quantum_offer
OracleNet Offer Card: catalog with pricing, SLAs, payment methods.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| tool | No | Optional filter |
OracleNet Offer Card: catalog with pricing, SLAs, payment methods.
| Name | Required | Description | Default |
|---|---|---|---|
| tool | No | Optional filter |
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 of behavioral disclosure. It does not state whether this is a read-only operation, what data it returns, or any side effects. The behavior is entirely implicit from the word 'catalog', which is insufficient.
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 a concise single sentence that efficiently conveys the general content of the tool. It is not overly verbose, but it also doesn't provide any useful detail beyond a label.
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 low complexity (one optional parameter, no output schema), the description is minimally adequate to understand the tool's domain. However, it lacks details about the filter behavior and return format, leaving some ambiguity about the tool's full scope.
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 single parameter 'tool' is described in the schema as 'Optional filter', providing 100% schema coverage. The description does not add any additional context about how the filter affects results, but it also doesn't contradict the schema. Baseline 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 identifies the tool as an OracleNet Offer Card catalog with pricing, SLAs, and payment methods, giving a general idea of its resource. However, it lacks an explicit action verb and does not distinguish it from sibling tools like quantum_deal or quantum_rate, making the purpose somewhat vague.
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 no guidance on when to use this tool versus alternatives, no prerequisite information, and no exclusions. It only describes what the tool is, not when to invoke it.
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.