GeoNatureAgent (GNA): A Framework and Benchmark for Pre-Production Evaluation of Tool-Using Agents on Geospatial and Environmental Tasks

Gabriel Diaz-Ireland; Diego Prieto-Herráez; Mario García Peces; Javier Velázquez; Benjamin Zaitchik; Devika Jain

cs.AIOct 6, 2026

GeoNatureAgent (GNA): A Framework and Benchmark for Pre-Production Evaluation of Tool-Using Agents on Geospatial and Environmental Tasks

Authors: Gabriel Diaz-Ireland, Diego Prieto-Herráez, Mario García Peces, Javier Velázquez, Benjamin Zaitchik, Devika Jain

Organizations: Universidad Católica de Ávila (UCAV) Ávila, Spain · Independent Researcher Madrid, Spain · Department of Earth and Planetary Sciences, Johns Hopkins University Baltimore, MD, USA · Center for Geographic Analysis, Harvard University Cambridge, MA, USA

Abstract

Before tool-using LLM agents are deployed in environmental and geospatial workflows, teams need evidence that an agent reliably selects the right operations against real APIs. We introduce GeoNatureAgent (GNA), a framework for pre-production evaluation of tool-using agents: a fixed sixteen-tool geospatial interface published as a Model Context Protocol (MCP) server, so the agent under test is the only variable, scored against an identical tool layer, task suite, and deterministic scorer. Its flagship instance is a 103-task benchmark (a 93-task main suite across 18 categories plus a ten-task comparison expansion) evaluated against an open, self-hostable geospatial API serving three environmental indicators across Spain and Portugal. We evaluate nine LLMs under three temperature-1.0 seeds, reporting capability and per-case cost as orthogonal axes. (1) Claude Sonnet 4 achieves the highest capability (61.7% +/- 0.7% on all 103 tasks; 60.8% on the main suite), followed closely by DeepSeek V3.2 (57.9%), while no other model exceeds 53%; (2) the cost-accuracy Pareto frontier is mostly open-weight, with DeepSeek V3.2 offering 93% of Claude's capability at 11.3x lower list-price cost; (3) under strict all-checks scoring the best model sits 24-36 points below the 85-97% reported on general-purpose GIS benchmarks, whereas per-check partial credit for the top four models (86-90%) is comparable, so much of that gap reflects scoring strictness rather than task difficulty alone. The MCP server, evaluation harness, benchmark, and API are publicly available; swapping the tool executors and task suite instantiates an equivalent benchmark for any geospatial domain.

Figures & tables

	GeoBenchX	GeoNatureAgent Benchmark
Tasks	202	103
Categories	5	18
Tools	24 primitives	12 principal ops
Tool abstraction	Low-level	High-level
Data source	Local files	Cloud API (COG + JSON)
Domain	General GIS	Environmental

Table 1 . Comparison of structured tool-calling geospatial benchmarks. Accuracies are not same-protocol: GeoBenchX uses an LLM judge, GNA strict all-checks scoring (best per-check partial credit:

89.6\%

Figure 1 . GNA system architecture. The LLM agent interacts with a production API serving COG data; the eval harness logs all tool calls and scores responses. NL denotes natural language.

Tool	Purpose
lookup_province	Resolve province $\to$ boundary geometry
lookup_municipality	Resolve municipality (+ province hint)
analyze_area	Zonal statistics for indicator in AOI
analyze_multi_layer	Multi-indicator analysis in single call
compare_areas	Compare two areas on the same indicator
find_top_n	Rank provinces by indicator value

Table 2 . Principal agent tools. The first eight are domain-specific; the last four are adapted from GeoBenchX ( Krechetova and Kochedykov, 2025 ) . The agent additionally exposes four auxiliary tools not listed here (layer discovery and erosion statistics).

Category	Tasks	Tests
Tool selection	21	Correct tool choice
Cross-indicator	8	CO 2 $\times$ erosion $\times$ land cover synthesis
Deep dive	6	Multi-tool environmental profiling
Interpretation	7	Reasoning over analysis results
Error handling	6	Non-existent entities, invalid indicators
Habitat analysis	7	BigEarthNet V2 land cover (Portugal)

Table 3 . GeoNatureAgent Benchmark main-suite (v5) task categories (

93

of the

103

tasks, 18 categories).

Check	Pass condition
expected_tools	Every expected tool was called (recall = 1.0). Extra tools do not cause failure. a
expected_actions	Every expected UI action was generated (recall = 1.0).
must_contain	Each required keyword appears in the answer (case-insensitive substring).
must_not_contain	Forbidden keyword does not appear in the answer.
numeric_accuracy	For each ground-truth entry, the label is found in the answer and the nearest percentage value is within tolerance. b
chart_generated	At least one chart URL was produced (only checked when generate_chart is an expected tool).

Table 4 . Scoring checks. Binary capability pass requires every applicable check except max_cost_usd to pass.

Model	Provider	Params	Access
DeepSeek V3.2	DeepSeek AI	671B MoE	Vertex MaaS
GLM-5	Zhipu AI	—	Vertex MaaS
Gemini 2.5 Pro	Google	—	Vertex native
Claude Sonnet 4	Anthropic	—	Anthropic API
GPT-4o	OpenAI	—	OpenRouter
Qwen3-235B	Alibaba	235B MoE	Vertex MaaS

Table 5 . Models evaluated. Six via Vertex AI (five MaaS plus Gemini native), two via OpenRouter, Claude via the Anthropic Messages API.

#	Model	All (103)	Main (93)	Cost	$/case
1	Claude Sonnet 4 $\dagger$	$61.7\pm 0.7$	$60.8\pm 0.8$	$\$ 11.84$	$0.127$
2	DeepSeek V3.2	$57.9\pm 3.9$	$56.3\pm 3.1$	$\$ 1.05$	$0.011$
3	GLM-5	$52.4\pm 2.6$	$50.2\pm 2.2$	$\$ 3.58$	$0.038$
4	Gemini 2.5 Pro	$49.2\pm 3.9$	$48.0\pm 3.3$	$\$ 4.87$	$0.052$
5	Qwen3-235B	$44.3\pm 5.0$	$41.2\pm 4.3$	$\$ 0.89$	$0.010$
6	GPT-4o	$43.7\pm 2.6$	$41.6\pm 2.7$	$\$ 6.49$	$0.070$

Table 6 . Capability leaderboard (accuracy in %, combined rank): nine models on the combined 103-task suite and the 93-task v5 main suite (Section 6.5 ). Per-seed mean

\pm

one sd, decoupled from cost (Section 5 ); cost is the mean per-seed main-suite run total, $/case the per-call mean.

Figure 2 . Cost-accuracy trade-off (bubble size

\propto

total tokens). The Pareto frontier (dashed) runs Scout

\to

Qwen3-235B

\to

DeepSeek V3.2

\to

Claude Sonnet 4; three of the four are open-weight.

Model	Accuracy	gCO 2 /case
Claude Sonnet 4	$60.8\%$	$6.44$
DeepSeek V3.2	$56.3\%$	$6.42$
GLM-5	$50.2\%$	$5.61$
Gemini 2.5 Pro	$48.0\%$	$4.92$
GPT-4o	$41.6\%$	$3.56$
Qwen3-235B	$41.2\%$	$4.99$

Table 7 . Estimated energy and carbon per case (tokens

\times\,0.40

Wh/1k;

400

gCO 2 /kWh). Order-of-magnitude estimate; footprint is token-driven.

Failure mode	% of failures
Tool missing (expected tool not called)	$68.0$
Keyword missing (answer lacks required term)	$19.3$
Wrong data (incorrect value)	$9.7$
Rounds exceeded	$1.3$
Forbidden keyword	$1.3$
Chart missing	$0.3$

Table 8 . Failure-mode distribution over all

1{,}429

failed runs (nine models).

Model	Cap.	Raw	Comp. cap/raw	103-task comb.
Qwen3-235B	$73\%$	$37\%$	$78/22\%$	$44.3\%$
GLM-5	$73\%$	$37\%$	$83/33\%$	$52.4\%$
DeepSeek V3.2	$73\%$	$27\%$	$83/17\%$	$57.9\%$
Claude Sonnet 4	$73\%$	$20\%$	$83/0\%$	$61.7\%$
GPT-4o	$63\%$	$50\%$	$72/50\%$	$43.7\%$
Gemini 2.5 Pro	$60\%$	$60\%$	$67/67\%$	$49.2\%$

Table 9 . v6 expansion (10 comparison-heavy tasks, 3 seeds) and the combined 103-task suite. Cap. is capability scoring (budget/round gates excused; the v5 leaderboard excuses cost only); raw is strict pass. 103-task comb. is capability accuracy over the full v5+v6 suite (per-seed mean), the inclusive headline metric for this paper.

Figure 3 . v6 comparison tasks. Left: pooled comparison accuracy vs. value gap, raw and capability scoring—flat in the gap, and the

45

pp control is no easier, so difficulty is not near-equal discrimination. Right: per-model comparison accuracy, capability vs. raw; the bar gap is round/budget exhaustion while composing repeated analyze_area calls.

Figure 4 . Accuracy by category across the nine models.

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GeoNatureAgent Benchmark: Benchmarking LLM Agents for Environmental Geospatial Analysis Across Frontier and Open-Weight Foundation Models

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Open paper AskPDF arXiv

Environmental scientists spend disproportionate effort on data wrangling rather than analysis. New AI agents can be a helpful tool, but no benchmark exists to evaluate AI agents that automate environmental geospatial workflows through structured tool calling against real APIs. We introduce the GeoNatureAgent Benchmark, the first benchmark for environmental analysis agents that operate via structured tool calls to a production-style geospatial API. The benchmark comprises 93 tasks across 18 categories. Tasks are evaluated against an open, self-hostable geospatial API that serves three environmental indicators across Spain and Portugal via sixteen tools. We evaluate nine frontier and open-weight LLMs, reporting capability and per-case cost as orthogonal axes. Results manifest that (1) Claude Sonnet 4 achieves the highest capability at 60.8% +/- 0.8%, followed closely by DeepSeek V3.2 at 56.3% +/- 3.1%, while no other model exceeds 51%; (2) the cost-accuracy Pareto frontier is occupied mostly by open-weight models, with DeepSeek V3.2 offering 93% of Claude's capability at 11.6x lower cost; and (3) structured tool calling against a real API provides a more discriminative measure of real-world agent capability, with mean accuracies 25-35 percentage points below those reported on general-purpose GIS benchmarks.

Abstract

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