir": "~/.delegate-mcp/state/sessions",
  "metrics_file": "~/.delegate-mcp/state/metrics.jsonl",
  "default_max_output_tokens": 4096,
  "file_walker": {
    "max_file_bytes": 262144,
    "max_total_bytes": 4194304,
    "exclude_glob": ["coverage", "*.min.js", "src/generated/**"]
  }
}

Secrets

An api_key should be a reference to an environment variable, written "env:VAR":

"api_key": "env:OPENAI_API_KEY"

At load time this resolves to process.env.OPENAI_API_KEY. If that variable is unset, the server fails loud — naming only the variable, never a value. A literal key is tolerated but triggers a warning; secret values are never echoed in any log, warning, or error.

Disabling a provider

disabled_providers lists provider names to keep in the file but drop from the active pool — handy for parking a provider without deleting its block:

"providers": [ { "name": "openai", ... }, { "name": "backup", ... } ],
"disabled_providers": ["backup"]

A disabled provider is filtered out before validation, so an incomplete parked block never blocks startup. If every provider ends up disabled, startup fails.

Schema

Top level

providers[]

file_walker

Tools reference

Every tool returns a text result whose first line is a fixed header:

[delegate <tool>] provider=<name> model=<model> in=<input_tokens> out=<output_tokens> session=<id>

followed by the reader's answer. Every call also appends one line to the metrics file (success or failure). A failure — all providers down, unreadable work_dir, unknown session — comes back as an MCP error result (isError), never as a crash.

analyze

Pack a directory (or a single file) and answer a prompt about it.

Returns the header + answer, plus (when files were skipped or truncated) a one-line footer. The result header carries a session_id you can pass to resume.

// call
{ "work_dir": "/repo/src/auth", "prompt": "Where is the session token verified? Cite files." }

[delegate analyze] provider=openai model=gpt-4o-mini in=1843 out=176 session=7f3e9c02-...
Token verification happens in `session.ts`:
- `verifyToken()` (session.ts:88) checks the signature and expiry.
- It is called by the `requireAuth` middleware (middleware.ts:22) on every request.

[delegate analyze] packed 12 file(s), 3 skipped

query

A fileless one-shot question to the reader model.

Returns the header + answer, with a session_id for follow-ups.

// call
{ "prompt": "Summarise the tradeoffs of optimistic vs pessimistic locking in two bullets." }

[delegate query] provider=openai model=gpt-4o-mini in=24 out=98 session=b1a4d7e8-...
- Optimistic: no locks held; cheap under low contention, but retries/aborts on conflict.
- Pessimistic: locks up front; predictable under high contention, but risks blocking/deadlock.

resume

Continue a prior session with a new prompt, pinned to the same provider and model that served it. Continuity is reconstructed from the stored history.

Returns the header + answer. An unknown session — or one whose provider is no longer configured — is returned as an error result.

// call
{ "session_id": "b1a4d7e8-...", "prompt": "Now give a one-line rule of thumb for choosing." }

[delegate resume] provider=openai model=gpt-4o-mini in=142 out=41 session=b1a4d7e8-...
Rule of thumb: optimistic when conflicts are rare, pessimistic when they're the norm.

Agent usage

Delegate large reads to save tokens

You have a delegate-mcp MCP server with tools analyze, query, resume. It routes large-context reads to a cheap model so they don't fill your expensive context.

Default to delegating when you'd otherwise pull a large payload into context:

• Scanning a large file / whole directory for a fact or summary → analyze (work_dir + prompt)

• A big log / command output / web page you only need summarized → save to a temp dir and analyze it (or paste small inline data into query)

• A general question not needing your own reasoning → query

• A follow-up on a prior delegate answer → resume (session_id + prompt), pinned to the same model

Rule of thumb: raw content likely > ~5K tokens and you only need to find/summarize (not edit) → delegate and reason over the reply.

Read directly instead when: (1) about to edit the file; (2) it's tiny (< ~50 lines); (3) you need an exact quote/line number/byte-precise detail; (4) the delegate failed → fall back and say so.

Prompting: state the output shape (table/bullets/JSON); cap max_output_tokens; trust the answer (don't re-read the source to "double-check" unless a destructive action depends on it).

How it works

   MCP client (your orchestrator)
        │  analyze / query / resume
        ▼
   delegate-mcp
        │
        ├─ files ····· pack work_dir → prompt (exclude · cap · skipped list)
        ├─ providers · weighted-random order → sequential failover
        ├─ sessions ·· one JSON per session (pin provider + model on resume)
        └─ metrics ··· append one JSONL line per call (ok or error)
        │
        ▼
   cheap reader model (OpenAI-compatible)  ──►  tight answer + header

Provider pool

One client is built per provider at startup. On each call the pool computes a weighted-random order (roulette selection without replacement, by weight, default 1), then tries providers sequentially: any error — network, HTTP, malformed response — falls through to the next provider. If all fail, the call returns an error result naming the last failure. The number of failed attempts before the served one is recorded as failovers. resume uses a single-provider view of the pool so it stays pinned to the session's provider.

File packer

analyze walks work_dir with a deterministic depth-first traversal (the same tree always yields a byte-identical prompt) and serialises each file as a --- file: <path> --- block, followed by an explicit skipped list so nothing is dropped invisibly. Controls:

• Excludes — .gitignore-style. A pattern without / (node_modules, *.min.js) matches a path segment at any depth; a pattern with / (src/generated/**) is anchored to work_dir. Your exclude_glob is merged on top of an always-on default floor — node_modules, .git, dist, build, target, .next, __pycache__, *.lock, .env*, *.pem, *.key, id_*, *secret* — that cannot be dropped, so common secret-bearing files never leave your machine even if you override the list.

• Binary skip — files with a NUL byte, or a high ratio of control bytes, in the first 4 KiB are skipped (UTF-8 text, including non-Latin scripts, is preserved).

• Caps — max_file_bytes truncates a large file (with a marker); max_total_bytes stops the corpus and marks the rest total-cap-hit. Skip reasons are excluded, binary, total-cap-hit, read-failed, stat-failed.

Sessions

Each analyze / query writes one JSON file to session_dir (<session_id>.json); resume reads it back and appends the new turns. Persistence is full-overwrite with no garbage collection — old session files accumulate, and pruning them is your responsibility (they're plain files; clear the directory when you like). Session ids are validated to contain no path separators (traversal-safe).

Metrics

Every call appends one JSON line to metrics_file. Recording never throws: an I/O failure is swallowed (with a stderr warning) so a metrics problem can't take down the call it describes. Each line carries:

{"ts":"2026-01-01T12:00:00.000Z","uuid":"...","tool":"analyze","provider":"openai","model":"gpt-4o-mini","input_tokens":1843,"output_tokens":176,"input_bytes":72104,"duration_ms":1320,"status":"ok","session_id":"7f3e...","files_attached":12,"failovers":0}

An error line sets "status":"error", adds an "error" message, and reports provider / model as -. The file is JSONL — tail it, or feed it to any log / metrics pipeline.

Security & privacy

• File contents leave your machine. analyze sends the packed files to whichever external provider you configured. Do not point it at directories containing secrets, credentials, or personal data you aren't willing to send to that provider. The always-on exclude floor (.env*, *.pem, *.key, id_*, *secret*, …) reduces accidental leaks but is not a substitute for choosing work_dir deliberately.

• Keep the config out of version control. It references secrets; store the file with tight permissions:

  chmod 600 ~/.config/delegate-mcp/config.json

• Prefer env:VAR for API keys so the secret lives in your environment, not the file. Secret values are never printed by this server.

Troubleshooting

• no delegate-mcp config found — none of the three resolution sources located a file. The message lists every path checked; pass --config <absolute-path> or set DELEGATE_MCP_CONFIG.

• api_key references environment variable "X", but it is not set — export the variable in the environment the MCP client launches the server in (a client's env may differ from your interactive shell's).

• all N provider(s) failed; last error: … — every provider errored for that call. Check base_url, the model name, network, and quota; the last provider's error is quoted.

• Empty answer / truncated reasoning — some thinking models need output headroom. Raise default_max_output_tokens (or pass max_output_tokens on the call); the floor is 200.

• A file you expected wasn't read — check the skipped (…) tail in the analyze output for the reason (excluded, binary, total-cap-hit, …), and review your exclude_glob and caps.

Contributing

Issues and pull requests are welcome. Development:

npm ci
npm run lint       # eslint
npm run build      # tsup → dist/
npm test           # vitest
npm run typecheck  # tsc --noEmit

Please keep changes typed, tested, and provider-agnostic (the pool depends only on the OpenAI-compatible chat-completions surface).

License

MIT