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Data coverage and freshness

malcolm_data_coverage
Read-only

Summarize existing data: live sensors, newest data age, per-dataset document volumes, and index count. Use before hunting to verify which sensors are active and how fresh their data is.

Instructions

Summarize what data exists: feeding sensors, freshness, and per-dataset volume.

    Use this before a hunt to see which sensors are live, how stale the newest data
    is (latest_age_seconds), document counts per event.dataset (conn, dns, ssl,
    alert, ...), and index count. For overall service/stack health rather than data
    volume, use malcolm_service_status. For distinct values of one arbitrary field
    rather than the dataset breakdown, use malcolm_field_values. Returns a JSON
    summary; each sub-section reports its own error key on failure instead of
    aborting, unless every one of them fails, which raises.

    The time range scopes the per-dataset counts ONLY — sensor liveness,
    latest_age_seconds and the index count come from endpoints that take no
    range at all. So a narrow window cannot make a live sensor look dead,
    but it will make a busy dataset look empty.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
time_toNoEnd time for the per-dataset counts, dateparser format. Empty = now.
time_fromNoStart time for the per-dataset counts, dateparser format. Empty = the last 24 hours; sensor liveness and the index count ignore this argument.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the read-only annotation, the description discloses key behavioral traits: error handling per sub-section with fallback to raising only if all fail, and the critical time-range scoping nuance (applies only to counts, not liveness or index count). This adds valuable context not captured by annotations alone.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear summary sentence, usage guidance, and a behavioral note. Every sentence contributes essential information, with no redundancy or filler. It is appropriately detailed for the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers all essential aspects: purpose, usage timing, alternatives, error behavior, and the precise semantics of the time range. Since an output schema exists, it does not need to explain return values, making this complete for an agent to select and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema coverage, the parameters are already well documented. The description adds meaningful nuance by clarifying that the time range only affects per-dataset counts and that sensor liveness and index count ignore it, which is not obvious from the schema descriptions alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a clear verb and resource: 'Summarize what data exists: feeding sensors, freshness, and per-dataset volume.' It also explicitly differentiates from sibling tools by naming malcolm_service_status and malcolm_field_values as alternatives for different use cases.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It states exactly when to use the tool ('before a hunt') and what it reveals. It also gives direct exclusions: 'For overall service/stack health... use malcolm_service_status' and 'For distinct values... use malcolm_field_values,' making the selection unambiguous.

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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