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.
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 4.2/5 across 1 of 1 tools scored.
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.
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?
Without annotations, the description carries the full burden. It explains that the tool searches the live web, provides AI-synthesized answers, and automatically picks the answer shape. It also details model behaviors (fast, standard, deep, agent) and the max_steps parameter for the agent mode. However, it does not disclose any side effects, rate limits, authentication needs, or explicitly state that it is read-only.
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 sentences plus a sentence about parameters. It is front-loaded with the main purpose, and every sentence serves a distinct purpose: stating the core functionality, listing answer shapes and automatic selection, and providing usage guidance. No wasted words.
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 (10 answer shapes, multiple models) and the absence of an output schema, the description covers the main use cases well. It explains that the answer shape is selected automatically, which helps users understand they don't need to specify a shape. It could be slightly more specific about how to target specific answer shapes, but overall it provides sufficient context for an AI 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?
Schema coverage is 100%, so the baseline is 3. The description adds value by explaining the semantic differences between model enum values (e.g., 'pixserp-fast = quick single-search lookup'), providing guidance on query phrasing ('Phrase as a natural-language question or a specific topic'), and clarifying that max_steps is only used when model = pixserp-agent. This goes beyond what the schema provides.
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 it searches the live web and returns an AI-synthesized answer with structured citations. It also lists 10 answer shapes and explains that the correct shape is chosen automatically from the query, making the tool's purpose very specific and distinguishable, even without siblings.
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 clear when-to-use guidance. It does not mention alternatives or when not to use, but the absence of sibling tools makes this less critical.
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.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
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.
No comments yet. Be the first to start the discussion!
