pixserp
Server Details
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.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 3.9/5 across 1 of 1 tools scored.
Only one tool exists, so there is no ambiguity whatsoever. The tool's purpose is clearly defined.
With a single tool, naming is inherently consistent. The verb 'search' is appropriate for its function.
One tool is at the low end of the acceptable range. While the tool is versatile and covers many query types, a single endpoint feels minimal for a search server's potential.
The tool claims to handle 10 answer shapes automatically, covering most search needs. However, lacking explicit endpoints for specific types (e.g., image-only search) is a minor gap.
Available Tools
1 toolsearchAInspect
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. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden for behavioral disclosure. The description mentions it returns an AI-synthesized answer with citations and that the answer shape is picked automatically. However, it does not disclose potential side effects, rate limits, or authentication needs. Since this is likely a read-only operation, the lack of such details is not critical, but more transparency about the AI synthesis process would be beneficial.
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 extremely concise at three sentences, each earning its place: first sentence states the core function, second explains the unique multi-shape capability, third provides usage guidance. No redundancy or fluff.
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 has 3 parameters and no output schema, the description covers the purpose and usage well but omits details about the output format beyond 'structured citations.' It also does not guide model selection, which is a key parameter with four options. The description is adequate for a simple search tool but could be more complete by briefly describing the return structure and model recommendations.
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 parameters. The description adds value by explaining that the tool automatically picks the correct answer shape based on the query, which supplements the query parameter's meaning. However, it does not elaborate on model selection strategy or when to use different models beyond the schema's enum names. 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 states the tool's purpose: 'Search the live web and get an AI-synthesized answer with structured citations.' It specifies the verb (search), resource (live web), and adds unique details about covering 10 answer shapes, making the purpose specific and unambiguous. With no sibling tools, differentiation is not tested, but the description stands alone well.
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 explicitly says 'Use this whenever you need fresh, factual information from the web,' providing a clear context for use. It does not mention when not to use or alternatives, but given no siblings and a general-purpose nature, this is acceptable. It could be improved by excluding scenarios like internal database queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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