Fetches search results from DuckDuckGo, returning source-bearing entries with titles, URLs, and snippets.
Fetches search results from Wikipedia, returning source-bearing entries with titles, URLs, and snippets.
Click on "Install 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., "@groundlink-mcp Search for the latest Mars rover discoveries and cite your sources"
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.
Use Groundlink's cited web-search results from any Model Context Protocol (MCP)
host. The server exposes one focused tool, groundlink_search, which sends a
query to Groundlink and returns source-bearing results (title, url,
snippet, and source) for the model to use when it needs evidence rather
than a guess.
Groundlink currently combines Wikipedia and DuckDuckGo results. Each Groundlink API key includes 100 free test queries; after that, prepaid usage is $0.001 per query (one credit per query).
Package:
groundlink-mcp@0.1.1is published on npm. Source: https://github.com/ohhavefun/groundlink
Live API documentation: https://9ea69cec60fa01f65bbb647a092bcbb4.ctonew.app/docs
Pricing and credits: https://9ea69cec60fa01f65bbb647a092bcbb4.ctonew.app/pricing
This repository is the home of Groundlink's official developer integrations. There are two, both maintained here:
Integration | What it is | Install |
groundlink-mcp (MCP server) | Exposes Groundlink to any MCP host (Claude, Cursor, …) |
|
langchain-groundlink (Python/LangChain) | A |
|
Both use the same free trial keys (100 queries) from the Groundlink onboarding, then prepaid usage at $0.001/query.
Related MCP server: google-search-mcp
Use Groundlink's cited search from any Python agent or BaseTool pipeline.
The tool returns source-bearing results (title, url, snippet, source)
so your agent answers from evidence and can cite it.
Install from GitHub (the package is intentionally not on PyPI):
pip install "langchain-groundlink @ git+https://github.com/ohhavefun/groundlink#subdirectory=langchain-groundlink"Quick usage (10 seconds): get a free key from the docs, then:
from langchain_groundlink import GroundlinkSearchTool
tool = GroundlinkSearchTool(api_key="glk_your_trial_key") # or export GROUNDLINK_API_KEY
result = tool.invoke({"query": "Who was Ada Lovelace?"})
print(result)Full usage, configuration, and error handling are in
langchain-groundlink/README.md.
Use Groundlink's cited web-search results from any Model Context Protocol (MCP) host below.
Field | Value |
Tool name |
|
Input |
|
| 1–10; default 5 (or |
Output | JSON: |
Use it for factual questions where the host should return URLs/sources with its answer. The MCP server is deliberately thin: it forwards the query to the Groundlink HTTPS API and returns the API response over MCP stdio.
You need a Groundlink API key (glk_...). Ask the Groundlink operator for one
or obtain one through the Groundlink onboarding flow.
npm install -g groundlink-mcp
export GROUNDLINK_API_KEY=glk_your_key_here
groundlink-mcpAlternatively, an MCP host can execute the package without a global install:
npx -y groundlink-mcpThe process uses stdio; it does not open an HTTP port or print normal output to stdout. MCP hosts should launch it rather than running it interactively.
Variable | Required | Default | Meaning |
| Yes | — | API key used for every Groundlink request. |
| No | Groundlink live URL | Override only for a compatible deployment. |
| No |
| Default results per tool call; capped at |
Add this entry to Claude Desktop's MCP configuration file and restart Claude Desktop. Keep the API key private: do not commit it to a repository or share the configuration file.
{
"mcpServers": {
"groundlink": {
"command": "npx",
"args": ["-y", "groundlink-mcp"],
"env": {
"GROUNDLINK_API_KEY": "glk_your_key_here"
}
}
}
}If the package is installed globally, use "command": "groundlink-mcp" and
omit args. For hosts that need an absolute executable path, point command
at the installed groundlink-mcp binary.
Missing GROUNDLINK_API_KEY: the server exits at startup with a clear setup
error.
API key rejected (401): returned to the MCP host as a tool error; correct the key rather than retrying.
No remaining free or prepaid credits (402): returned as a tool error; fund the key before another search.
Search-source outage (502): returned as a tool error; a short retry may help.
Groundlink — cited web search for MCP. Give Claude, Cursor, and other MCP
hosts one groundlink_search tool that returns source-bearing results instead
of unsupported factual guesses. Every key starts with 100 free tests, then
usage is $0.001 per query through prepaid credits.
bun install
bun run typecheck
bun run build # emits executable dist/index.js
bun run test # MCP stdio smoke test
npm run pack:check # inspect the npm tarball without publishingThe npm package intentionally ships only the compiled dist/ runtime, this
README, and npm's required package metadata. Source and smoke tests stay out of
the tarball.
Search Groundlink's verified index (Wikipedia + DuckDuckGo) for a query and return cited results (title, url, snippet, source) so the model can answer with sources. Each call costs a fraction of a cent against the API key's free/credit balance. Prefer this over guessing when a factual claim needs verification or a source.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The search query to ground. Use a concise, factual question or phrase. | |
| max_results | No | How many cited results to return (1–10). Defaults to 5. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the full burden. It discloses meaningful behavioral traits: searches a verified index rather than arbitrary web content, returns structured cited results, and mentions cost ('a fraction of a cent against the API key's free/credit balance'). It does not mention rate limits or explicit read-only semantics, but the search action clearly implies no mutation.
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 compact at three sentences, front-loads the primary purpose and output, adds a relevant cost note, and closes with usage guidance. Every sentence adds distinct value with no redundancy or filler.
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?
The description is reasonably complete for a search tool with no output schema: it lists the returned fields, explains the verified-index scope, and provides cost awareness. It does not cover failure modes or authorization requirements, but for a read-only search operation these are not critical gaps and the essential calling context is present.
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 the baseline is 3. The description does not add parameter-specific semantics beyond what the schema already provides; the 'query' guidance and max_results behavior are already fully documented in the input schema. No additional insight is given about parameter formats or edge cases.
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?
The description opens with a specific verb ('Search'), names the exact resource ('Groundlink's verified index (Wikipedia + DuckDuckGo)'), and enumerates the returned fields ('title, url, snippet, source'). This unambiguously explains what the tool does and what output it produces, easily distinguishing it from any guessing or other knowledge retrieval behavior.
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?
The description gives explicit when-to-use guidance: 'Prefer this over guessing when a factual claim needs verification or a source.' It also implicitly states when not to use it (do not guess) and is the only tool of its kind, so no alternative routing is needed. This is direct and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.1groundlink_searchWith only one tool, there is no possibility of overlap or confusion between tools. The single search tool has a clearly defined purpose.
The tool name follows a clear and logical pattern: groundlink_search. Although there is only one tool, the naming is consistent with common conventions and leaves no ambiguity.
A single tool feels thin for a server, but it is focused on one specific search function that could be reasonably self-contained. It does not appear excessive, yet the surface is minimal.
For a search-focused server, the tool fully covers the core capability of searching and returning cited results. There are no obvious missing operations for its apparent purpose.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Live AI-native web search with citations. One tool for every MCP client. Flat per-request pricing.
Web search, scraping, RAG answers with citations, and translation as MCP tools.
Provides AI assistants with access to Seltz's powerful Web Search capabilities.
Collaborative, cache-first web search for agents — cited answers from a shared live-web pool.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/ohhavefun/groundlink'
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