Keenable MCP
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., "@Keenable MCPsearch for latest tech news"
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.
Keenable MCP
MCP server for live web search and clean-markdown page fetch over the Keenable web index.
Hosted endpoint:
https://api.keenable.ai/mcp(Streamable HTTP)Tools:
search_web_pages,fetch_page_contentKeyless by default (1,000 requests/hour). An optional API key lifts the cap.
The server is hosted, so most clients just point at the URL. For clients (or
config paths) that only accept a stdio command there is a thin npm wrapper
(@keenable/mcp) that bridges to the same endpoint.
Tools
Tool | Description |
| Live web search. Args: |
| Fetch a URL and return clean markdown. Args: |
Related MCP server: exa-search
Use it
Remote (recommended)
Clients with remote MCP support — Cursor, Cline, VS Code, and Claude Desktop's Custom Connectors UI — connect to the URL directly:
{
"mcpServers": {
"keenable": {
"type": "streamable-http",
"url": "https://api.keenable.ai/mcp"
}
}
}Stdio (e.g. Claude Desktop's claude_desktop_config.json)
For clients or config paths that only accept a stdio command, run the wrapper
with npx:
{
"mcpServers": {
"keenable": {
"command": "npx",
"args": ["-y", "@keenable/mcp"]
}
}
}That is all you need for keyless use.
API key (optional)
Keenable is keyless by default. To lift the hourly rate limit, pass a key.
Remote:
{
"mcpServers": {
"keenable": {
"type": "streamable-http",
"url": "https://api.keenable.ai/mcp",
"headers": { "X-API-Key": "YOUR_KEY" }
}
}
}Stdio:
{
"mcpServers": {
"keenable": {
"command": "npx",
"args": ["-y", "@keenable/mcp"],
"env": { "KEENABLE_API_KEY": "YOUR_KEY" }
}
}
}How the wrapper works
@keenable/mcp is a small stdio bridge: it connects to the hosted server over
Streamable HTTP and forwards every tool the server exposes. There is no
search logic in this package; the index and tools live behind
https://api.keenable.ai/mcp. Environment variables:
KEENABLE_API_KEY— optional API key, sent asX-API-Key.KEENABLE_MCP_URL— override the endpoint (defaults tohttps://api.keenable.ai/mcp).
Links
Site: https://keenable.ai
Registry:
ai.keenable/web-search(Official MCP Registry)
License
MIT
Available Tools
2 toolsfetch_page_contentARead-onlyIdempotentInspect
Fetch and extract content from a web page. Returns the page content in markdown format.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The URL to fetch. Example: "https://example.com". | |
| live | No | Fetch live content. Defaults to false. | |
| max_chars | No | Maximum number of characters of content to return. Longer content is truncated. Defaults to 50000 when omitted. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds that the output is markdown format, which is useful but does not disclose other behaviors like handling of JavaScript, rate limits, or error states.
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 concise with two sentences, front-loading the action and output. Every word adds value with no redundancy.
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?
For a tool with three parameters and no output schema, the description covers the core function and return format. It lacks detail on error handling or content extraction specifics, but given the simplicity, it is largely complete.
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?
The input schema has 100% coverage, so the description does not need to add parameter details. It provides no additional meaning beyond the schema, maintaining the baseline score.
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 clearly states the tool's action: 'Fetch and extract content from a web page.' It includes the output format ('Returns the page content in markdown format.'), which differentiates it from the sibling tool 'search_web_pages' that presumably searches for pages rather than fetching a specific URL.
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 implies that the tool is used when a specific URL is known, in contrast to 'search_web_pages,' but it does not explicitly state when to use or not use this tool, nor does it provide alternative suggestions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_web_pagesARead-onlyIdempotentInspect
Live web search over the Keenable web index. Describe the ideal page, not keywords. Use date/site filters to narrow results.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | Search mode: 'pro' (default) for enhanced results. | |
| site | No | Restrict results to a specific site (e.g. "techcrunch.com"). | |
| query | Yes | Natural language search query. A semantically rich description of the ideal page, not just keywords. | |
| acquired_after | No | Filter to pages acquired/indexed after this date (YYYY-MM-DD). | |
| acquired_before | No | Filter to pages acquired/indexed before this date (YYYY-MM-DD). | |
| published_after | No | Filter to pages published after this date (YYYY-MM-DD). | |
| published_before | No | Filter to pages published before this date (YYYY-MM-DD). |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description adds the context 'live' to indicate real-time results. It does not detail response format or pagination, but the annotations cover the safety profile adequately.
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 consists of two concise sentences with no wasted words. The key action 'Live web search' is front-loaded, making the purpose immediately clear.
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?
While the description effectively conveys the core search behavior, it lacks details about the response (e.g., result count, sorting) and potential limitations. Given the parameter richness (7 params) and no output schema, additional context would improve completeness for an agent.
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?
With 100% schema description coverage, the baseline is 3. The description adds value by explaining the intent of the query parameter ('describe the ideal page, not keywords') beyond the schema's 'natural language search query', and it reinforces the use of date/site filters.
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 clearly states 'Live web search over the Keenable web index', specifying the verb 'search' and the resource 'web pages'. It distinguishes from the sibling tool 'fetch_page_content' by focusing on discovery rather than retrieval.
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 advises 'Describe the ideal page, not keywords' and prompts using date/site filters, offering concrete query formulation guidance. However, it does not explicitly state when to use this tool versus the sibling 'fetch_page_content', leaving the differentiation implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
The two tools have clearly distinct purposes: one fetches content from a specific page, the other searches the web. There is no overlap.
Both tools use a consistent verb_noun pattern in snake_case: 'fetch_page_content' and 'search_web_pages'.
Only 2 tools, which is below the typical 3-15 range, but the domain is narrow (web search and page fetching), so it may be acceptable.
The set covers the basic workflow of searching and fetching content. Minor gaps exist (e.g., no tool to list search results separately), but the core functionality is complete.
Maintenance
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Related MCP Connectors
Docs: https://docs.keenable.ai/mcp-server Keenable is a free, remote MCP server that gives agents access to the web index. Search the web with ranked results and date/site filters, then fetch any indexed page as clean markdown. Works out of the box with no account or API key.
MCP server (stdio): fetch web pages as clean readable markdown via the AgentForge API
MCP server for Google search results via SERP API
Related MCP Servers
- MIT
- AlicenseAqualityDmaintenanceClaude can perform Web Search | Exa with MCP (Model Context Protocol)225,4621MIT
- FlicenseNot gradedqualityNot gradedmaintenanceEnables web search capabilities within MCP-compatible LLM clients using the Parallel Search API. Designed for daily use and everyday smaller web-search tasks.16

Keenable MCP Serverofficial
AlicenseAqualityBmaintenanceAn MCP server that enables web search and page content retrieval via the Keenable API, supporting search with filters and fetching clean markdown content from indexed URLs.2374MIT
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