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callen-assetworks

FA-Technician-Brain-MCP

get_test_fixtures

Retrieve test fixture data for automation by filtering on entity type or fetching a specific fixture by ID, providing structured test data on demand.

Instructions

Get test fixture data for automation. Filter by entity type or retrieve a specific fixture by ID.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoSpecific fixture ID (e.g., 'default_technician', 'test_work_order_open', 'test_equipment_fleet_004')
entityNoFilter by entity type (e.g., 'Operator', 'Equipment', 'WorkOrder', 'Vendor', 'TimeCode', 'Location')
Behavior3/5

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

No annotations are provided, so the description carries the full burden. The description establishes this is a read-only retrieval operation ('Get test fixture data') which signals non-destructive behavior. However, it doesn't disclose what a fixture returns, whether results differ by entity type, or any caveats about fixture completeness or staleness.

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?

One sentence, two distinct retrieval modes, zero filler. Every clause carries meaning - the purpose verb, the tool domain, and both access patterns are packed into a compact sentence.

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?

For a simple read tool with 2 well-documented parameters and 100% schema coverage, the description is reasonably complete. The main gaps are the absence of any return-format indication and no mention of the context for when fixtures are appropriate (e.g., only for certain environments). No output schema exists to compensate.

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?

Schema description coverage is 100%, with both 'id' and 'entity' parameters having descriptive text in the schema itself, including concrete examples. The description adds the relationship between the two params (filter vs. retrieve by ID) but doesn't add format or syntax details beyond what the schema already provides. Baseline 3 is appropriate.

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 'Get test fixture data for automation' with a specific verb (get) and resource (test fixture data). It adds filtering capabilities (by entity type or ID) that distinguish it from sibling get_* tools like get_api_endpoint, get_table_schema, etc., though it doesn't explicitly name these alternatives.

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

Usage Guidelines3/5

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

The description implies usage for automation testing workflows and mentions two retrieval modes (filter by entity type, get by ID), but provides no explicit when-to-use guidance or exclusions versus sibling tools like get_workflow or get_table_schema. Sibling names are discoverable but no differentiation is stated.

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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