How do I install Local Llm Worker?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Local Llm Worker need an API key?

The publisher lists these environment variables: LLW_BASE_URL, LLW_MODEL, LLW_SEARCH_URL.

What transport does Local Llm Worker use?

Local Llm Worker runs locally over stdio.

When was Local Llm Worker last checked?

Last checked 2026-10-16. Local LLM does Claude's bulk work: reads logs and files, researches the web, writes test-gated code.

Local Llm Worker MCP 服务器

localize

Let Claude hand the bulk reading to your local LLM: test logs, big files, web pages. Claude only gets the answer.

Formerly local-llm-worker. npm: localize-mcp

Works with Ollama · llama.cpp · LM Studio · vLLM · LocalAI · any OpenAI-compatible endpoint

Reading a 5,000-line test log or three docs pages costs the same frontier-model tokens as hard architectural work. localize is an MCP server and Claude Code plugin that moves that bulk work onto the GPU (or CPU) you already own:

offload: your local model runs the noisy command or reads the big file. Claude gets the answer.
research: your local model searches the web, reads the pages in full and returns a cited answer. Claude never sees the pages.
delegate (opt-in): your local model writes one file in an isolated git worktree and retries until a test Claude wrote first passes. Claude gets a one-line verdict, not the code.

> offload  npm test 2>&1   "Which test fails, and why?"
`fixed coupon never goes below zero` (test/cart.test.js:404): applyCoupon(5, {type:'fixed',
value:10}) returns -5, expected 0.
— qwen3-coder:30b-a3b-q4_K_M · 19,132 input tokens read locally

Why

	Claude does it	Claude delegates it
Find one failure in a 50 KB test log	~19,000 tokens of log in context	a ~130-token answer
Answer from three docs pages	the pages, or a summary of them	a cited paragraph + source list, from the full pages
Write a helper that tests can pin down	output tokens for the code, then re-reading it	writes the test, reads `PASS`
Several independent helpers	sequential edits	parallel delegates, each in its own worktree

Any model, any hardware. No hardcoded models, no GPU assumptions. CPU-only works, just slower.
Parallel by default. Several calls in one turn, or from several subagents, run at once. The concurrency limit is a config value, not a hardcoded lock.
Fits the context automatically. Material is sized to the model's context window per text (number-heavy logs need more tokens than prose). Anything cut is reported.
Zero dependencies. Plain Node ≥ 18. Installing from git needs no npm install.
Agent-configurable. One configure call shows the config, the backend and its models, and changes any setting. It takes effect on the next call, with no restart.

Install

Prerequisites: Node ≥ 18, git, and a running local model server (e.g. ollama pull qwen3-coder:30b).

Claude Code plugin

/plugin marketplace add JoblessJoe/localize
/plugin install localize@localize

Then just ask: "set up localize for my machine". Claude finds your backend, picks a model, and asks which tools it should use on its own (auto_use). By default that's offload and research; delegate is opt-in. Every tool also works whenever you ask for it.

Make Claude use it every time

A session-start reminder nudges Claude toward the tools you chose. For dependable use, add this to your CLAUDE.md; setup offers to do it for you:

## Local LLM (localize)
- Run test suites, builds and other noisy commands through `offload` (`command`), and read logs or files over ~300 lines through it, instead of reading the output yourself.
- Use `research` for web lookups instead of WebSearch/WebFetch.

With this in place, Claude ran a noisy failing test suite through offload every time we tried. That session cost 40 % less than one that read the output itself.

Use it in any MCP client

Claude Desktop, Cursor, Windsurf, and others:

{
  "mcpServers": {
    "localize": {
      "command": "node",
      "args": ["/path/to/localize/src/index.js"],
      "env": { "LLW_MODEL": "qwen3-coder:30b" }
    }
  }
}

Claude Code without the plugin: claude mcp add localize -- node /path/to/localize/src/index.js

How `delegate` works

flowchart LR
    A[Claude writes the test<br/>+ spec + invariants] --> B[delegate]
    B --> C[fresh git worktree<br/>+ your uncommitted changes]
    C --> D[local LLM writes<br/>target file]
    D --> E{run test}
    E -- fail --> F[parse failures<br/>test source stays hidden]
    F --> D
    E -- pass --> G[copy file into checkout<br/>unless it changed meanwhile]
    G --> H[Claude gets a one-line verdict]

Isolated. Each call gets its own git worktree with your uncommitted changes mirrored in, plus symlinked node_modules / .venv, so parallel calls never collide.
Locked scope. Exactly one target file, jailed to the repo, and never one of the test_files you name.
Useful feedback. Retries get the parsed failing assertions (TAP, pytest, jest, vitest, go, cargo), not a stack-trace tail.
Safe apply. If you or another delegate touched the target meanwhile, nothing is overwritten.
Honest failure. After N attempts you get the last failure and the kept worktree. Transport errors are reported as errors, never as a model FAIL.
Self-cleaning. Each delegate prunes llw-* worktrees left behind by a crashed server, and kept ones older than 24 h. Untracked files over 10 MB are not copied into the worktree (the verdict says how many were skipped).

The bundled skill teaches Claude how to write specs that pass: one invariant per test, exact API surface in context, properties instead of examples, and never delegating auth or money code.

Results

On one 24 GB GPU (Tesla P40) with qwen3-coder:30b-a3b-q4_K_M on Ollama:

Task	Read locally	Claude received
`offload`: find the failing test in a 50 KB test log	~19,100 tokens	~130 tokens
`offload`: find one ERROR line in a 90 KB log	~30,400 tokens	the line, quoted
`research`: "Latest Node.js LTS and its end of life?" (3 pages)	7,025 tokens	a cited answer, ~250 tokens

Several calls run in parallel: three in one turn took as long as the slowest one.

Which model?

From a reproducible benchmark (bench/) of 120 delegate runs across six task types:

Model	Good for	Speed per call
`qwen3-coder:30b-a3b`	the best default: parsers, pure functions, Python	15–55 s
`devstral-small-2:24b`	edits to existing files, parsers	2–9 min
`granite4.1:8b`	small, well-specified functions on modest hardware	25–90 s

Retries matter: with failure feedback, qwen3-coder's pass rate rose by half from the first attempt to the third. offload and research work well with any of these. Every run is logged (see Stats), so you can measure your own setup.

Configuration

Agents should use configure. Humans can edit JSON. Layers (later wins):

built-in defaults
user: ~/.config/localize/config.json (respects $XDG_CONFIG_HOME)
project: <git root>/.localize.json
env: LLW_<KEY>, e.g. LLW_BASE_URL, LLW_MODEL. Integers are digits only, booleans true/false/1/0/yes/no, link_dirs a comma list or JSON array, headers a JSON object. An invalid value is ignored and configure lists it under warnings.

A config file with invalid JSON is an error that names the file.

Key	Default	Meaning
`base_url`	`http://localhost:11434`	Backend root. A trailing `/v1` is stripped.
`api`	`auto`	`auto` · `ollama` · `openai`. Auto probes `/api/version`.
`api_key`	`""`	Sent as `Authorization: Bearer`. Shown as `(set)`, never printed. Plugin users can set it under the plugin's settings instead, which keeps it in the OS keychain.
`headers`	`{}`	Extra HTTP headers for a gateway, e.g. `{"X-Api-Key": "..."}`. Values shown as `(set)`.
`model`	`""`	Default model for both tools.
`offload_model`	`""`	Override for `offload` (e.g. long-context).
`delegate_model`	`""`	Override for `delegate` (e.g. a coder).
`research_model`	`""`	Override for `research` (e.g. long-context).
`search_url`	`""`	SearXNG instance for `research` (JSON output enabled).
`research_sources`	`3`	Pages `research` reads per search (max 10).
`page_timeout_ms`	`30000`	Per page download and per search.
`allow_private_urls`	`false`	Let `research` read pages on loopback, link-local or private addresses (the `search_url` itself is always allowed).
`num_ctx`	`32768`	Context window (Ollama).
`max_tokens`	`8192`	Output limit per answer, sent on both APIs (`num_predict` on Ollama). A cut-off answer is reported, not tested.
`keep_alive`	`""`	Ollama only: how long the model stays loaded, e.g. `"30m"` or `-1` (forever). `""` keeps the server default (5 min).
`concurrency`	`4`	Max in-flight LLM requests per server process.
`timeout_ms`	`600000`	Per LLM request, counted from slot acquisition.
`test_timeout_ms`	`600000`	Per test / command run.
`max_attempts`	`3`	Delegate rounds (cap 10).
`link_dirs`	`node_modules, .venv, venv, vendor`	Symlinked into each worktree.
`log_path`	`~/.localize/runs.jsonl`	Run log; `""` disables.
`auto_use`	`offload, research`	Tools Claude uses on its own, via a short session-start reminder. `delegate` is opt-in. Every tool still works when you ask for it; `[]` turns the reminder off.

// configure — no args: effective config, value sources, backend, model list
// configure — change settings (null removes a key):
{ "set": { "model": "qwen3-coder:30b", "num_ctx": 65536 }, "scope": "user" }

With no model set, it uses the backend's only model, or fails with the list. It never guesses, because the guess could be an embedding model.

Backends

Backend	`base_url`	API
Ollama	`http://localhost:11434`	native `/api/chat`. Its OpenAI endpoint ignores `num_ctx` and silently truncates long prompts.
llama.cpp `llama-server`	`http://localhost:8080`	`/v1/chat/completions`
LM Studio	`http://localhost:1234`	`/v1/chat/completions`
vLLM	`http://localhost:8000`	`/v1/chat/completions`
LocalAI, others	your endpoint	`/v1/chat/completions`

Verified end-to-end on Ollama (GPU) and llama.cpp llama-server (CPU only). <think> blocks from reasoning models are stripped. If a backend reads less of the prompt than was sent, the result says so.

Tool reference

Arg
`task`	required. The question.
`files`	Paths relative to the git root; must stay inside it.
`command`	Shell command whose stdout + stderr is the material, e.g. `npm test 2>&1`.
`model`	Override.

Material that doesn't fit next to the answer is cut in the middle (head and tail kept, sized per text), and the cut is reported.

Arg
`question`	required. The research question.
`query`	Search query, if it should differ from the question.
`urls`	Read these pages instead of searching (no `search_url` needed).
`max_sources`	Pages to read when searching (1–10).
`model`	Override.

Page URLs that resolve to loopback, link-local or private addresses are refused, on every redirect hop too, unless allow_private_urls is true. Fetches pages in parallel and walks further down the results when a page fails (404, PDF, JavaScript-only). Each page is reduced to readable text, with <main>/<article> preferred and nav, footer and scripts dropped, then the pages share the num_ctx budget. Searching needs a SearXNG instance: docker run -d -p 8888:8080 searxng/searxng, add json under search.formats in its settings.yml, then set search_url to http://localhost:8888.

Arg
`spec`	required. What the file must do.
`target_file`	required. The one file to create or modify.
`test_command`	required. Run in the worktree by the platform shell (`sh -c`, or `cmd.exe /d /s /c` on Windows); exit 0 = pass. On timeout the whole process tree is killed.
`properties`	Invariants, one per gating test.
`context`	The exact API surface the file may use.
`test_files`	The target may not be one of these.
`max_attempts`, `model`	Overrides.
`show_code`	Also return the code (default `false`, which is where the saving comes from).
`apply`	Copy a passing file into the checkout (default `true`).

No args returns the report. { "set": {...}, "scope": "user" | "project" } validates and writes. Unknown keys are rejected with the list of valid ones.

Stats

Every run appends one line to log_path:

{"ts":"2026-10-04T17:51:28.476Z","tool":"delegate","model":"qwen3-coder:30b-a3b-q4_K_M","api":"ollama","ok":true,"attempts":1,"in_tok":201,"out_tok":195,"ms":57909}

jq -s 'group_by(.tool) | map({tool: .[0].tool, runs: length, ok: (map(select(.ok)) | length), out_tok: (map(.out_tok) | add)})' ~/.localize/runs.jsonl

FAQ

Does Claude ever see the generated code? Only if it asks (show_code: true) or reads the file. Review before merging anything that matters. A passing test is a gate, not a code review.

What should I not delegate? Auth, money, permissions, security, and anything whose spec has no single right answer. The skill tells Claude this too.

Can the local model cheat the test? It never sees the test source. It can write only the one target file, and that file can't be one of the test_files you list.

A long delegate or research call was cut off. The server sends MCP progress notifications every 15 s when the client asks for them (progressToken), but whether that helps depends on the client:

Client	Tool-call limit	Knob
Claude Code	No practical wall-clock limit by default (~28 h); a stdio call is aborted after 30 min with no response and no progress, which the heartbeat prevents	`MCP_TOOL_TIMEOUT` (ms, hard limit, progress does not extend it), `CLAUDE_CODE_MCP_TOOL_IDLE_TIMEOUT` (ms, `0` disables), or `"timeout"` in `.mcp.json`
Claude Desktop	~60 s for local servers; progress does not reset it	none known: run long jobs from Claude Code, or lower `max_attempts` and `research_sources`
Cursor	~60 s; progress reportedly does not reset it	none known

Does it send my code anywhere? Only to the base_url you configure, which is localhost by default. research sends your search query to your own SearXNG and downloads public pages. It never sends your files.

Development

npm test   # 88 tests, fake backend + fake web, no LLM or network needed

src/index.js is the stdio JSON-RPC server. src/worker.js holds the config, backend client, tools and failure extractor.

Releases are automatic: every push to main runs the tests, bumps the patch version everywhere it appears (npm run bump does the same locally; pass minor, major or x.y.z for more), tags it and creates a GitHub Release.

Roadmap

more search backends (Brave, Tavily) alongside SearXNG
multi-file delegates
per-task model routing
an async job API for clients with a 60 s tool limit (Claude Desktop, Cursor)

License

MIT © Johannes Tebbert

localize

Let Claude hand the bulk reading to your local LLM: test logs, big files, web pages. Claude only gets the answer.

Formerly local-llm-worker. npm: localize-mcp

Works with Ollama · llama.cpp · LM Studio · vLLM · LocalAI · any OpenAI-compatible endpoint

offload: your local model runs the noisy command or reads the big file. Claude gets the answer.
research: your local model searches the web, reads the pages in full and returns a cited answer. Claude never sees the pages.
delegate (opt-in): your local model writes one file in an isolated git worktree and retries until a test Claude wrote first passes. Claude gets a one-line verdict, not the code.

> offload  npm test 2>&1   "Which test fails, and why?"
`fixed coupon never goes below zero` (test/cart.test.js:404): applyCoupon(5, {type:'fixed',
value:10}) returns -5, expected 0.
— qwen3-coder:30b-a3b-q4_K_M · 19,132 input tokens read locally

Why

	Claude does it	Claude delegates it
Find one failure in a 50 KB test log	~19,000 tokens of log in context	a ~130-token answer
Answer from three docs pages	the pages, or a summary of them	a cited paragraph + source list, from the full pages
Write a helper that tests can pin down	output tokens for the code, then re-reading it	writes the test, reads `PASS`
Several independent helpers	sequential edits	parallel delegates, each in its own worktree

Any model, any hardware. No hardcoded models, no GPU assumptions. CPU-only works, just slower.
Parallel by default. Several calls in one turn, or from several subagents, run at once. The concurrency limit is a config value, not a hardcoded lock.
Fits the context automatically. Material is sized to the model's context window per text (number-heavy logs need more tokens than prose). Anything cut is reported.
Zero dependencies. Plain Node ≥ 18. Installing from git needs no npm install.
Agent-configurable. One configure call shows the config, the backend and its models, and changes any setting. It takes effect on the next call, with no restart.

Install

Prerequisites: Node ≥ 18, git, and a running local model server (e.g. ollama pull qwen3-coder:30b).

Claude Code plugin

/plugin marketplace add JoblessJoe/localize
/plugin install localize@localize

Make Claude use it every time

A session-start reminder nudges Claude toward the tools you chose. For dependable use, add this to your CLAUDE.md; setup offers to do it for you:

## Local LLM (localize)
- Run test suites, builds and other noisy commands through `offload` (`command`), and read logs or files over ~300 lines through it, instead of reading the output yourself.
- Use `research` for web lookups instead of WebSearch/WebFetch.

With this in place, Claude ran a noisy failing test suite through offload every time we tried. That session cost 40 % less than one that read the output itself.

Use it in any MCP client

Claude Desktop, Cursor, Windsurf, and others:

{
  "mcpServers": {
    "localize": {
      "command": "node",
      "args": ["/path/to/localize/src/index.js"],
      "env": { "LLW_MODEL": "qwen3-coder:30b" }
    }
  }
}

Claude Code without the plugin: claude mcp add localize -- node /path/to/localize/src/index.js

How `delegate` works

flowchart LR
    A[Claude writes the test<br/>+ spec + invariants] --> B[delegate]
    B --> C[fresh git worktree<br/>+ your uncommitted changes]
    C --> D[local LLM writes<br/>target file]
    D --> E{run test}
    E -- fail --> F[parse failures<br/>test source stays hidden]
    F --> D
    E -- pass --> G[copy file into checkout<br/>unless it changed meanwhile]
    G --> H[Claude gets a one-line verdict]

Isolated. Each call gets its own git worktree with your uncommitted changes mirrored in, plus symlinked node_modules / .venv, so parallel calls never collide.
Locked scope. Exactly one target file, jailed to the repo, and never one of the test_files you name.
Useful feedback. Retries get the parsed failing assertions (TAP, pytest, jest, vitest, go, cargo), not a stack-trace tail.
Safe apply. If you or another delegate touched the target meanwhile, nothing is overwritten.
Honest failure. After N attempts you get the last failure and the kept worktree. Transport errors are reported as errors, never as a model FAIL.
Self-cleaning. Each delegate prunes llw-* worktrees left behind by a crashed server, and kept ones older than 24 h. Untracked files over 10 MB are not copied into the worktree (the verdict says how many were skipped).

The bundled skill teaches Claude how to write specs that pass: one invariant per test, exact API surface in context, properties instead of examples, and never delegating auth or money code.

Results

On one 24 GB GPU (Tesla P40) with qwen3-coder:30b-a3b-q4_K_M on Ollama:

Task	Read locally	Claude received
`offload`: find the failing test in a 50 KB test log	~19,100 tokens	~130 tokens
`offload`: find one ERROR line in a 90 KB log	~30,400 tokens	the line, quoted
`research`: "Latest Node.js LTS and its end of life?" (3 pages)	7,025 tokens	a cited answer, ~250 tokens

Several calls run in parallel: three in one turn took as long as the slowest one.

Which model?

From a reproducible benchmark (bench/) of 120 delegate runs across six task types:

Model	Good for	Speed per call
`qwen3-coder:30b-a3b`	the best default: parsers, pure functions, Python	15–55 s
`devstral-small-2:24b`	edits to existing files, parsers	2–9 min
`granite4.1:8b`	small, well-specified functions on modest hardware	25–90 s

Configuration

Agents should use configure. Humans can edit JSON. Layers (later wins):

built-in defaults
user: ~/.config/localize/config.json (respects $XDG_CONFIG_HOME)
project: <git root>/.localize.json
env: LLW_<KEY>, e.g. LLW_BASE_URL, LLW_MODEL. Integers are digits only, booleans true/false/1/0/yes/no, link_dirs a comma list or JSON array, headers a JSON object. An invalid value is ignored and configure lists it under warnings.

A config file with invalid JSON is an error that names the file.

Key	Default	Meaning
`base_url`	`http://localhost:11434`	Backend root. A trailing `/v1` is stripped.
`api`	`auto`	`auto` · `ollama` · `openai`. Auto probes `/api/version`.
`api_key`	`""`	Sent as `Authorization: Bearer`. Shown as `(set)`, never printed. Plugin users can set it under the plugin's settings instead, which keeps it in the OS keychain.
`headers`	`{}`	Extra HTTP headers for a gateway, e.g. `{"X-Api-Key": "..."}`. Values shown as `(set)`.
`model`	`""`	Default model for both tools.
`offload_model`	`""`	Override for `offload` (e.g. long-context).
`delegate_model`	`""`	Override for `delegate` (e.g. a coder).
`research_model`	`""`	Override for `research` (e.g. long-context).
`search_url`	`""`	SearXNG instance for `research` (JSON output enabled).
`research_sources`	`3`	Pages `research` reads per search (max 10).
`page_timeout_ms`	`30000`	Per page download and per search.
`allow_private_urls`	`false`	Let `research` read pages on loopback, link-local or private addresses (the `search_url` itself is always allowed).
`num_ctx`	`32768`	Context window (Ollama).
`max_tokens`	`8192`	Output limit per answer, sent on both APIs (`num_predict` on Ollama). A cut-off answer is reported, not tested.
`keep_alive`	`""`	Ollama only: how long the model stays loaded, e.g. `"30m"` or `-1` (forever). `""` keeps the server default (5 min).
`concurrency`	`4`	Max in-flight LLM requests per server process.
`timeout_ms`	`600000`	Per LLM request, counted from slot acquisition.
`test_timeout_ms`	`600000`	Per test / command run.
`max_attempts`	`3`	Delegate rounds (cap 10).
`link_dirs`	`node_modules, .venv, venv, vendor`	Symlinked into each worktree.
`log_path`	`~/.localize/runs.jsonl`	Run log; `""` disables.
`auto_use`	`offload, research`	Tools Claude uses on its own, via a short session-start reminder. `delegate` is opt-in. Every tool still works when you ask for it; `[]` turns the reminder off.

// configure — no args: effective config, value sources, backend, model list
// configure — change settings (null removes a key):
{ "set": { "model": "qwen3-coder:30b", "num_ctx": 65536 }, "scope": "user" }

With no model set, it uses the backend's only model, or fails with the list. It never guesses, because the guess could be an embedding model.

Backends

Backend	`base_url`	API
Ollama	`http://localhost:11434`	native `/api/chat`. Its OpenAI endpoint ignores `num_ctx` and silently truncates long prompts.
llama.cpp `llama-server`	`http://localhost:8080`	`/v1/chat/completions`
LM Studio	`http://localhost:1234`	`/v1/chat/completions`
vLLM	`http://localhost:8000`	`/v1/chat/completions`
LocalAI, others	your endpoint	`/v1/chat/completions`

Tool reference

Arg
`task`	required. The question.
`files`	Paths relative to the git root; must stay inside it.
`command`	Shell command whose stdout + stderr is the material, e.g. `npm test 2>&1`.
`model`	Override.

Material that doesn't fit next to the answer is cut in the middle (head and tail kept, sized per text), and the cut is reported.

Arg
`question`	required. The research question.
`query`	Search query, if it should differ from the question.
`urls`	Read these pages instead of searching (no `search_url` needed).
`max_sources`	Pages to read when searching (1–10).
`model`	Override.

Arg
`spec`	required. What the file must do.
`target_file`	required. The one file to create or modify.
`test_command`	required. Run in the worktree by the platform shell (`sh -c`, or `cmd.exe /d /s /c` on Windows); exit 0 = pass. On timeout the whole process tree is killed.
`properties`	Invariants, one per gating test.
`context`	The exact API surface the file may use.
`test_files`	The target may not be one of these.
`max_attempts`, `model`	Overrides.
`show_code`	Also return the code (default `false`, which is where the saving comes from).
`apply`	Copy a passing file into the checkout (default `true`).

No args returns the report. { "set": {...}, "scope": "user" | "project" } validates and writes. Unknown keys are rejected with the list of valid ones.

Stats

Every run appends one line to log_path:

{"ts":"2026-10-04T17:51:28.476Z","tool":"delegate","model":"qwen3-coder:30b-a3b-q4_K_M","api":"ollama","ok":true,"attempts":1,"in_tok":201,"out_tok":195,"ms":57909}

jq -s 'group_by(.tool) | map({tool: .[0].tool, runs: length, ok: (map(select(.ok)) | length), out_tok: (map(.out_tok) | add)})' ~/.localize/runs.jsonl

FAQ

Does Claude ever see the generated code? Only if it asks (show_code: true) or reads the file. Review before merging anything that matters. A passing test is a gate, not a code review.

What should I not delegate? Auth, money, permissions, security, and anything whose spec has no single right answer. The skill tells Claude this too.

Can the local model cheat the test? It never sees the test source. It can write only the one target file, and that file can't be one of the test_files you list.

Client	Tool-call limit	Knob
Claude Code	No practical wall-clock limit by default (~28 h); a stdio call is aborted after 30 min with no response and no progress, which the heartbeat prevents	`MCP_TOOL_TIMEOUT` (ms, hard limit, progress does not extend it), `CLAUDE_CODE_MCP_TOOL_IDLE_TIMEOUT` (ms, `0` disables), or `"timeout"` in `.mcp.json`
Claude Desktop	~60 s for local servers; progress does not reset it	none known: run long jobs from Claude Code, or lower `max_attempts` and `research_sources`
Cursor	~60 s; progress reportedly does not reset it	none known

Development

npm test   # 88 tests, fake backend + fake web, no LLM or network needed

src/index.js is the stdio JSON-RPC server. src/worker.js holds the config, backend client, tools and failure extractor.

Roadmap

more search backends (Brave, Tavily) alongside SearXNG
multi-file delegates
per-task model routing
an async job API for clients with a 60 s tool limit (Claude Desktop, Cursor)

添加此服务器

用我们的密钥与日志托管一份副本。

Related MCP

Related skills

localize

Why

Install

Claude Code plugin

Make Claude use it every time

Use it in any MCP client

How delegate works

Results

Which model?

Configuration

Backends

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How `delegate` works