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FA-Technician-Brain-MCP

FA-Technician-Brain-MCP

by callen-assetworks
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript

get_field

Get field-level details: component type, validation rules, and API mapping by providing a field ID to ensure correct data integration.

Instructions

Get field-level specification including component type, validation, and API mapping.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "field_id": "<field_id>"
    },
    "name": "get_field"
  }
}

Input Schema

NameRequiredDescriptionDefault
field_idYesField identifier (e.g., 'work_order_id', 'employee_id', 'time_code', 'task_id', 'labor_hours', 'delay_code')

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.1/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not state whether this is a read-only safe operation, how much data is returned, whether the field must exist or what the error behavior is, or the granularity of the spec (single field vs. summary). For an unannotated tool with no output schema, this is a notable gap.

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?

A single sentence that is front-loaded and efficient with zero waste. It names the resource, the action, and the key content categories immediately.

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 single-parameter lookup tool with 100% schema coverage, the description is mostly adequate. However, the lack of an output schema and absence of annotations means the description could usefully add return format details or note associations (e.g., 'use after get_table_schema' or 'returns the full spec in JSON'). It covers the essential purpose but leaves return-shape expectations unspecified.

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 the field_id parameter fully documented including example values. Per the rubric, this sets a baseline of 3. The description adds the context that the field spec includes component type, validation, and API mapping, which helps the agent understand what the field_id will be used to retrieve, but it doesn't add syntactic or format details beyond the schema.

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 states a specific verb+resource ('Get field-level specification') and lists the included content categories (component type, validation, API mapping). This is reasonably clear and distinct from siblings like get_validation_rules and get_api_endpoint, though the overlap with get_validation_rules isn't explicitly addressed.

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

Usage Guidelines2/5

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

No guidance is given on when to use this tool versus alternatives like get_validation_rules (which also covers validation) or get_api_endpoint (which covers API mapping). The description implies usage by enumerating content but provides no explicit when-to-use or exclusion guidance.

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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