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perception_classify

Run Prithvi-EO-2.0-300M-TL-Sen1Floods11 flood classification on a Sentinel-2 tile previously fetched by perception_fetch_tile. Sends the 6-band chip to a RunPod endpoint and returns: dominant_class, flood_pixel_pct, confidence, class_counts, and the full perception_chain. The perception chain is written to Spatial Memory and a signed audit breadcrumb is dropped to the agent trail.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYesClassification task. Currently only "flood" is supported.
h3_cellNoOverride H3 cell. Defaults to the cell from the original fetch.
tile_idYestile_id from perception_fetch_tile result (must be in session cache).
write_to_spatial_memoryNoWrite perception chain to geiant_geometry_state. Default: true.

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 burden of disclosing side effects, and it does: it writes the perception chain to Spatial Memory and drops a signed audit breadcrumb. It also reveals that it sends data to a RunPod endpoint and lists the returned fields. However, it omits details about failure behavior, permissions, or whether the memory write can be disabled beyond the parameter default.

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?

Three sentences, front-loaded with the main action, and every sentence earns its place: what it does, what it returns, and what side effects it has. No filler or redundant restatement of the name.

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?

The description covers the essential prerequisites, processing location (RunPod), return fields, and side effects. Since there is no output schema, it reasonably enumerates the main return values, though it leaves the structure of 'perception_chain' unspecified and does not discuss error conditions. Still, it provides a solid operational picture for a moderately complex tool.

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?

Schema description coverage is 100%, so the baseline is 3. The description adds no extra parameter-level meaning beyond the schema, but it reinforces that tile_id must come from a prior fetch and that the chain is written to memory. No compensation needed since the schema already documents each parameter's purpose.

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 opens with a specific, actionable verb: "Run Prithvi-EO-2.0-300M-TL-Sen1Floods11 flood classification on a Sentinel-2 tile previously fetched by perception_fetch_tile." It clearly identifies the resource (Sentinel-2 tile), the operation (classification), and distinguishes it from siblings like perception_fetch_tile and perception_embed.

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 tool explicitly states it operates on a tile 'previously fetched by perception_fetch_tile', which establishes a required sequencing precondition. It does not explicitly enumerate when to avoid using it or name alternative tools for other tasks, but the prerequisite and domain are clearly implied.

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.