Skip to main content
Glama

Search index fields

malcolm_field_search
Read-only

Find and confirm field names in Malcolm's index by keyword, prefix, or type before running queries, avoiding non-standard naming issues.

Instructions

Discover which field NAMES exist in Malcolm's index, by keyword, prefix, or type.

    Use this first, before any query, to confirm a field name exists — Malcolm uses
    non-standard names (e.g. http.useragent, NOT http.user_agent). To then see the
    VALUES a field holds, use malcolm_field_values; to see which datasets contain
    it, use malcolm_field_profile. Do NOT source an arkime_* argument from here:
    these are the names malcolm_* and search_dsl take, and Arkime has its own
    spelling for the same field (ip.src, srcIp) that arkime_field_search reports.
    Pass at least one argument. Returns a text list of "name (type)" lines,
    sorted alphabetically.

    Arguments narrow (AND), they never widen, and the mapping is big enough
    that one keyword rarely lands: it runs to thousands of fields, and a
    keyword as common as "ip" matches over a thousand of them on its own.
    The header line counts every match but only the first 100 names are
    printed, so add a prefix or a field_type rather than reading the printed
    list as the whole answer.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
prefixNoField-name prefix to match, e.g. "zeek.dns", "suricata.alert", "rule". Empty = no prefix filter.
keywordNoSubstring to match anywhere in a field name, e.g. "useragent", "signature". Empty = no keyword filter.
field_typeNoFilter by the type Malcolm reports for a field — measured on Malcolm v26.07.1 those are "string", "integer", "float", "date", "ip" and "geo". They are NOT OpenSearch type names: "keyword", "long" and "text" match nothing here, even though index_mapping reports the same fields under those names. Empty = any type.

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/destructive annotations, the description discloses key behaviors: arguments narrow (AND) and never widen, results are capped at 100 printed names while the header counts all matches, and the field-type values are non-OpenSearch names. This provides meaningful context the annotations alone cannot convey.

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 its core purpose and then packs in high-value caveats and examples. While longer than typical, every sentence adds necessary operational detail, and there is no redundancy or filler.

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?

Given the tool's complexity—interaction with sibling tools, non-standard field names, result truncation, and AND semantics—the description is remarkably complete. It covers return format, limits, exclusions, and parameter behavior, so an agent has everything needed to invoke it accurately.

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

Parameters5/5

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

Though the schema already documents all three parameters, the description adds substantial extra semantics: examples for prefix and keyword, an explanation that arguments narrow results in an AND fashion, and the precise allowed field_type values (string, integer, float, date, ip, geo) along with a warning that OpenSearch type names like 'keyword' will not match. This goes far beyond the schema.

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 the tool's purpose: discovering field NAMES in Malcolm's index by keyword, prefix, or type. It explicitly distinguishes itself from sibling tools by naming malcolm_field_values for values, malcolm_field_profile for datasets, and arkime_field_search for Arkime-specific spellings.

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?

Provides explicit guidance: 'Use this first, before any query, to confirm a field name exists.' It also gives exclusions ('Do NOT source an arkime_* argument from here') and names the correct alternative tools for different needs. This is a model of when-to-use and when-not-to-use.

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