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Glama

Geoapify

by io.github.pipeworx-io

Reverse Geocode

reverse_geocode
Read-onlyIdempotent

Reverse geocoding: convert latitude/longitude coordinates into the nearest street address. Returns formatted address, country, city, street, house number, and postcode. Example: reverse_geocode({ lat: 48.8584, lon: 2.2945 })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latYesLatitude in decimal degrees, e.g. 48.8584
lonYesLongitude in decimal degrees, e.g. 2.2945
_apiKeyNoOptional — your own Geoapify API key for higher limits; omit to use the shared Pipeworx key.

TDQS

A4.5/5.0
Behavior4/5

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

Annotations declare readOnlyHint, idempotentHint, etc. The description adds behavioral context: optional API key, return format (address components). No contradictions.

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?

Two sentences, front-loaded with action, includes an example, no extraneous 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?

No output schema, but description enumerates return fields. Annotations provide safety profile. Tool is simple; description is sufficiently complete for selection and invocation.

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 covers 100% of parameters with descriptions. The description adds value with an example and lists return fields, which are not in the input schema, and mentions optional _apiKey parameter.

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 purpose: converting lat/lon to nearest street address, lists return fields, and provides a concrete example. It distinguishes itself from sibling `geocode` (forward geocoding) implicitly.

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 (reverse geocoding) but does not explicitly mention when not to or suggest alternatives. The sibling list includes geocode, making distinction clear.

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

All Geoapify toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Multiple tools have overlapping purposes, such as ask_pipeworx, ask_pipeworx_grounded, entity_profile, compare_entities, and validate_claim, all retrieving entity data. Agents may struggle to choose correctly between them. Likewise, bet_research, polymarket_edges, and polymarket_arbitrage cover similar prediction market territory.

Naming Consistency3/5

All tool names use underscores, but the verb/noun order varies: compare_entities (verb_noun), entity_profile (noun_noun), scan_competitor_ai_presence (verb_noun), bet_research (noun_verb). Some names are overly long (scan_competitor_ai_presence). The pattern is readable but not fully consistent.

Tool Count3/5

With 31 tools, the count is high but not extreme. However, the set covers geospatial, data retrieval, prediction markets, npm scanning, and memory/feedback - too broad for a single server. Many tools feel added without clear justification, making the set feel bloated.

Completeness2/5

The geospatial subset is incomplete (missing elevation, distance matrix, isochrones). The data access tools overlap rather than form a complete API surface (e.g., no entity update/delete). Prediction market tools are numerous but redundant. The server tries to do too much and lacks depth in any area.

Resources