Skip to main content
Glama

InfraNode

by dev.infranode

City Overview

get_city_overview
Read-onlyIdempotent

Get a ONE-CALL overview of everything InfraNode knows about a German city.

Start here for any city question. Returns: the city's base data, a CATALOG of all 81 available data types (weather, air quality, public transit, trains, traffic, charging, parking, solar, energy, demographics, taxes, accidents, tourism, heritage, trees, population density, playgrounds, post boxes and many more), each with its coverage status and the exact tool to call next (for most data types that is get_city_resource(slug, resource=<type>)), plus a small live highlights snapshot (current weather, air quality and train departures). Data types not yet covered for this city show where they ARE available so you can pivot. InfraNode keeps adding data and cities, so the catalog grows over time. Read-only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesCity identifier, e.g. 'berlin' or 'hamburg'. Resolved leniently: the German name with or without umlauts, any casing, a common English exonym or short form also works (München/munich/munchen -> muenchen, cologne -> koeln, frankfurt -> frankfurt-am-main). An unknown name returns 404 with a 'Meintest du ...?' suggestion. list_cities gives the canonical slugs.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYes
metaYes

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already convey readOnly, openWorld, idempotent, and non-destructive behavior. The description adds valuable context not in annotations: the catalog grows over time, data types not covered show where they ARE available, and the tool returns a live highlights snapshot. The only slight gap is no mention of rate limits or potential cost, but the annotations cover core safety traits well.

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 well-structured with a clear opening imperative, a bullet-style enumeration of return values, and a forward-looking note about data growth. Every sentence adds value without redundancy. Front-loading is excellent.

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?

Despite having only one simple parameter, the tool returns a complex response (base data, catalog, live highlights). The description thoroughly explains what the agent can expect, including how to interpret coverage gaps and the catalog's dynamic nature. The return values are well-covered, making this complete for agent decision-making.

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% and the slug description is already rich. The description doesn't add to the slug's semantics directly, but it provides critical context about how the tool uses the parameter (to return the overview and catalog). Given full schema coverage, baseline is 3; the extra usage context lifts it to 4.

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 uses a strong verb-resource pair ('Get a ONE-CALL overview') and explicitly distinguishes this tool from siblings by stating it returns a catalog of all 81 data types and directs to the exact tool to call next (get_city_resource), making the purpose crystal clear and well-differentiated.

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?

The description commands 'Start here for any city question' and provides explicit guidance on what to do next (call get_city_resource for specific data types, and pivot to other cities when data is unavailable). This gives excellent when-to-use and next-step context.

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

All InfraNode toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.1/5.0
Disambiguation3/5

Most tools have distinct purposes, but there is potential confusion between 'get_city_resource' and specific resource accessors like 'weather', 'air_quality', 'transit_departures', 'station_board_arrivals', and 'station_board_departures'. The descriptions do clarify that some specific tools exist for convenience or source-specific details, but the boundary isn't always sharp. Also, 'compare' could overlap with using 'get_city_resource' repeatedly. An agent might hesitate between using the generic resource accessor and the named tool.

Naming Consistency4/5

The naming convention is mostly consistent: verbs like 'get', 'list', 'compare', and 'pois' (abbreviation) are used. The pattern is generally 'verb_noun' (e.g., 'get_city', 'list_cities', 'station_board_arrivals'). Minor deviations include 'pois' being an acronym rather than a full verb phrase, and 'air_quality' vs 'weather' implying a noun rather than an action. But overall it's predictable and readable.

Tool Count5/5

12 tools is a well-scoped count for a city data platform. The function set covers discovery (list_cities, sources), overview (get_city_overview), base data (get_city), specific data types (weather, air_quality, transit_departures, station_board_*, pois), a generic accessor (get_city_resource), and a comparison tool (compare). Each tool feels necessary and the set is not overwhelming.

Completeness4/5

The tool surface is quite comprehensive for a read-only city information server. It provides discovery (list_cities, get_city_overview), base data, and access to 81 data types via get_city_resource. The named tools cover the most common queries (weather, air quality, transit). A minor gap is the lack of a tool to aggregate or search across cities (though 'compare' helps). Editing or write operations are not expected here, but for read-only, it's nearly complete.

Resources