Live AI-native web search with citations. One tool for every MCP client. Flat per-request pricing.
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Average 4.2/5 across 1 of 1 tools scored.
With only a single tool, there is no possibility of confusion or overlap. The tool's purpose is clear and distinct.
The tool name 'search' is simple and directly reflects its function. Although there is no pattern to compare, it is consistent and unambiguous.
The server has only one tool, which feels thin for a typical server. However, the tool itself is broad and covers many search types, so it earns a borderline score rather than a lower one.
The single tool covers web, news, images, places, shopping, flights, hotels, YouTube, transcripts, and URLs, providing comprehensive search coverage. There are no obvious gaps in the search domain.
searchAInspectSearch the live web and get an AI-synthesized answer with structured citations. One endpoint covers 10 answer shapes — web, news, images, places & maps, shopping, flights, hotels, YouTube, transcripts, any URL — the right one is picked automatically from your query. Use this whenever you need fresh, factual information from the web.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | `pixserp-fast` = quick single-search lookup (default). `pixserp-standard` = balanced. `pixserp-deep` = multi-angle thorough research. `pixserp-agent` = multi-step research loop, decides itself when to stop. | pixserp-fast |
| query | Yes | What to search for. Phrase as a natural-language question or a specific topic. | |
| max_steps | No | Only used when `model = pixserp-agent`. Caps the research loop length. Default 50, max 100. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral transparency. It discloses that the tool returns an AI-synthesized answer with citations and that the appropriate answer shape is selected automatically from the query. This conveys key behavioral attributes beyond what a schema would show, though it does not cover potential limitations like data freshness or rate limits.
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 and well-structured, with three sentences that each deliver value: core function, scope of answer shapes, and usage guidance. There is no redundant or filler content, making it effectively front-loaded and easy to parse.
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 the tool's complexity (multiple answer shapes, model enums, and no output schema), the description offers a solid overview. It clarifies the automatic selection of answer shapes and the intended use case, which is sufficient for an agent to decide when to invoke it, though it does not detail the response structure.
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 minimal parameter-specific meaning—only implying that the query determines which answer shape is used. This is consistent with a baseline score of 3 since the schema does the heavy lifting.
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 function: 'Search the live web and get an AI-synthesized answer with structured citations.' It specifies the resource (live web) and the action (search), and adds detail about the 10 answer shapes, making the purpose explicit and comprehensive.
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 provides a clear usage context: 'Use this whenever you need fresh, factual information from the web.' It does not explicitly mention when not to use it or name alternatives, but given no sibling tools exist, the guideline is sufficient and well-articulated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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For users:
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For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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