AgenticTotem Web Extractor
Server Details
AI web extraction: send URLs + a JSON Schema, get clean structured data. Pay-per-use via x402.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4/5 across 1 of 1 tools scored.
With only one tool, there is no possibility of confusion between tools. The single web_extract tool has a clearly defined purpose, eliminating any ambiguity.
The single tool name 'web_extract' follows a consistent verb_noun pattern. With only one tool, naming consistency is trivially maintained.
The server has only one tool, which is on the borderline for minimalism. It is a focused utility but may feel thin for users expecting a broader toolkit.
The tool covers the core extraction workflow end-to-end: sending URLs and a schema, receiving structured data. Minor auxiliary features like billing or history are not included, but they are not essential for the primary purpose.
Available Tools
1 toolweb_extractAInspect
Extract structured data from web pages. Send 1-10 URLs and a JSON Schema describing the data shape you want. Returns extracted data for each URL. Costs $0.01 USDC per URL via x402 or MPP.
| Name | Required | Description | Default |
|---|---|---|---|
| urls | Yes | URLs to extract data from (http/https only) | |
| schema | Yes | JSON Schema with type "object" and properties describing desired output shape |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It transparently mentions the cost per URL and the payment methods (x402 or MPP), which is valuable. However, it does not disclose error handling, failure behavior, or how results are structured if individual URLs fail, leaving some behavioral gaps.
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 three sentences, front-loaded with the core purpose, and every sentence adds relevant information: what the tool does, how to use it, what it returns, and the cost. There is no wasted language.
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 only two parameters, both fully described in the schema, and no output schema, the description covers the essentials: purpose, inputs, output, and cost. It could be more complete by addressing error handling or response format edge cases, but it is sufficient for straightforward use.
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%, with both 'urls' and 'schema' parameters described in the input schema. The description adds that 1-10 URLs are accepted and that the schema describes the desired output shape, but it does not add meaning beyond what the schema already provides. Baseline of 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 extracts structured data from web pages, with a specific verb ('extract') and resource ('web pages'). It also specifies the inputs (URLs and JSON Schema) and output (extracted data), making its purpose unambiguous even without sibling tools.
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 clear usage context: send 1-10 URLs and a JSON Schema, and it returns extracted data. It also mentions costs, which is useful for deciding when to use the tool. There are no sibling tools to differentiate from, so the lack of explicit exclusions is acceptable.
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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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
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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
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Credentials required to access the server are missing or invalid
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