Run up to 10 bounded HTTPS URL-to-JSON extraction jobs for one prepaid, idempotent completion receipt. $0.02/call via x402; one reconciled batch of up to 10 jobs.
| Name | Required | Description | Default |
|---|---|---|---|
| jobs | Yes | ||
| idempotency_key | Yes |
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, the description carries the full burden and does disclose important behavioral traits: it is a pre-paid operation ($0.02/call via x402), idempotent via a receipt, and limited to 10 jobs. However, it does not describe failure semantics, partial job failures, return format, or authentication requirements, leaving gaps for a paid operation.
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, front-loaded with the core purpose, and includes only essential details (cost, idempotency, batch limit). There is no fluff or repetition of schema field names.
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 paid batch operation with no output schema, the description covers the core function and payment but lacks details about what the returned receipt/batch looks like, error handling, or retry behavior. It is adequate for basic selection but not fully complete for safe invocation.
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 schema has 0% description coverage, so the description must compensate. It adds meaning by explaining that 'jobs' are URL-to-JSON extractions and limits them to 10. It does not explicitly explain 'idempotency_key' beyond the word 'idempotent,' which is a partial gap. Still, the description provides more than the schema alone.
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 action: 'Run up to 10 bounded HTTPS URL-to-JSON extraction jobs.' It specifies the resource (URL-to-JSON extraction), batch limit, and adds unique traits (prepaid, idempotent receipt, cost) that distinguish it from siblings like web_extract or fetch_x402_content.
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 implies usage for batch extraction of up to 10 URLs and mentions a cost, offering some context. However, it does not explicitly state when to use this tool vs alternatives, nor when not to use it. The batch size and payment model serve as implicit guidance but no clear exclusions or alternatives are named.
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.