get_index_health
Index Health Dashboard: relative volume & momentum for top components. Bundle tier. index: "SPY" | "QQQ"
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| index | No | SPY |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Index Health Dashboard: relative volume & momentum for top components. Bundle tier. index: "SPY" | "QQQ"
| Name | Required | Description | Default |
|---|---|---|---|
| index | No | SPY |
| 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 mentions the computed signals and index choices, but it does not clarify whether the tool is read-only, what time window or data source is used, or what 'health' means operationally beyond the two metrics.
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 core content is short and front-loaded, but the fragment 'Bundle tier.' adds no actionable information, and the parameter hint is formatted as a terse inline leftover. It is compact but not every part earns its place.
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 low complexity, a single optional parameter, and an output schema, the description is minimally viable: it states the subject matter and the parameter choices. However, it lacks any broader context for when to use the tool, and the cryptic 'Bundle tier.' line slightly undermines clarity.
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?
Schema description coverage is 0%, but the description partially compensates by specifying the allowed values: index: 'SPY' | 'QQQ'. It does not explain that these are tickers or how the value affects the result, but the parameter name and default make the meaning reasonably self-evident.
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 resource as an 'Index Health Dashboard' and the substance as 'relative volume & momentum for top components,' which goes beyond a simple restatement of the name. It does not use an explicit verb like 'retrieve' or 'show,' but the intent is clear and distinguishable from the sibling market tools.
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 about when to use this tool, what question it answers, or which sibling tool might be a better alternative. The only practical hint is the allowed index values, so an agent must infer the appropriate context on its own.
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.