Multi-day historical graded events from scanner log (optional YYYY-MM-DD).
| Name | Required | Description | Default |
|---|---|---|---|
| date | No | ||
| limit | No | ||
| min_score | No |
| 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?
With no annotations, the description carries the full burden of behavioral disclosure, but it only states the data source and scope. It does not explain how the optional date behaves when omitted, how limit and min_score affect results, whether events are sorted, or any pagination behavior. The phrase 'graded events' hints at scoring, but that is not enough for a tool with no annotation safety or behavior hints.
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 single front-loaded sentence with no filler or repetition. It packs the key scope ('multi-day', 'historical', 'scanner log') into a compact phrase. However, it is thin enough that it misses important semantic detail, so it earns a strong but not perfect conciseness score.
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 three optional parameters, no annotations, low schema coverage, and many sibling tools, this description is not complete enough for an agent to invoke the tool confidently. The output schema may document return shape, but the description still fails to clarify parameter semantics, default behavior for date, or when to choose this over get_replay_today. The core selection and invocation context is missing.
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%, so the description must compensate for explaining all parameters. It only covers 'date' with 'optional YYYY-MM-DD', and does not explain 'limit' or 'min_score' at all. The one parameter it does mention adds some meaning, but the other two are left completely undocumented.
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 identifies the resource as 'multi-day historical graded events from scanner log' and indicates an optional date parameter. It lacks an explicit action verb like 'retrieve' or 'list', but the tool name plus the resource scope make the purpose reasonably clear. The 'multi-day' qualifier also helps distinguish it from get_replay_today.
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?
No guidance is provided about when to use this tool versus alternatives such as get_replay_today or list_alert_history. The phrase 'multi-day historical' implies past-date usage, but it never explicitly says 'use this for historical replays' or 'use get_replay_today for the current day's replay'. The agent is left to infer the selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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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.