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perception_fetch_tile

Fetch the least-cloudy Sentinel-2 L2A tile covering a given H3 cell from Microsoft Planetary Computer. Returns signed COG band URLs for all 6 Prithvi/Clay spectral bands (B02 Blue, B03 Green, B04 Red, B8A NIR, B11 SWIR1, B12 SWIR2), plus tile metadata. The tile is cached in memory for subsequent perception_classify or perception_embed calls.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
h3_cellYesH3 cell ID at any resolution.
days_backNoDays to search back. Default: 30.
timestampNoISO 8601 datetime. Search back from this point. Defaults to now.
max_cloud_coverNoMax cloud cover %. Default: 20.

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses the source, the return content (signed COG band URLs for six bands plus metadata), and the in-memory caching behavior. It does not mention authentication, rate limits, or URL expiration, but the core behavioral traits are covered.

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 concise sentences. The first sentence states the action and subject; the second lists the return value and the caching side effect. Every clause earns its place, and key information is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Although there is no output schema, the description explicitly enumerates the six spectral bands and notes the inclusion of tile metadata, which covers return values. It also mentions the caching behavior that links to sibling tools. A small gap is the absence of failure/edge-case behavior, but for a fetch tool with well-documented parameters, this is reasonably complete.

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?

The input schema already provides 100% parameter coverage with descriptions for h3_cell, days_back, timestamp, and max_cloud_cover. The tool description adds context about the tile selection ('least-cloudy') and cache usage, but does not add new parameter-level semantics, so the baseline of 3 applies.

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 action ('Fetch'), the specific resource ('least-cloudy Sentinel-2 L2A tile covering a given H3 cell'), and the source ('Microsoft Planetary Computer'). It also distinguishes itself by enumerating the returned bands and noting the cache behavior for subsequent perception_classify or perception_embed calls.

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 this tool by noting the tile is cached for subsequent perception_classify or perception_embed calls, which gives contextual placement among siblings. It does not explicitly state when not to use it or name an alternative, but the guidance is sufficient for typical usage.

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

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TDQS

A3.7/5.0
Disambiguation5/5

Each tool has a distinct purpose within its domain: gns_* tools handle compliance reporting, trust scoring, epoch rolling, and chain verification, while perception_* tools handle tile fetching, classification, embedding, and weather queries. Even similar tools like gns_get_compliance_report and gns_get_trust_score are clearly differentiated by scope (full report vs quick score).

Naming Consistency3/5

The gns_* tools follow a consistent verb_noun pattern (get_compliance_report, get_trust_score, roll_epoch, verify_chain), but perception_* tools mix styles: perception_fetch_tile is verb_noun, perception_classify and perception_embed are just verbs, and perception_weather is a noun. The two prefixes (gns_ vs perception_) also introduce a split, though each group is internally readable.

Tool Count5/5

Eight tools is well-scoped for a server that combines two related functions: AI-powered earth observation and cryptographic compliance auditing. Each tool serves a distinct role and there are no redundant utilities, making the count feel intentional and complete.

Completeness4/5

The core workflows are covered: fetch a tile, classify it, embed it (though not yet implemented), and weather queries for context, with a full compliance trail via breadcrumbs, epochs, and verification. The only notable gap is that perception_embed is explicitly marked as not implemented, leaving a placeholder in the tool surface, but the rest of the pipeline is functional.