ddg-search
Provides web search through DuckDuckGo with multiple backends, automatic failover, and compact result formatting.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@ddg-searchsearch for latest Python async web scraping tutorials"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
ddg-search
A DuckDuckGo search MCP server that refuses to have a single point of failure. One process, many backends, automatic failover, honest error messages.
The idea
Web search is load-bearing infrastructure for coding agents, and it fails in boring ways: rate limits, bot detection, your VPS provider having a moment. Most servers give you one HTTP client and hope. This one routes each query across several backends — a local searcher on this machine plus any number of remote duckduckgo-mcp-server instances you happen to run — and keeps trying until something answers or the budget runs out.
Backends that fail get put in timeout. Backends that behave get more traffic. You get the results, one compact block, with a one-line note of who served it.
Related MCP server: DuckDuckGo Search MCP Server
Install
Requires Python 3.10+ and uv.
git clone <this repo> ~/.local/share/mcp/ddg-search # or anywhere you like
cd ~/.local/share/mcp/ddg-search
uv syncThat is the whole ceremony. uv sync creates .venv, locks dependencies, and
installs the package editable, so edits to src/ apply on restart.
Wire it into your agent
Any MCP client that speaks stdio works. For an mcp.json-style config:
{
"mcpServers": {
"ddg-search": {
"type": "stdio",
"command": "/path/to/ddg-search/.venv/bin/python",
"args": ["-m", "ddg_search.server"],
"env": {
"DDG_SAFE_SEARCH": "OFF",
"DDG_SEARCH_BACKEND": "auto"
},
"timeout": 60000
}
}
}DDG_SAFE_SEARCH is content filtering only — it does nothing against bot
detection, and it is off by default because agents doing research want recall,
not a chaperone.
Tools
search
Argument | Type | Default | Notes |
| string | required | Exact nouns beat vague one-word vibes |
| int | 10 | Upstream caps around 10–11 regardless |
| string |
| DuckDuckGo region code |
|
|
| Manual skips health sorting |
| string |
| One backend name/alias/IP (manual mode) |
| list |
| Ordered fallback chain (manual mode) |
Results come back compact on purpose:
via relay-b
3 results:
1. Some Page Title
https://example.com/page
The snippet text, labels stripped, no blank lines eating your tokens.
2. ...Every response states which backend served it. Failed attempts are listed
under Attempts: with a tag telling you where it broke:
Tag | Meaning |
| DuckDuckGo returned zero matches — genuine no-results or bot-empty, indistinguishable from here |
| This machine's client failed. Do not blame the remote hosts |
| A remote answered badly |
| The 25s budget ran out while waiting |
When things break, you get a log path
The router distinguishes "the internet is being the internet" from "this tool
is actually broken". Timeouts and empty result sets just get their [tag].
But when an attempt fails in a way that means our side broke — local
transport errors, remote backends answering badly — the response ends with:
log: /path/to/ddg-search/logs/20260822T090206-remote-tool-error.jsonThat file holds everything needed to replay and diagnose: the exact query and
arguments, every attempt with its failure detail, and a snapshot of per-backend
state at the time. Point DDG_SEARCH_LOGS_DIR somewhere else if you want;
logs are never written for timeouts or empty results.
status
Backend table: online flag, observed attempts this minute, last status,
cooldown expiry. Pass probe: true to actually ping remote backends instead
of trusting cached state.
Configuration
Environment variables, all optional:
Variable | Default | Purpose |
|
|
|
|
| Local transport: |
|
| Total budget across all backends per query |
|
| Timeout penalty per backend |
|
| Error penalty per backend |
|
| Per-backend probe wait for |
|
| Router state directory |
Backends live in src/ddg_search/config.py. The
default fleet is local (this machine) plus two remote relays; edit the tuple
to match your own infrastructure.
Behavior worth knowing
Failover prefers healthy backends with the fewest recent attempts, so traffic spreads instead of hammering one poor box.
Cooldowns are per-backend and time-boxed: a timeout sits a backend out for 90s, a soft failure for 30s. One success clears the slate instantly.
State survives restarts in
state/router-state.json. Delete it if you want amnesia; the server recreates it on next boot.
One quirk deserves its own paragraph. DuckDuckGo serves empty pages to clients it does not trust, so "no results" can mean either genuinely no matches or quiet bot-flagging — the router cannot tell those apart, and it does not pretend to. It treats empty as failure and tries the next backend; if every backend comes back empty you get a banner saying exactly how ambiguous that is.
Last thing: the 30 requests/minute ceiling is enforced by each duckduckgo-mcp-server instance, not here. The router spreads load across backends, but it will not lie about capacity the fleet does not have.
Running your own relays
Any machine that can run the stock server works as a backend:
pip install 'duckduckgo-mcp-server[browser]'
python -m duckduckgo_mcp_server.main --transport streamable-http --host 0.0.0.0 --port 18082Point a BackendConfig(url="http://that-host/ddg-mcp") at it. The
realip/ directory contains a launcher used by a systemd unit to
run one such exit behind mullvad-exclude on residential IP — useful if your
datacenter egress gets worse captcha treatment than your home connection.
Development
uv sync # install everything including dev tools
uv run pytest # 26 tests, no network needed except one optional live check
uv run ruff check src tests # lint
uv run ruff format src tests # format
uv run pyrefly check # static typesA quick manual smoke test through the full router:
uv run python -c "import asyncio; from ddg_search.router import SearchRouter; \
print(asyncio.run(SearchRouter().search('crawl4ai', 3, '', 'auto', None, None, None)))"See also
fast-webfetch-mcp — the other half: opens the URLs these searches find, through a local Crawl4AI browser
Model Context Protocol — what "MCP" means
duckduckgo-mcp-server — the search library doing the actual scraping underneath
License
MIT.
Available Tools
2 toolssearchA
Search DuckDuckGo via ddg-search (local + remote backends). Discovery only — titles/URLs/snippets, not page proof. Then open promising URLs with fast-webfetch. Routing: auto = healthy low-use backends; manual = target/targets. On failure, read Attempts tags: [empty] = DDG no matches; [local]/[local-transport] = this machine (do not assume remote VPS missing packages); [remote-rpc]/[remote-tool-error] = remote answered badly; [timeout] = real wait timeout.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | DuckDuckGo query. Prefer exact nouns, error strings, version, site/domain, or API names. Avoid vague one-word searches. | |
| region | No | DuckDuckGo region code. Empty = default/global. | |
| target | No | Manual mode: single backend name, alias, or IP. Empty = all backends. | |
| targets | No | Manual mode: ordered backend names/aliases/IPs. Combined with target; duplicates ignored. | |
| route_mode | No | 'auto' for normal failover; 'manual' only when targeting specific backend(s). | auto |
| max_results | No | Max results. Use 5-10 normally; raise when the first page looks noisy. |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the behavioral burden. It discloses the local+remote backend split, that it's read-only discovery, and decodes error tags to distinguish local vs remote failures. It does not mention rate limits, caching, or concurrency behavior, but for a search tool it covers the most critical operational behaviors.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single dense paragraph, but every sentence adds value: purpose, follow-up action, routing modes, and failure decoding. It is slightly long but not verbose; front-loads the core purpose before details. Could be improved with bullet points but is acceptable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 6 parameters (1 required), an output schema exists (not shown), and helper sibling 'status', the description covers all necessary operational context: what it returns (titles/URLs/snippets), when to use each route mode, how to interpret failures, and the follow-up tool (fast-webfetch). Nothing essential is missing—an agent could call this tool correctly without further clarification.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% so baseline is 3. The description does not add meaning beyond the schema; it repeats the routing guidance already in route_mode and the query tips already in the query parameter. The extra notes about backend combinations and error tags are behavioral rather than parameter-specific, so no increment.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource: 'Search DuckDuckGo via ddg-search'. It clearly delimits scope with 'Discovery only — titles/URLs/snippets, not page proof', distinguishing it from follow-up fetch tools and giving an agent an unambiguous sense of what it returns and what it does not.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit routing rules: 'auto = healthy low-use backends; manual = target/targets' and tells when to use manual mode. It also instructs on failure interpretation: 'read Attempts tags' with specific meanings for [empty], [local]/[local-transport], [remote-rpc]/[remote-tool-error], and [timeout]. This is actionable when-to-use and how-to-handle guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
statusA
Show ddg-search backend router status (health, cooldowns, last errors). Optional probe pings backends. Use after search failures; if last_error is local/transport, fix the workstation ddg-search process before remote packages.
| Name | Required | Description | Default |
|---|---|---|---|
| probe | No | If true, probe backends before reporting. | |
| target | No | Optional single backend name/alias/IP. | |
| targets | No | Optional ordered backend names/aliases/IPs. |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses that probe pings backends, but doesn't detail what the probe does when failures occur or the exact output structure. It does mention cooldowns and last errors as return content, which is useful. There's no contradiction with annotations since none exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences: purpose, key optional behavior, and usage context. Front-loads the main purpose and then adds actionable guidance. No wasted words—every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given an output schema exists (not described in detail but signaled), the return value structure need not be explained. Tool complexity is moderate (3 optional params, one boolean). The description explains when to use it and what to check for. It could mention what happens if probe=true and backends fail, but since it focuses on diagnostics, this is adequate. Sibling 'search' is the only other tool, and status is clearly distinct.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, with 'probe', 'target', and 'targets' all described in the schema. The description mentions 'Optional probe pings backends' which aligns with the probe field but doesn't add much beyond the schema. It also implies target/targets are optional, consistent with schema defaults. Baseline 3 applies since schema already documents parameters fully.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clear verb ('Show') and resource ('ddg-search backend router status'). Distinguishes itself from sibling 'search' by explicitly targeting the router health, cooldowns, and errors—a diagnostic role that search does not play.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
States when to use it ('after search failures') and gives a conditional directive ('if last_error is local/transport, fix the workstation...'). Doesn't explicitly rule out other scenarios, but provides useful decision context. Sibling 'search' is clearly the alternative primary action tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
2 tool updates
v0.2.0- First observed
search - First observed
status
TDQS
Scored across 2 tools
Search and status have completely distinct purposes: one performs queries, the other reports backend health. There is no overlap or ambiguity between them.
Both names are short, lowercase, and clear. Search is a verb while status is a noun, which is a minor stylistic deviation but not confusing.
With only two tools, the set feels minimal. However, the server's scope is narrow and the two tools cover its core responsibilities, so the count is reasonable if sparse.
The server covers search and backend status monitoring, and explicitly delegates page fetching to another tool. Minor gaps exist (e.g., no configuration or backend management), but the stated surface is fully supported.
Maintenance
Related MCP Connectors
Search the agentic web. 4,100+ sites, 11 tools incl. check_url + verify_mcp for probe-before-use.
Serper MCP — wraps the Serper Google Search API (serper.dev)
Scrape, crawl and search the web for AI agents via MCP.
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