AcreLens

analyze_land

Generates a comprehensive land analysis report for a US property through one of four analytical lenses: off_grid, rural_residential, recreational, or investment. Call this when the user asks for a full analysis of a specific property. If the user's intent is unclear, ask which mode to use before calling. Returns a report ID and poll URL — the final structured report (scores, confidence ratings, narrative summary, source citations) is delivered asynchronously via polling or webhook. Consumes one analysis credit from your AcreLens account.

Input Schema

TableJSON Schema

Name	Required	Description
`lat`	No	Latitude (skip geocoding if provided).
`lng`	No	Longitude (skip geocoding if provided).
`mode`	Yes	Analysis lens: off_grid, rural_residential, recreational, or investment.
`state`	Yes	2-letter US state code (e.g. "NM").
`county`	No	County name (recommended for better regulation research).
`acreage`	No	Total acreage of the parcel.
`address`	Yes	Full street address of the US property (e.g. "123 Cabin Rd, Taos, NM").

Output Schema

TableJSON Schema

Name	Required	Description
`mode`	Yes	The analysis lens that was applied (echoed from the request).
`status`	Yes	Report status. Initially "authorized" or "processing"; transitions to "completed" or "failed" once analysis finishes.
`poll_url`	Yes	Absolute URL to GET the report. Returns 202 while processing, 200 with full body once completed.
`report_id`	Yes	Unique ID for the report. Use this with the poll URL to retrieve the final structured report.
`estimated_completion_seconds`	Yes	Approximate seconds until the report is ready. Use as a hint for when to first poll.

TDQS

A4.5/5.0

Behavior5/5

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

Beyond the annotations (readOnlyHint=false, openWorldHint=true), the description discloses critical behavioral details: consumes one analysis credit, returns an ID and poll URL, and delivers results asynchronously via polling or webhook. This adds significant context about side effects and workflow that annotations alone do not convey.

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 three sentences, front-loaded with purpose, then usage, then behavioral details. Every sentence serves a distinct function: what it does, when to use it, and what to expect. No wasted words.

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 async delivery mechanism and credit consumption, the description fully covers what the agent needs to know: the final report contents (scores, confidence ratings, narrative summary, source citations) and the polling/webhook flow. The output schema exists, so return format details are not required in the description.

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 coverage is 100%, so the description does not need to explain parameters. The description adds the concept of 'four analytical lenses' which maps to the mode enum, but this is also evident from the schema. No additional parameter-level meaning is provided, so baseline 3 is appropriate.

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 'Generates a comprehensive land analysis report for a US property' through four named analytical lenses, distinguishing it from siblings like quick score or solar potential. The verb 'generates' and the resource 'land analysis report' are specific, and the scope (US property) is explicit.

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 provides explicit usage guidance: 'Call this when the user asks for a full analysis of a specific property.' It also advises asking for the mode when intent is unclear. However, it does not explicitly name alternative tools or say when not to use this tool, so it falls short of a perfect 5.

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

A4.5/5.0

Disambiguation5/5

Each tool targets a clearly distinct purpose: analyze_land for full property analysis, compare_properties for batch comparison, get_land_quick_score for quick screening, get_solar_potential for solar estimates, and get_state_land_profile for state-level context. No overlap or ambiguity.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case: analyze_land, compare_properties, get_land_quick_score, get_solar_potential, get_state_land_profile. Naming is uniform and predictable.

Tool Count5/5

5 tools is well-scoped for the land analysis domain. Each tool provides a distinct, necessary function without redundancy. The count is appropriate and not excessive.

Completeness4/5

The tool set covers core workflows: full analysis, comparison, quick screening, solar potential, and state context. Minor gaps exist (e.g., water access, soil data, or zoning details) but these are not essential for typical use cases.

Resources

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