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KallistoX

mcp-unifi-applications

Get Endpoint

get_endpoint

Retrieve the complete schema for any UniFi API endpoint by providing its slug. Get a compact field summary or raw JSON to understand endpoint parameters.

Instructions

Get the full schema for a UniFi API endpoint.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesEndpoint identifier (e.g. 'createnetwork', 'listnetworks'). Use list_endpoints or search_endpoints to find slugs.
summaryNoIf True, return a compact field summary. If False, return raw JSON.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

B3.2/5.0
Behavior2/5

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

Annotations are completely absent, so the description carries the full burden. The description only says 'Get the full schema,' which is minimal. It does not disclose any behavioral traits such as output format nuances, potential error conditions, rate limiting, or what 'full schema' means. The schema exists but the description adds no behavioral context beyond the tool's basic action.

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 a single sentence that is concise and front-loaded with the action. No extra fluff. However, given the tool's complexity and absent annotations, a bit more behavioral context could be added without much bloat, so it's not a 5.

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?

The tool has an output schema, which likely conveys the return structure, so that part is covered. The parameters are well-documented in the schema. However, with no annotations and a minimal description, the agent lacks information on when to use this tool vs othersabbildung, the exact nature of the schema returned, and potential edge cases. For a tool that retrieves schemas, it is minimally adequate but not thorough.

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 input schema already provides detailed descriptions for both parameters: 'slug' explains format and how to find it, 'summary' explains true/false behavior. Since schema coverage is 100%, the description adds little extra. The description itself mentions no parameter details, so it does not compensate beyond the schema, which is adequate.

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 the verb and resource: 'Get the full schema for a UniFi API endpoint.' It distinguishes the tool from siblings by focusing on schema retrieval, though it doesn't explicitly name which sibling to use instead. The purpose is specific and unambiguous.

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: to get schemas for endpoints. It doesn't explicitly state when to use alternatives like list_endpoints or search_endpoints, but the parameter descriptions for 'slug' mention using those to find slugs, which gives some guidance. However, no explicit exclusions or alternative selection criteria beyond that.

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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