Passively inspects public machine-readable discovery surfaces for a domain. Returns declared and observed evidence, coverage, assurance and a bounded NOMOS read-only admission decision. It does not execute third-party tools, make payments, certify identity or issue compliance verdicts.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | Bare public domain, e.g. example.com. URLs, IP literals, ports, credentials and local names are rejected. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / domain / descriptionPrevious value: -"Domain to scan (e.g. openai.com, stripe.com, your-startup.io)"New value: +"Bare public domain, e.g. example.com. URLs, IP literals, ports, credentials and local names are rejected."Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses the passive nature of the scan, the type of output (declared/observed evidence, coverage, assurance, and a read-only admission decision), and what it does not do. This gives an agent a clear behavioral profile without needing annotations.
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 with no wasted words. It is front-loaded with the core purpose, then describes outputs, then lists exclusions. Every sentence serves a clear informational role.
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?
Although there is no output schema, the description explains what is returned (evidence, coverage, assurance, admission decision). It also clarifies non-behaviors. For a one-parameter tool, this is quite complete, though terms like 'NOMOS' would benefit from elaboration.
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 provides a thorough description for the single 'domain' parameter, including examples and rejections. The tool description adds no extra parameter meaning. Since schema coverage is 100%, 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 clearly states the tool's function with a specific verb ('passively inspects') and resource ('public machine-readable discovery surfaces for a domain'). It also lists explicit exclusions ('does not execute third-party tools, make payments, certify identity or issue compliance verdicts'), which distinguishes it from siblings like quantum_execute or quantum_deal.
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?
Usage is implied rather than explicitly stated. The description outlines what the tool does and what it does not do, giving context, but it does not mention specific alternatives or provide direct 'when to use vs. when not to use' guidance. The exclusions help, but fall short of explicit alternatives or a clear usage decision tree.
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 tools have unclear boundaries: quantum_ask and quantum_intent both serve as natural language 'front doors' with similar descriptions, and quantum_route and quantum_refer both find the best oracle for a task. This creates significant overlap that could cause agents to select the wrong tool.
Most tools follow a consistent quantum_<verb_or_noun> snake_case pattern (e.g., quantum_join, quantum_execute, quantum_settle). The single exception is neural_status, which breaks the prefix convention, but overall the naming is predictable and readable.
With 20 tools, the server sits in the heavy range (16-25). While the broad scope of OracleNet (intelligence, natural language, deals, reputation, subscriptions, scanning) somewhat justifies the count, it feels overpacked and could be streamlined by merging overlapping tools.
The tool surface covers core workflows: joining, querying status/nodes/reputation, routing, executing, dealing, settling, rating, and subscribing. However, lifecycle gaps exist—no unsubscribe, leave/delete node, deal cancellation, or dispute resolution—which can leave agents with dead ends.