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Dorit

by bot.dorit.mcp
OverviewDiscussionsAdmin

Neighborhood context

neighborhood_context
Read-onlyIdempotent

What a neighborhood is like: Dorit's editorial description, typical asking rents by room count, live listing count, rail and light-rail stations nearby, and bordering neighborhoods — plus a dorit.bot browse link. Without a neighborhood, returns the city's description and its neighborhood registry. Use estimate_price for a priced subject.

Input Schema

NameRequiredDescriptionDefault
cityYesCity by name or slug.
contextYesExplain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
languageNoLanguage of the description; defaults to the connected user's language.
llm_modelYesThe exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
neighborhoodNoNeighborhood by name or slug. Omit for the city itself: its description and neighborhood list.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / context
      Added value: +{
+  "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\"",
+  "type": "string"
+}
    • addedInput schema / properties / llm_model
      Added value: +{
+  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[
-  "city"
-]New value: +[
+  "city",
+  "context",
+  "llm_model"
+]
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already establish readOnly, idempotent, non-destructive, closed-world behavior. The description adds meaningful context beyond that: the dual output mode with and without a neighborhood, and the contents returned. It does not mention auth or rate limits, but the fallback disclosure is a useful behavioral detail.

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 dense, front-loaded sentences with no waste. The main output contents come first, followed by the fallback mode and the pricing alternative.

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?

With no output schema and five parameters, the description does well by enumerating the return contents and both operating modes. It could be slightly more complete about the response shape or how the city fallback differs structurally, but an agent has enough to call it correctly.

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%, so the schema already documents all five parameters, including the optional neighborhood and its omission semantics. The description reinforces the neighborhood omission behavior but adds no new syntax or constraint beyond what the schema provides.

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?

The description names the exact resource and enumerates its contents (editorial description, rents, listing count, stations, borders, browse link), and explicitly contrasts with estimate_price for priced subjects. An agent can tell precisely what this tool returns and which sibling handles pricing.

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?

It explicitly states the fallback behavior when neighborhood is omitted and points to estimate_price as the alternative for pricing. However, it does not clarify when to use this versus find_neighborhoods_near or search_listings, leaving some sibling disambiguation to inference.

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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