mcp-unifi-applications

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Find Field

find_field

Read-onlyIdempotent

Search every UniFi API endpoint for a field name, including nested discriminator variants. Returns each occurrence with endpoint, dotted path, and schema section, capped at 50 with true totals, plus close alternatives for no matches.

Instructions

Locate a field by name across every endpoint, including inside discriminator variants.

Returns one line per occurrence: endpoint slug, dotted path, and which schema section it sits in (request body, parameters, or response). Common names appear hundreds of times; the reply is capped at 50 and states the true total and how many endpoints are involved, so a short list is never mistaken for a complete one. Paths from here can be passed straight to get_field_schema. A name that matches nothing returns close alternatives.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`slug`	No	Optional — limit search to a specific endpoint.
`field_name`	Yes	The field name to search for (case-insensitive).

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 9, 2026

TDQS

A4.4/5.0

Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive, and the description layers substantial behavior on top: the 50-line cap with true total and endpoint count, the close-alternatives fallback, the response format, and case-insensitivity. This is exactly the contextual value annotations cannot provide.

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?

Purpose is front-loaded in the first sentence, and every subsequent sentence earns its place (format, cap, successor tool, fallback). Slightly long but information-dense with no 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?

For a search tool with an output schema, the description covers everything an agent needs: result format, line-by-line fields, pagination/truncation honesty, endpoint-count reporting, and downstream integration. Nothing actionable is missing.

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 coverage is 100% — both parameters are documented, including case-insensitivity on field_name. The description adds the common-names-capped behavior but otherwise relies on the schema, which is the appropriate division of labor at the baseline.

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?

States a specific verb ('locate') and resource ('field by name across every endpoint'), and adds the discriminator-variant nuance that shows real scope understanding. This cleanly separates it from siblings like list_endpoints and get_field_schema.

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?

Explicitly names how output feeds into get_field_schema ('Paths from here can be passed straight to'), connecting this tool to its successor. It does not enumerate when-not-to-use cases, but the purpose and output semantics make the use case unambiguous.

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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