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sepehr071

snapp-mcp

by sepehr071
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Find location

find_location
Read-onlyIdempotent

Convert a place name or address into latitude and longitude before food or market searches. Provide near coordinates to bias results for cities outside Tehran.

Instructions

Turn a place name or address into latitude/longitude.

Use first when the user gives an address; food_* and market_* tools need lat/long. Biased toward near_lat/near_long (default Tehran): pass a city center for other cities.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax places to return.
queryYesPlace, street or landmark, e.g. 'میدان ونک'.
near_latNoBias latitude (e.g. a city center from list_cities).
near_longNoBias longitude.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed9 schema fields changedv0.2.0
    • removedInput schema / properties / limit / title
      Removed value: -"Limit"
    • changedInput schema / properties / near_lat / description
      Previous value: -"Bias results toward this latitude (e.g. a city center from list_cities)."New value: +"Bias latitude (e.g. a city center from list_cities)."
    • removedInput schema / properties / near_lat / title
      Removed value: -"Near Lat"
    • changedInput schema / properties / near_long / description
      Previous value: -"Bias results toward this longitude."New value: +"Bias longitude."
    • removedInput schema / properties / near_long / title
      Removed value: -"Near Long"
    • changedInput schema / properties / query / description
      Previous value: -"Place, street, landmark or neighbourhood, Persian works best, e.g. 'میدان ونک'."New value: +"Place, street or landmark, e.g. 'میدان ونک'."
    • removedInput schema / properties / query / title
      Removed value: -"Query"
    • removedInput schema / title
      Removed value: -"find_locationArguments"
    • changedOutput schema / (root)
      Previous value: -{
-  "additionalProperties": true,
-  "title": "find_locationDictOutput",
-  "type": "object"
-}New value: +null
  2. First observedv0.1.1

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true and destructiveHint=false, so safety is covered. The description adds real behavioral context beyond that: results are geographically biased toward near_lat/near_long and default to Tehran, which materially affects output.

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?

Three lines, purpose front-loaded, followed by usage and the bias caveat. No filler sentences and every clause carries actionable information.

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 annotations and full schema coverage in place, the description covers purpose, workflow position, and the bias quirk. It does not describe the shape of the returned list (multiple place results given the 'limit' param) and no output schema exists to compensate, leaving a minor gap.

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 description coverage is 100%, so baseline is 3. The description earns an extra point by explaining the intent of near_lat/near_long ('Biased toward... pass a city center for other cities'), clarifying a parameter whose schema text alone is terse.

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?

States a specific verb and transformation: 'Turn a place name or address into latitude/longitude.' This clearly distinguishes it from the sibling reverse_geocode, which does the opposite direction. An agent can select it without ambiguity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit sequencing ('Use first when the user gives an address') and names the downstream consumers ('food_* and market_* tools need lat/long'). It also documents the default bias location and how to override it for other cities, which is the key routing decision an agent must make.

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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