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Glama

divar_list_cities

Find Divar city IDs by entering a Persian city name, or list all available cities to enable localized classifieds searches.

Instructions

Look up divar.ir city ids by Persian name (or list them all).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoPersian city name (e.g. "مشهد").

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It states the tool can either look up a specific city or list all cities, which is useful. However, it doesn't disclose details like whether the query is case-sensitive, whether it returns partial matches, what the output format looks like, or whether it makes a network request. For a simple lookup tool, this is adequate but not rich.

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?

The description is a single sentence that is concise and front-loaded. It states the primary action ('look up divar.ir city ids'), the input ('by Persian name'), and the alternative behavior ('or list them all') without any wasted words. Every part earns its place.

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 simple lookup tool with one optional parameter and no output schema, the description is nearly complete. It tells the agent what the tool does and how to use it. The only missing context is the exact output format (e.g., a list of {id, name} pairs), but since there is no output schema, a brief note on the return shape would have been helpful. Still, the tool is simple enough that an agent can likely infer the output.

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%: the only parameter 'query' is described as a Persian city name with an example. The description adds the behavior that omitting the query lists all cities, which is valuable and not in the schema. However, it doesn't add more detail about the parameter's format (e.g., normalization, exact match vs. fuzzy). Baseline 3 is appropriate since the schema already covers the parameter 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?

The description clearly states the tool's function: looking up divar.ir city IDs by Persian name, with the option to list all cities. It uses a specific verb ('look up') and resource ('divar.ir city ids'), and the optional listing behavior is explicitly mentioned. This distinguishes it from sibling tools like divar_search or divar_list_categories, which focus on other aspects of the Divar API.

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?

The description implies when to use this tool: when you need to map a Persian city name to its ID, or when you need the full list of city IDs. It doesn't explicitly state when not to use it or name alternatives, but the context is clear enough for an agent to select it over siblings like divar_search or divar_list_categories. A small gap is the lack of explicit exclusion, but the purpose is so specific that it's nearly self-evident.

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