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Glama
HackerFund

Glendale GIS MCP Server

by HackerFund

nearest_resources

Read-onlyIdempotent

Find the nearest fire stations, police stations, hospitals, schools, libraries, parks, and bus stops for any Glendale address or coordinates, listing names, addresses, and straight-line distances.

Instructions

Nearest community resources to a location: fire stations, police station, hospitals, schools, libraries, parks and Beeline bus stops, with name, address and distance. Distances are straight-line meters, not travel distance or time, and only resources inside Glendale are included (neighboring cities' facilities may be closer). For other filters (e.g. only high schools) use query_dataset.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindsNoWhich kinds to include; default all
limitNoResults per kind, 1-25
locationYesWhere to look: {"address": "613 E Broadway"} for a street address in Glendale, or {"lat": 34.1466, "lon": -118.2483}. Coordinates skip geocoding.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
notesNo
groupsYes
locationYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds critical behavioral details: distances are straight-line meters (not travel distance/time) and only resources inside Glendale are included, even if neighboring cities are closer. This goes beyond annotations and is essential for correct interpretation.

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 sentences, front-loaded with the primary purpose, followed by essential caveats and a routing instruction. No wasted words; every sentence earns its place.

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?

Given the tool's complexity (multiple resource kinds, location input, limit, and output fields), the description covers the key nuances: Glendale-only scope, straight-line distances, and a clear pointer to an alternative for different filters. The agent has all necessary context to invoke the tool correctly.

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 all parameters are well-documented in the schema (e.g., location accepts address or lat/lon, kinds has enum, limit has default and range). The description does not add new parameter semantics beyond what the schema already provides. Baseline of 3 applies because the schema carries the burden.

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 states a clear verb ('Nearest') and resource ('community resources') with an explicit list of types and output fields. It also distinguishes itself from query_dataset by name, making it easy for an agent to select the right tool.

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?

It explicitly instructs when to use an alternative: 'For other filters (e.g. only high schools) use query_dataset.' It also clarifies the scope (only Glendale) and the distance metric, setting clear expectations for usage.

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