get_spy_qqq_divergence
SPY↔QQQ rails divergence feather.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
SPY↔QQQ rails divergence feather.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| result | 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?
No annotations are provided, so the description carries the full burden of disclosing behavior. It does not state whether this is a read-only lookup, what data source it pulls from, how the divergence is computed, what the output represents, or any other behavioral detail.
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 terse, but it is under-specified rather than appropriately concise. 'SPY↔QQQ rails divergence feather' is not a complete statement of purpose or behavior, so the brevity does not serve the agent.
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?
Despite an output schema existing, the description still fails to explain the tool's core function, expected use, or relationship to sibling indicators. This is effectively an opaque label, and the agent would need external knowledge to call or interpret this tool correctly.
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 tool has zero parameters, so the input schema carries no burden and the description does not need to explain parameter semantics. The baseline of 4 applies because there is nothing missing on this dimension.
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 gives a resource ('SPY↔QQQ rails divergence') but no clear verb or explanation of what the tool actually does. Terms like 'feather' and 'rails' are unexplained jargon, making the purpose ambiguous. It does not meaningfully distinguish itself from sibling tools such as get_vrp_divergence or get_cross_asset.
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?
There is no guidance on when to use this tool versus any of its many siblings. The description is a bare phrase with no context, use cases, or exclusions, so an agent has no way to choose it over the related divergence and market indicator 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.
Each tool targets a distinct metric or workflow—alert checks, alert lists, gamma maps, volatility indices, replay timelines, execution plans—so an agent can reliably pick the right one from its description. Even the alert-related tools (get_latest_alert, list_alert_history, check_alert_tradeable) have clearly separate outputs.
All tool names follow a consistent verb_noun snake_case pattern (mostly get_, plus check_, format_, list_, plan_). This makes the set predictable and easy to scan.
At 32 tools, the surface is heavy and approaches a disorganized collection of endpoints rather than a curated set. Many individual get_* indicators could be grouped into a smaller number of dashboard or snapshot tools without losing clarity.
The server covers the core 0DTE intelligence lifecycle: alerts, historical replays, risk overlays, structure, gamma, volatility, news, and advisory planning. Minor gaps exist—such as a direct quote or option chain feed—but they are not essential to the stated purpose.