Hotyata

report_issue

Report an issue with data accuracy, stale or off-market listings, incorrect coordinates, spam, or tool usability limitations. AI agents should call this when finding inaccuracies or when human users report off-market listings.

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": {
      "entity_type": "<entity_type>",
      "issue_category": "<issue_category>",
      "explanation": "<explanation>"
    },
    "name": "report_issue"
  }
}

Input Schema

Name	Required	Description
`entity_id`	No	ID or URL of the property, item, or complex (if applicable)
`entity_type`	Yes	What entity has the issue
`explanation`	Yes	Clear explanation of what is incorrect and what the correct information should be
`agent_client`	No	Name of the AI assistant/agent (e.g. 'ChatGPT-4o', 'Claude-3.5', 'Cursor')
`issue_category`	Yes	Category of the reported issue
`source_context`	No	How this issue was discovered (e.g. 'User called landlord and confirmed rented', 'Coordinates point to the sea')

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 6, 2026

TDQS

A4/5.0

Behavior3/5

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

No annotations are supplied, so the description carries the full behavioral burden. It makes clear this is a submission action rather than a lookup, but says nothing about what happens after submission (ticket creation, review SLA, deduplication), whether reporting is anonymous, or whether the caller gets any confirmation back.

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?

Two sentences, zero filler, with the scope enumeration front-loaded and the trigger condition second. Nothing is redundant with the tool name or schema.

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?

For a six-parameter feedback tool with full schema coverage and no output schema, the description covers purpose and triggers adequately. The remaining gap is downstream behavior — what the report produces and whether the agent should tell the user anything afterwards.

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% and both enums are documented inline, so the schema already does the parameter work. The description adds only loose topical hints ('incorrect coordinates', 'off-market listings') that map onto issue_category values but supplies no guidance on filling explanation or source_context well.

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 (report) and resource (issue) and enumerates the concrete problem classes it covers: data accuracy, stale/off-market listings, incorrect coordinates, spam, and tool usability limits. An agent can immediately tell this is the feedback channel, distinct from the read-only lookup and search siblings.

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?

Gives explicit triggering conditions — when the agent itself finds inaccuracies, or when a human user reports an off-market listing. It stops short of saying when NOT to use it (e.g. do not file a report for a simple empty search result) or naming any alternative escalation path.

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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