Skip to main content
Glama

List Arkime cron queries

arkime_crons
Read-only

List scheduled Arkime cron queries and identify which automated search stamped a tag onto session data.

Instructions

List Arkime's cron queries — saved expressions that re-run on a schedule.

    Use this for two questions. First, the same one arkime_views answers:
    which searches has the human team thought worth keeping. Second, and
    only this tool can answer it: where a tag came from. A cron query
    re-runs its expression every few minutes and stamps its own tags onto
    whatever matches, so those tags sit in session data with nothing in the
    session explaining them — this list is the explanation. For saved
    searches nobody schedules use arkime_views, for named value lists (IOC
    sets) use arkime_shortcuts, and to see the tags actually present in the
    data use malcolm_field_values on the `tags` field.

    Disabled queries are listed too — one switched off last week still
    explains tags already sitting in the data. A deployment with none
    configured gets a plain sentence instead of an empty list; that is an
    answer, not a fault (measured: the reference lab has none). Per-query
    fields are in the output schema.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds behavioral details beyond annotations: disabled queries are included, and an empty deployment returns a plain sentence rather than an empty list. It also explains how cron queries stamp tags, providing context. This goes beyond the annotation baseline, though it could mention the openWorldHint implication.

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 front-loaded with a clear one-sentence purpose, then structured into usage guidance and edge-case behavior. Every sentence contributes value, and the length is justified by the need to differentiate from many sibling tools.

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?

With no parameters, an output schema, and clear annotations, the description fully covers purpose, usage, alternatives, edge cases (disabled queries, empty deployment), and points to the output schema for fields. It is complete for an agent to select and invoke the tool 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?

The tool has zero parameters, so the description needs no parameter explanation. The empty input schema is fully covered, and the baseline for 0 params is 4. The description correctly uses the space for usage and behavior instead.

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 clearly states 'List Arkime's cron queries — saved expressions that re-run on a schedule' with a specific verb and resource. It explicitly distinguishes from sibling tools by noting this is the only tool that answers 'where a tag came from' and contrasts with arkime_views, arkime_shortcuts, and malcolm_field_values.

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?

The description provides explicit when-to-use guidance: 'Use this for two questions' and names alternatives: 'For saved searches nobody schedules use arkime_views, for named value lists (IOC sets) use arkime_shortcuts, and to see the tags actually present in the data use malcolm_field_values.' It also explains when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/nagameTW/mcp-server-malcolm'

If you have feedback or need assistance with the MCP directory API, please join our Discord server