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create_filter

Create a data filter on a module or integration pipeline to transform log data using steps like rename, add, grok, json, kv, or delete.

Instructions

Create a new data filter on a pipeline.

logstash_filter: the YAML pipeline text. Steps include rename, add (with where), grok, json, kv, delete. Example to standardize a username value:

pipeline:
  - dataTypes: [generic]
    steps:
      - add:
          function: "string"
          params: {key: target.user, value: "standard.name"}
          where: equals("target.user", "legacy_name")

NOTE: user filters can only be created on module/integration pipelines; the built-in Windows/Linux agent 'system' filters are edited in the web UI, not via this API. server: which configured server to target (default active/default).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
activeNo
serverNo
data_typeYes
filter_nameYes
pipeline_idYes
data_type_idYes
logstash_filterYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description carries full responsibility for behavioral disclosure. It does not mention any side effects, permissions, idempotency, or constraints beyond the note on pipeline types. The agent cannot infer safety or mutation semantics from the description alone.

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

Conciseness4/5

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

The description is structured with a clear opening, a detailed explanation of the complex logstash_filter parameter with an example, and a usage note. It is not overly verbose for the complexity it covers. Slightly longer than minimal due to example, but that adds value.

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

Completeness3/5

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

With 7 parameters (5 required) and no output schema explanation needed (since output schema exists), the description provides good context for logstash_filter and usage constraints. However, it fails to explain what the other required parameters mean or how the active flag works, leaving the agent to guess or rely on schema names.

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

Parameters3/5

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

The schema has 0% parameter description coverage, so the description must compensate. It does so for logstash_filter by explaining its YAML format, listing steps, and giving an example. It also briefly mentions the server parameter. However, other required parameters like pipeline_id, filter_name, data_type_id, data_type, and active are left unexplained, leaving gaps.

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

Purpose4/5

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

The description clearly states 'Create a new data filter on a pipeline' with a concrete verb and resource. It details the logstash_filter parameter with an example. However, it does not explicitly differentiate from sibling tools like get_filters_by_pipeline or delete_filter, so it loses a point for missing sibling distinction.

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 explicitly notes that user filters can only be created on module/integration pipelines, not on built-in system filters which must be edited via the web UI. This provides clear when-to-use and when-not-to-use guidance, along with a mention of the server parameter's default behavior. This is exemplary.

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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