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., "@multi-api-web-search Search for the latest breakthroughs in fusion energy with cited 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.
Universal multi-model web search MCP server with precision capability probing, dual-mode routing, and multi-engine consensus.
Dual-Mode Routing: Benchmark and dispatch between Native Grounding (fast, silent retrieval) and Explicit Tool (web_search tool calling with rich citations).
Multi-Engine Consensus: Query multiple models concurrently (Hybrid), cross-verify perspectives, and deduplicate citations.
Related MCP server: perplexity-mcp
git clone https://github.com/Peteroooooooo/multi-api-web-search-mcp.git
cd multi-api-web-search-mcp
npm installcp .env.example .envEdit .env:
MULTI_WEB_SEARCH_BASE_URL=https://api.your-provider.com/v1
MULTI_WEB_SEARCH_API_KEY=sk-your-key-hereWindows: Double-click doctor.bat
Linux / macOS:
npm run doctorSelect your preferred model or input multiple indices (e.g. 1, 5, 2) for Consensus mode. The wizard writes optimal configurations directly into .env.
~/.claude.json){
"mcpServers": {
"multi-api-web-search": {
"type": "stdio",
"command": "node",
"args": ["D:/path/to/multi-api-web-search-mcp/src/index.js"],
"env": {
"MULTI_WEB_SEARCH_BASE_URL": "https://api.your-provider.com/v1",
"MULTI_WEB_SEARCH_API_KEY": "sk-your-key",
"MULTI_WEB_SEARCH_MODEL": "hybrid",
"MULTI_WEB_SEARCH_HYBRID_MODELS": "grok-chat-fast,gemini-3.8-flash,gpt-5-6",
"MULTI_WEB_SEARCH_FORCE_NATIVE": "gpt-5,gemini"
}
}
}
}.cursor/mcp.json){
"mcpServers": {
"multi-api-web-search": {
"command": "node",
"args": ["D:/path/to/multi-api-web-search-mcp/src/index.js"],
"env": {
"MULTI_WEB_SEARCH_BASE_URL": "https://api.your-provider.com/v1",
"MULTI_WEB_SEARCH_API_KEY": "sk-your-key",
"MULTI_WEB_SEARCH_MODEL": "gemini-3.8-flash",
"MULTI_WEB_SEARCH_FORCE_NATIVE": "gpt-5,gemini"
}
}
}
}Variable | Required | Default | Description |
| Yes | - | OpenAI-compatible endpoint base URL |
| Yes | - | API key |
| No |
| Default search model (or |
| No |
| Comma-separated models for consensus search |
| No | - | Comma-separated models forced to use Native Grounding (prevents 504 timeouts) |
| No |
| Upstream request timeout in ms |
The MCP server registers multi_api_web_search and alias web_search:
query (string, required): Search query.
model (string, optional): Override search model for this request (e.g. grok, gemini, gpt-5-6, hybrid).
max_sources (number, optional): Maximum sources to return (default: 15).
MIT
Multi-API Web Search: Live internet search across your configured API models. Supports single model or multi-engine consensus ('hybrid').
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | Optional search engine mode or model name (e.g. 'gemini', 'grok', 'fast', 'hybrid' for multi-engine consensus). | |
| query | Yes | Search query or research question in natural language. | |
| max_sources | No | Max number of sources to include (1-30, default 15). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden. It does convey that this performs live external searches and can aggregate multiple engines into a consensus, but it omits operational traits like rate limits, failure behavior, and unconfigured model handling.
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 two tight sentences with no filler. The core operation is front-loaded, and the second sentence adds meaningful scope about single-model and hybrid consensus.
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 no annotations, no output schema, and a sibling tool to differentiate from, the description is incomplete. It does not clarify default model behavior, expected result shape, or when to use this tool instead of web_search.
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%, so the schema already documents all parameters. The description adds no real parameter semantics beyond restating the hybrid mode that is already described in the model parameter.
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 states a specific operation: live internet search across configured API models, with an optional hybrid consensus mode. It is clear and distinct from a vague generic search, but it does not explicitly position itself against the sibling web_search tool.
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?
There is no guidance on when to choose this tool over web_search, nor when to prefer single-model vs hybrid mode. The mention of hybrid implies a capability but does not provide selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Multi-API Web Search: Live internet search across your configured API models. Supports single model or multi-engine consensus ('hybrid').
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | Optional search engine mode or model name (e.g. 'gemini', 'grok', 'fast', 'hybrid' for multi-engine consensus). | |
| query | Yes | Search query or research question in natural language. | |
| max_sources | No | Max number of sources to include (1-30, default 15). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden of behavioral disclosure. It states that the search is live and may span multiple configured API models, but it does not describe result format, failure behavior, rate limits, or what hybrid consensus actually returns. This is minimal behavioral transparency.
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 front-loaded sentence with no filler. It communicates the core capability efficiently, though it omits deeper usage guidance that would have made it more complete.
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 simple three-parameter search tool, the minimum invocation is well covered by the schema and the live-search statement. However, with no output schema and no annotations, the absence of result-format details and the lack of distinction from the sibling tool leave notable gaps.
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 schema already documents all three parameters adequately. The description adds the idea of hybrid consensus but does not meaningfully extend parameter semantics beyond the schema. Baseline 3 is appropriate.
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 identifies a live web search action and explains single-model vs hybrid consensus modes. It names the resource and action, making the tool's purpose understandable. However, it doesn't distinguish this tool from sibling multi_api_web_search, especially since the description itself says 'Multi-API'.
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?
There is no explicit guidance on when to use web_search versus multi_api_web_search, and the sibling is not mentioned. The only usage signal is that 'hybrid' enables multi-engine consensus, which implies a mode choice but not when this tool should be preferred over an alternative.
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.
v2.3.0multi_api_web_searchweb_searchBoth tools have the exact same description and purpose, differing only in the name prefix. An agent cannot reliably choose between them because they appear to be identical duplicates.
Both names use snake_case, but 'web_search' and 'multi_api_web_search' are near-duplicates with no clear convention. The prefixed variant is inconsistent with the generic one, making the naming feel redundant rather than patterned.
Two tools exist but they appear to serve the exact same function. One tool would be appropriate for this simple search server; the extra tool is redundant and does not earn its place.
For a web search server, the tool surface covers the core capability of performing live internet searches. No additional operations like create, update, or delete are relevant to this domain, so there are no obvious gaps.
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.
Multi-engine search for AI agents. Trust scoring, local corpus, MCP-native. Self-hostable, BYOK.
Scrape, crawl and search the web for AI agents via MCP.
Search the agentic web. 4,100+ sites, 11 tools incl. check_url + verify_mcp for probe-before-use.
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