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yq_object_store

Apply jq filters to query or transform structured data in the object store without loading full objects. Filter, map, or update nested fields and store results as new objects.

Instructions

Query or transform structured data stored in the object store using a jq filter expression.

Use this to filter, map, select, or rewrite nested data without pulling the whole object into context, e.g. {"object_id": "@obj_001", "filter": ".items[] | select(.active) | .name"}. Transforming filters work the same way, e.g. .config.timeout = 30.

If the targeted value is a JSON or YAML string (e.g. a pipeline's YAML config), it is parsed before the filter runs so you can navigate into its fields; a transformed result is re-serialized back to that same format.

By default, the result is stored as a new object and returned with a preview, so you can reference it further (e.g. get_from_object_store) or feed it back into another yq_object_store call. Set store=False if you just want the full result inline and don't need it stored, e.g. for a small lookup you'll act on immediately.

:param object_id: The id of the object to query in the format @obj_001. :param filter: jq filter expression to apply, e.g. .field, .items[], .[] | select(.status=="done"), or a transformation like .field = "value". :param path: Navigation path to a nested attribute to query instead of the whole object (optional). :param store: Whether to store the result as a new object (default: True). Set to False to get the full result inline instead of a preview, without writing anything to the object store. :return: New object ID and a preview of the result, the full result if store is False, or a message if the filter is invalid or produced no results.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathNo
storeNo
filterYes
object_idYes
Behavior4/5

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

With no annotations, the description must disclose behavior fully. It explains JSON/YAML parsing, re-serialization, the default store behavior, return types (preview vs. full result), and error messages. It does not explicitly state read-only nature but implies it by storing new objects. No contradictions with missing annotations.

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: starts with purpose, then usage guidelines, then detailed parameter explanations. It uses examples and front-loads key information. No redundant sentences; every sentence adds value. Appropriate length for the tool's complexity.

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

Completeness4/5

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

Given no output schema and 0% schema coverage, the description covers purpose, parameters, return values, and behavior with JSON/YAML. It mentions error conditions but lacks details on performance or size limits. Overall complete for a data query/transform tool.

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?

Schema description coverage is 0%, so the description compensates fully. It provides :param explanations for all four parameters: object_id (format with @obj_001), filter (jq expressions), path (optional nested attribute), and store (default True vs. inline). This adds crucial meaning beyond schema titles and types.

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: querying or transforming structured data using a jq filter. It specifies the action (query/transform), resource (object store data), and method (jq filter). It differentiates from siblings like grep_object_store (text search) and sed_object_store (text substitution) by focusing on structured data transformation.

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

Usage Guidelines4/5

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

The description provides clear usage context: 'filter, map, select, or rewrite nested data without pulling the whole object into context.' It includes examples and explains the default behavior. However, it does not explicitly state when not to use or mention alternatives beyond implicit differentiation from siblings.

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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