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Glama

Adjacent neighbourhoods

dv_neighbours
Read-onlyIdempotent

Find adjacent neighbourhoods to a district to widen a search area. Combines Divar's adjacency list with nearest districts by centroid distance, then pass the ids to dv_search.

Instructions

Neighbourhoods adjacent to a district, to widen a neighbourhood search to the surrounding area.

districts is Divar's own adjacency list, which is short for some areas (Vanak lists one). nearest adds the closest districts of city by centroid distance (within 3 km, up to 6) so a widened search is not too narrow. Pass the ids (or names) as districts to dv_search.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityNoCity name or id; several comma-separated, e.g. 'تهران', 'mashhad', 'تهران,کرج'.تهران
district_idYesDistrict id from dv_districts, e.g. 655.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, openWorld), so the bar is lower. The description still adds real behavioral context: `districts` is Divar's short adjacency list (Vanak lists one) and `nearest` supplements it with centroid-distance districts within 3 km, up to 6, which explains why results vary by area.

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 purpose is front-loaded in the first sentence, followed by the mechanics of `districts` and `nearest`, and it ends with the actionable handoff to dv_search. It is well ordered, though the parenthetical detail (within 3 km, up to 6, Vanak lists one) is somewhat granular for a definition.

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 read-only lookup with no output schema, the description does enough: it names the two result sets (districts, nearest) the agent will get back and what to do with them. It is not exhaustive about the exact return shape, but the essential call-and-consume loop is covered.

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 both parameters are already documented in the schema (district_id from dv_districts, city name/id with comma-separated examples). The description does not meaningfully extend parameter semantics beyond noting the outputs should be fed back as `districts` to dv_search, so the baseline 3 is appropriate.

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 states a specific resource (adjacent neighbourhoods around a district) and the intent (widening a neighbourhood search to the surrounding area). It does not explicitly differentiate itself from the sibling dv_nearby, which is the most likely confusable tool, so it falls just short of a 5.

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?

It implies usage by describing the widening scenario and by saying to pass the ids as `districts` to dv_search, which gives the agent a downstream workflow. However, it never states when NOT to use it or how it differs from dv_nearby/dv_districts, leaving the selection decision partly to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.