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Glama

ground-motion

by com.sibfly

check_ground_motion

Read-only

Measured ground motion (sinking/uplift) for a US address or lat/lon, in mm/year AND in/year, from NASA OPERA Sentinel-1 InSAR. Costs $0.40 from your SibFly credits; out-of-coverage / too-stale / too-low-confidence / fuzzy-geocode return FREE. Use the gate params to avoid paying for data you'd reject. dry_run=true is a FREE coverage+cost preview.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNo
lonNo
sinceNoYYYY-MM-DD; if no new data since this date, return free
addressNoUS street address
compactNolean payload (fewer tokens)
dry_runNoFREE preview: coverage + would_cost + data age, no rate, no charge
explainNoadd ground_brief: ~600 tokens of plain-English narration + FAQ (engine GL52 free tier / CL46 paying). Adds ~10s latency. Only set when a human will read the output; use ask_inspector for follow-up Q&A.
max_age_daysNoif data older than this, return free instead of billing
min_confidenceNo0-1; below this, return free instead of billing
require_significantNoif the rate would be no_clear_trend (within its error bar), return FREE instead of billing — never pay for statistical noise

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
engineNonarration engine: GL52 (free tier) or CL46 (paying)
statusNo
cost_usdNo
confidenceNo
ground_briefNoonly when explain=true
data_age_daysNo
near_thresholdNo
assessment_codeNo
credits_remaining_usdNo
velocity_vertical_in_yrNo
velocity_vertical_mm_yrNo
velocity_uncertainty_mm_yrNo

TDQS

A5/5.0
Behavior5/5

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

The description discloses cost ($0.40), free return conditions (out-of-coverage, too-stale, too-low-confidence, fuzzy-geocode, dry_run, require_significant), and that dry_run is a free preview. This adds context beyond the readOnlyHint annotation, which already marks it as read-only.

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 brief yet comprehensive, starting with the core function, then cost, free conditions, and gate params. Every sentence adds value without redundancy. It is front-loaded with the most important information.

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 has 10 parameters and an output schema exists, the description covers all critical aspects: input options, cost, free scenarios, gate logic, explanation feature, and references sibling tools (ask_inspector). It provides sufficient detail for correct selection and invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description explains the purpose of key parameters (dry_run for free preview, explain for plain-English narration, require_significant to avoid paying for noise) and adds meaning to schema-covered params by describing their behavior (e.g., since: return free if no new data; max_age_days: return free if data older).

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 measures ground motion (sinking/uplift) for US addresses or lat/lon in mm/year and in/year from NASA OPERA Sentinel-1 InSAR. It distinguishes itself from siblings like get_coverage and get_motion_history by specifying the source and output units.

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 explicitly advises using gate params (dry_run, max_age_days, min_confidence, require_significant) to avoid paying for unwanted data. It contrasts explain with ask_inspector for follow-up Q&A and notes when to use explain (only when a human reads the output).

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

All ground-motion toolsInspect
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TDQS

A4.2/5.0
Disambiguation5/5

Each tool targets a distinct function: account management, ground motion queries, batch processing, history, and Q&A. No two tools serve overlapping purposes, and descriptions clearly differentiate them.

Naming Consistency4/5

Most tools use a 'verb_noun' pattern with verbs like get, register, buy, check, ask. However, 'check' is used for two tools (check_ground_motion, check_portfolio) while similar retrieval tools use 'get' (get_motion_history), creating a slight inconsistency in verb choice.

Tool Count5/5

With 9 tools, the server covers account setup, credit management, ground motion queries (single, batch, history), and follow-up Q&A—a well-scoped set that earns each tool's place.

Completeness5/5

The tool surface covers the full lifecycle: account creation (register_agent), credit purchasing (buy_credits), balance/usage (get_account, get_usage), coverage checks (get_coverage), ground motion queries (check_ground_motion, check_portfolio), motion history (get_motion_history), and post-purchase Q&A (ask_inspector). No obvious gaps.

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