pixserp is an AI-native search MCP server. Add it to your client and your AI assistant gets access to the live web — with structured citations, across ten answer shapes, from a single tool call.
Web, news, images, places, shopping, flights, hotels, YouTube videos & transcripts, any URL. One endpoint. One bill. Ask in natural language, get a cited answer from the live web.
Scored across 1 tool
With only one tool, there is no possibility of confusion with other tools. The single tool 'search' is clearly distinct by default.
The single tool name 'search' is a clear verb that matches its function. Consistency is trivial with one tool.
One tool is on the low end of typical server sizes. While the tool is versatile and covers many answer shapes, a single tool feels thin for a server that could benefit from separate tools for different search types or actions.
The tool covers a comprehensive set of answer shapes including web, news, images, places, maps, shopping, flights, hotels, YouTube, transcripts, and arbitrary URLs. It also provides AI-synthesized answers with citations, leaving no obvious gaps for a general-purpose web search server.
Search 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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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