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Dorit

by bot.dorit.mcp
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Coverage

coverage
Read-onlyIdempotent

Which Israeli cities, regional groups and localities Dorit searches, with rent/sale availability and neighborhood counts; with a city, that city's neighborhood registry (ids, slugs, names in Hebrew, English and Russian, centroids) and its named areas. Call this first when unsure how a place is named.

Input Schema

NameRequiredDescriptionDefault
cityNoA city by name or slug: returns its full neighborhood registry and its named areas. Omit for the list of cities, regional groups and localities.
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."
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.

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"
+}
    • addedInput schema / required
      Added value: +[
+  "context",
+  "llm_model"
+]
  2. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds real context the annotations don't: this is a registry lookup whose result set varies entirely on whether a city is supplied, and that it is the naming authority for the domain.

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?

Two dense, front-loaded clauses with no filler, and the actionable routing hint comes last where it is likely to be read. The semicolon-chained sentence is heavy but every clause carries distinct content.

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, the description does the work of describing return values in detail (registry fields, multilingual names, centroids, availability flags), which is exactly what is needed. The one loose end is "Dorit", an unexplained product name that an agent has no way to interpret.

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%, so the city parameter's name-vs-slug and omit-for-list behavior is already documented structurally. The description restates that same split rather than adding new syntax, format or edge-case guidance, so the baseline 3 applies.

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 specific resource (Israeli cities, regional groups, localities and, with a city, its neighborhood registry) and enumerates the concrete payload: rent/sale availability, neighborhood counts, ids, slugs, multilingual names and centroids. It is impossible to confuse with get_listing or estimate_price.

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?

"Call this first when unsure how a place is named" gives an explicit triggering condition, and the city/no-city split is stated. It stops short of naming alternatives such as find_neighborhoods_near or neighborhood_context, so the agent must infer when to leave this tool.

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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