Fetch and extract content from x402-protected web pages, handling edge payment automatically. Returns clean article content with payment proof. $0.02/call via x402. Free tier: 1 fetch/day (content only).
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to fetch (must be https) | |
| format | No | Output format (default: markdown) | |
| extract_options | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses automatic payment handling, cost ($0.02/call), free tier limits (1 fetch/day), and that it returns payment proof. This is valuable behavioral information beyond the input schema, though it doesn't cover failure modes or what happens if payment fails.
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: two sentences that front-load the main purpose and include cost/free tier info. Every word earns its place, with no redundancy or filler.
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?
For a tool with no output schema and moderate complexity (payment handling, multiple formats), the description provides enough context: purpose, cost, free tier, return content. It doesn't explain error cases or detailed usage, but it's sufficient for an agent to decide when to call it and what to expect.
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?
The input schema already describes all three parameters with reasonable detail (URL must be https, format enum, selector note). The description adds context about output being 'clean article content' and mentions 'content only' for the free tier, but doesn't deeply elaborate on parameter meanings. With 67% schema coverage, a 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 uses a specific verb 'Fetch and extract' with a clear resource ('x402-protected web pages') and scope ('handling edge payment automatically'). It distinguishes itself from generic tools like web_extract by focusing on x402-protected content and automatic payment.
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 clearly implies when to use this tool: for x402-protected pages where edge payment is needed. It doesn't explicitly name alternatives or exclusions, but the automatic payment handling is a strong contextual indicator that sets it apart from ordinary fetch tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tool groups are nearly indistinguishable: wallet_analyze, wallet_spy, and base_wallet_profile all inspect wallets; batch_extract, batch_url_json, and x401_batch_extract all batch-extract URLs; route_task, agentcore_route, and mpp_route all perform routing. An agent would need to read very carefully to avoid selecting the wrong tool.
All names are snake_case, but there is no consistent verb_noun or namespace pattern: many are noun-only (inference, echo, sentiment, server_time), some are prefixed by domain (bazaar_, base_, x402_, rep_), and action prefixes vary widely (fetch_, compile_, extract_, purchase_, route_). The naming is readable but not predictable across the set.
Seventy tools is an extremely large surface for an agent to choose from, and most appear to be independent paid service wrappers. This exceeds the 50+ extreme mismatch threshold in the calibration and creates an overwhelming selection problem.
Relative to its apparent purpose—exposing x402 payments and Bazaar market data—the coverage is extensive: diagnostics, preflight, settlement verification, receipt lookup, wallet checks, Bazaar analytics, web extraction, and text processing are all represented. The main gaps are operational side-effects like creating or updating a Bazaar listing, but those appear to be outside this read/purchase surface.