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sheypoor-mcp

sheypoor-mcp

by sepehr071
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Prices per neighbourhood

sp_area_prices
Read-onlyIdempotent

Compare median asking prices across city neighbourhoods to locate lower-cost rental or sale areas using price per square metre, rent, and deposit data.

Instructions

Compare asking prices between neighbourhoods of a city: median price, price per square metre, rent and deposit.

Use it for "where is renting cheapest in Tehran". It samples each area's newest 24 ads, so medians are rough. Sale: median_price, median_per_m2; rentals: median_deposit, median_rent. Toman. Pick ids with sp_filters(category, city, area=...).

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": {
      "city": "<city>",
      "category": "<category>"
    },
    "name": "sp_area_prices"
  }
}

Input Schema

NameRequiredDescriptionDefault
topNoBusiest neighbourhoods when none are given.
cityYesCity slug, e.g. tehran (not 'iran').
categoryYesCategory slug, e.g. house-apartment-for-rent, car.
neighbourhoodsNoNeighbourhood ids from sp_filters(area=...). Omit for the busiest.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.1/5.0
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, open-world), so the description's job is to add data-quality context — and it does: it samples each area's newest 24 ads and warns medians are rough, which is a material caveat an agent cannot infer elsewhere. It also discloses the return fields per category and the currency (Toman), but says nothing about failure modes when an area has too few ads.

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?

Purpose is front-loaded in the first clause, followed by a use-case example, a caveat, output field names, and the id-sourcing hint — each sentence carries distinct information and nothing is repeated. The compressed, backtick-heavy phrasing is slightly telegraphic but still parseable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description takes on the burden of describing return values, and it does so by enumerating the sale and rental metric fields plus the unit. Combined with the sampling caveat, default behavior, and id workflow, an agent has everything needed to call this correctly without opening other tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3; the description adds genuine meaning on top by explaining where neighbourhood ids come from (sp_filters with area=), that omitting them yields the busiest neighbourhoods, and how the returned metric names differ for sale vs rental categories. This is more than a restatement of the schema.

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 names a specific verb and scope: 'Compare asking prices between neighbourhoods of a city', plus the exact metrics returned (median price, price per m2, rent, deposit). That cleanly separates it from city-wide sp_price_summary and from sp_car_prices, but neither sibling is named, so the differentiation is inferred rather than stated.

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 gives a concrete intended question ('where is renting cheapest in Tehran') and a prerequisite workflow ('Pick ids with sp_filters(category, city, area=...)'), which tells an agent both when to reach for it and how to prepare inputs. It stops short of explicit exclusions — e.g. it never says to use sp_price_summary when only one area or the whole city is needed.

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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