diff --git a/docs/core_docs/docs/integrations/tools/duckduckgo_search.ipynb b/docs/core_docs/docs/integrations/tools/duckduckgo_search.ipynb
index e0053c4cd9b1..1ff5b72ffdea 100644
--- a/docs/core_docs/docs/integrations/tools/duckduckgo_search.ipynb
+++ b/docs/core_docs/docs/integrations/tools/duckduckgo_search.ipynb
@@ -30,7 +30,7 @@
"### Integration details\n",
"\n",
"| Class | Package | [PY support](https://python.langchain.com/docs/integrations/tools/ddg/) | Package latest |\n",
- "| :--- | :--- | :---: | :---: | :---: |\n",
+ "| :--- | :--- | :---: | :---: |\n",
"| [DuckDuckGoSearch](https://api.js.langchain.com/classes/langchain_community_tools_duckduckgo_search.DuckDuckGoSearch.html) | [`@langchain/community`](https://www.npmjs.com/package/@langchain/community) | ✅ |  |\n",
"\n",
"## Setup\n",
diff --git a/docs/core_docs/docs/integrations/tools/tavily_search.ipynb b/docs/core_docs/docs/integrations/tools/tavily_search.ipynb
index ec6b12c0a0dd..ff63e76ba54c 100644
--- a/docs/core_docs/docs/integrations/tools/tavily_search.ipynb
+++ b/docs/core_docs/docs/integrations/tools/tavily_search.ipynb
@@ -30,7 +30,7 @@
"### Integration details\n",
"\n",
"| Class | Package | [PY support](https://python.langchain.com/v0.2/docs/integrations/tools/tavily_search/) | Package latest |\n",
- "| :--- | :--- | :---: | :---: | :---: |\n",
+ "| :--- | :--- | :---: | :---: |\n",
"| [TavilySearchResults](https://api.js.langchain.com/classes/langchain_community_tools_tavily_search.TavilySearchResults.html) | [`@langchain/community`](https://www.npmjs.com/package/@langchain/community) | ✅ |  |\n",
"\n",
"## Setup\n",
diff --git a/libs/langchain-community/src/tools/duckduckgo_search.ts b/libs/langchain-community/src/tools/duckduckgo_search.ts
index 70c440253a1c..7bfd04307a89 100644
--- a/libs/langchain-community/src/tools/duckduckgo_search.ts
+++ b/libs/langchain-community/src/tools/duckduckgo_search.ts
@@ -24,8 +24,69 @@ export interface DuckDuckGoSearchParameters extends ToolParams {
const DEFAULT_MAX_RESULTS = 10;
/**
- * Class for interacting with the DuckDuckGo search engine
- * It extends the base Tool class to perform retrieval.
+ * DuckDuckGo tool integration.
+ *
+ * Setup:
+ * Install `@langchain/community` and `duck-duck-scrape`.
+ *
+ * ```bash
+ * npm install @langchain/community duck-duck-scrape
+ * ```
+ *
+ * ## [Constructor args](https://api.js.langchain.com/classes/_langchain_community.tools_duckduckgo_search.DuckDuckGoSearch.html#constructor)
+ *
+ *
+ * Instantiate
+ *
+ * ```typescript
+ * import { DuckDuckGoSearch } from "@langchain/community/tools/duckduckgo_search";
+ *
+ * const tool = new DuckDuckGoSearch({ maxResults: 1 });
+ * ```
+ *
+ *
+ *
+ *
+ *
+ *
+ * Invocation
+ *
+ * ```typescript
+ * await tool.invoke("what is the current weather in sf?");
+ *
+ * // output: [{"title":"San Francisco, CA Current Weather | AccuWeather","link":"https://www.accuweather.com/en/us/san-francisco/94103/current-weather/347629","snippet":"Current weather in San Francisco, CA. Check current conditions in San Francisco, CA with radar, hourly, and more."}]
+ * ```
+ *
+ *
+ *
+ *
+ *
+ *
+ * Invocation with tool call
+ *
+ * ```typescript
+ * // This is usually generated by a model, but we'll create a tool call directly for demo purposes.
+ * const modelGeneratedToolCall = {
+ * args: {
+ * input: "what is the current weather in sf?",
+ * },
+ * id: "tool_call_id",
+ * name: tool.name,
+ * type: "tool_call",
+ * };
+ * await tool.invoke(modelGeneratedToolCall);
+ * ```
+ *
+ * ```text
+ * ToolMessage {
+ * "content": "[{\"title\":\"San Francisco, CA Weather Conditions | Weather Underground\",\"link\":\"https://www.wunderground.com/weather/us/ca/san-francisco\",\"snippet\":\"San Francisco Weather Forecasts. Weather Underground provides local & long-range weather forecasts, weatherreports, maps & tropical weather conditions for the San Francisco area.\"}]",
+ * "name": "duckduckgo-search",
+ * "additional_kwargs": {},
+ * "response_metadata": {},
+ * "tool_call_id": "tool_call_id"
+ * }
+ * ```
+ *
*/
export class DuckDuckGoSearch extends Tool {
private searchOptions?: SearchOptions;
diff --git a/libs/langchain-community/src/tools/tavily_search.ts b/libs/langchain-community/src/tools/tavily_search.ts
index a68f97d5e588..b83a042f7888 100644
--- a/libs/langchain-community/src/tools/tavily_search.ts
+++ b/libs/langchain-community/src/tools/tavily_search.ts
@@ -12,7 +12,70 @@ export type TavilySearchAPIRetrieverFields = ToolParams & {
};
/**
- * Tool for the Tavily search API.
+ * Tavily search API tool integration.
+ *
+ * Setup:
+ * Install `@langchain/community`. You'll also need an API key set as `TAVILY_API_KEY`.
+ *
+ * ```bash
+ * npm install @langchain/community
+ * ```
+ *
+ * ## [Constructor args](https://api.js.langchain.com/classes/_langchain_community.tools_tavily_search.TavilySearchResults.html#constructor)
+ *
+ *
+ * Instantiate
+ *
+ * ```typescript
+ * import { TavilySearchResults } from "@langchain/community/tools/tavily_search";
+ *
+ * const tool = new TavilySearchResults({
+ * maxResults: 2,
+ * // ...
+ * });
+ * ```
+ *
+ *
+ *
+ *
+ *
+ *
+ * Invocation
+ *
+ * ```typescript
+ * await tool.invoke("what is the current weather in sf?");
+ * ```
+ *
+ *
+ *
+ *
+ *
+ *
+ * Invocation with tool call
+ *
+ * ```typescript
+ * // This is usually generated by a model, but we'll create a tool call directly for demo purposes.
+ * const modelGeneratedToolCall = {
+ * args: {
+ * input: "what is the current weather in sf?",
+ * },
+ * id: "tool_call_id",
+ * name: tool.name,
+ * type: "tool_call",
+ * };
+ * await tool.invoke(modelGeneratedToolCall);
+ * ```
+ *
+ * ```text
+ * ToolMessage {
+ * "content": "...",
+ * "name": "tavily_search_results_json",
+ * "additional_kwargs": {},
+ * "response_metadata": {},
+ * "tool_call_id": "tool_call_id"
+ * }
+ * ```
+ *
*/
export class TavilySearchResults extends Tool {
static lc_name(): string {
diff --git a/libs/langchain-community/src/tools/wikipedia_query_run.ts b/libs/langchain-community/src/tools/wikipedia_query_run.ts
index d0166e5c58d0..2c2f6b066761 100644
--- a/libs/langchain-community/src/tools/wikipedia_query_run.ts
+++ b/libs/langchain-community/src/tools/wikipedia_query_run.ts
@@ -53,16 +53,70 @@ interface PageResult {
}
/**
- * Class for interacting with and fetching data from the Wikipedia API. It
- * extends the Tool class.
- * @example
+ * Wikipedia query tool integration.
+ *
+ * Setup:
+ * Install `@langchain/community`. You'll also need an API key.
+ *
+ * ```bash
+ * npm install @langchain/community
+ * ```
+ *
+ * ## [Constructor args](https://api.js.langchain.com/classes/_langchain_community.tools_wikipedia_query_run.WikipediaQueryRun.html#constructor)
+ *
+ *
+ * Instantiate
+ *
* ```typescript
- * const wikipediaQuery = new WikipediaQueryRun({
+ * import { WikipediaQueryRun } from "@langchain/community/tools/wikipedia_query_run";
+ *
+ * const tool = new WikipediaQueryRun({
* topKResults: 3,
* maxDocContentLength: 4000,
* });
- * const result = await wikipediaQuery.call("Langchain");
* ```
+ *
+ *
+ *
+ *
+ *
+ *
+ * Invocation
+ *
+ * ```typescript
+ * await tool.invoke("what is the current weather in sf?");
+ * ```
+ *
+ *
+ *
+ *
+ *
+ *
+ * Invocation with tool call
+ *
+ * ```typescript
+ * // This is usually generated by a model, but we'll create a tool call directly for demo purposes.
+ * const modelGeneratedToolCall = {
+ * args: {
+ * input: "what is the current weather in sf?",
+ * },
+ * id: "tool_call_id",
+ * name: tool.name,
+ * type: "tool_call",
+ * };
+ * await tool.invoke(modelGeneratedToolCall);
+ * ```
+ *
+ * ```text
+ * ToolMessage {
+ * "content": "...",
+ * "name": "wikipedia-api",
+ * "additional_kwargs": {},
+ * "response_metadata": {},
+ * "tool_call_id": "tool_call_id"
+ * }
+ * ```
+ *
*/
export class WikipediaQueryRun extends Tool {
static lc_name() {
diff --git a/libs/langchain-exa/src/tools.ts b/libs/langchain-exa/src/tools.ts
index 6bf52ad64ede..285ea7f66d0b 100644
--- a/libs/langchain-exa/src/tools.ts
+++ b/libs/langchain-exa/src/tools.ts
@@ -13,7 +13,75 @@ export type ExaSearchRetrieverFields<
};
/**
- * Tool for the Exa search API.
+ * Exa search tool integration.
+ *
+ * Setup:
+ * Install `@langchain/exa` and `exa-js`. You'll also need an API key.
+ *
+ * ```bash
+ * npm install @langchain/exa exa-js
+ * ```
+ *
+ * ## [Constructor args](https://api.js.langchain.com/classes/_langchain_exa.ExaSearchResults.html#constructor)
+ *
+ *
+ * Instantiate
+ *
+ * ```typescript
+ * import { ExaSearchResults } from "@langchain/exa";
+ * import Exa from "exa-js";
+ *
+ * const client = new Exa(process.env.EXASEARCH_API_KEY);
+ *
+ * const tool = new ExaSearchResults({
+ * client,
+ * searchArgs: {
+ * numResults: 2,
+ * },
+ * });
+ * ```
+ *
+ *
+ *
+ *
+ *
+ *
+ * Invocation
+ *
+ * ```typescript
+ * await tool.invoke("what is the current weather in sf?");
+ * ```
+ *
+ *
+ *
+ *
+ *
+ *
+ * Invocation with tool call
+ *
+ * ```typescript
+ * // This is usually generated by a model, but we'll create a tool call directly for demo purposes.
+ * const modelGeneratedToolCall = {
+ * args: {
+ * input: "what is the current weather in sf?",
+ * },
+ * id: "tool_call_id",
+ * name: tool.name,
+ * type: "tool_call",
+ * };
+ * await tool.invoke(modelGeneratedToolCall);
+ * ```
+ *
+ * ```text
+ * ToolMessage {
+ * "content": "...",
+ * "name": "exa_search_results_json",
+ * "additional_kwargs": {},
+ * "response_metadata": {},
+ * "tool_call_id": "tool_call_id"
+ * }
+ * ```
+ *
*/
export class ExaSearchResults<
T extends ContentsOptions = { text: true }