US land due-diligence MCP server. Returns structured reports covering solar potential, groundwater depth, flood zones, building codes, and county regulations for any US property address. Mode-aware across off-grid, rural residential, recreational, and investment use cases. 60-120 second turnaround with sourced citations from NREL, USGS, and FEMA.
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Average 4.5/5 across 5 of 5 tools scored.
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.
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.
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.
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.
analyze_landAInspectGenerates 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.
| Name | Required | Description | Default |
|---|---|---|---|
| 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"). |
| 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. |
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.
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.
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.
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.
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.
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.
compare_propertiesAInspectCompare 2–5 US properties side by side using the same analysis mode. Call this when the user is evaluating multiple parcels or listings and wants a comparative view. Kicks off the analyses asynchronously and returns a batch ID plus one report ID per property (in input order) to poll — scored results arrive via polling or the batch webhook, not inline. Consumes one analysis credit per property from your AcreLens account.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | Yes | Analysis lens to apply to every property. | |
| properties | Yes | Array of 2–5 properties to compare. |
| Name | Required | Description |
|---|---|---|
| status | Yes | Batch status. Initially "processing"; transitions to "completed" once all per-property reports terminate. |
| batch_id | Yes | Unique batch ID grouping the report jobs created by this call. |
| report_ids | Yes | Per-property report IDs in the same order as the input properties array. Poll each individually or wait for the batch.completed webhook. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint=false, idempotentHint=false), the description discloses async behavior, batch/report ID polling, non-inline results, and credit consumption per property. This is valuable context that annotations do not provide and significantly aids agent expectations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences with no wasted words. It front-loads the core purpose, then provides usage context, then async/credit details. Each sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (async, batch, credits, multiple properties) and an existing output schema, the description covers all essential behavioral aspects: comparative purpose, async kickoff, polling mechanism, and credit cost. It does not need to explain return values due to the output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides 100% description coverage for both parameters, so the baseline applies. The description adds no extra parameter-specific detail beyond mentioning 'same analysis mode' and '2–5 properties,' but it doesn't need to since the schema is exhaustive.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Compare') and resource ('US properties') with a clear scope (2–5) and a shared analysis mode. It clearly distinguishes from sibling tools, which focus on single-property analysis, quick scores, solar potential, or state profiles.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Call this when the user is evaluating multiple parcels or listings and wants a comparative view.' It provides clear when-to-use guidance but does not explicitly name alternatives or state when not to use, though the contrast with siblings is implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_land_quick_scoreAInspectGet a fast suitability score (0-100) for a US property without generating a full report. Call this when the user wants a quick go/no-go assessment or an initial screening before committing to a full analysis. Returns a single score with confidence level and one-sentence rationale. Consumes a partial (0.25) analysis credit from your AcreLens account.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | Yes | Analysis lens: off_grid, rural_residential, recreational, or investment. | |
| state | Yes | 2-letter US state code (e.g. "NM"). | |
| address | Yes | Full street address of the US property. |
| Name | Required | Description |
|---|---|---|
| score | Yes | Overall suitability score 0-100. Null while still processing. |
| status | Yes | "completed" when the score is ready; "processing" if the poll timed out and the caller should retry the report later. |
| summary | Yes | One-sentence rationale for the score. Null while still processing or if no summary was generated. |
| report_id | Yes | Unique ID for the underlying quick-mode report. |
| confidence | Yes | Aggregate confidence level derived from per-category confidences. Null while still processing. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate non-read-only and non-idempotent behavior, but the description adds valuable context by disclosing the 'partial (0.25) analysis credit' consumption and the return format (single score with confidence and rationale). This goes beyond what annotations provide, enhancing the agent's understanding of side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is four concise sentences, each serving a distinct purpose: function, use case, output, and cost. There is no redundancy or fluff, making it well-structured and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema, the description provides important context not in the schema: the cost in credits, the US-only scope, and the distinction from a full report. This makes the description complete for an agent deciding whether to invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with all three parameters (address, state, mode) already described in the schema. The tool description does not add additional parameter-specific meaning beyond what the schema provides, so a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Get a fast suitability score (0-100) for a US property without generating a full report.' It uses a specific verb and resource, and distinguishes from siblings by emphasizing speed and the lack of a full report, which aligns with analyze_land being the full-analysis alternative.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit usage guidance is provided: 'Call this when the user wants a quick go/no-go assessment or an initial screening before committing to a full analysis.' This tells the agent exactly when to use it and implies the alternative of running a full analysis, effectively differentiating from sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_solar_potentialAEstimate solar energy production potential for a US address using NREL PVWatts data. Call this when the user asks about solar power viability, off-grid energy, or panel sizing. Returns estimated annual production and a typical installed-cost bracket for the system size you specify (default 5 kW).
| Name | Required | Description | Default |
|---|---|---|---|
| lat | No | Latitude of the location. | |
| lng | No | Longitude of the location. | |
| address | No | US street address (used for geocoding fallback). | |
| system_size_kw | No | System size in kilowatts (default 5). |
| Name | Required | Description |
|---|---|---|
| latitude | Yes | Latitude used for the calculation. Will be the resolved geocoded value if address was provided instead of lat/lng. |
| longitude | Yes | Longitude used for the calculation. Will be the resolved geocoded value if address was provided instead of lat/lng. |
| annual_kwh | Yes | Estimated annual AC electricity production in kilowatt-hours, from NREL PVWatts. |
| cost_bracket | Yes | Typical installed-cost range (USD) for a system of this size. Indicative only; varies by region and installer. |
| system_size_kw | Yes | System nameplate capacity in kilowatts (echoed from the request, default 5). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds useful context beyond those: it specifies the data source (NREL PVWatts), the return values (annual production and cost bracket), the system size default (5 kW), and the US-only geographic scope, enriching what annotations alone convey.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, front-loads the core purpose, then covers usage triggers, return content, and default behavior. Every clause serves a purpose, and there is no redundant or filler text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the annotations, output schema, and schema descriptions, the description covers all essential aspects: what it does, when to use it, what it returns, and key defaults. It is fully sufficient for an agent to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already provides 100% coverage with descriptions for all four parameters. The description's only parameter-related addition is 'default 5 kW' in reference to system size, which duplicates the schema's default field. Thus, it adds minimal extra meaning, matching the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Estimate') and resource ('solar energy production potential for a US address'). It also distinguishes itself from sibling land-analysis tools by focusing on solar viability and using NREL PVWatts data, making its role unmistakable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Call this when the user asks about solar power viability, off-grid energy, or panel sizing,' which gives clear context for when to use it. However, it does not mention when not to use it or point to alternatives, so it doesn't quite meet the threshold for a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_state_land_profileARetrieve state-level land intelligence data covering regulation, climate, solar potential, water access, and building codes. Call this when the user wants general context about a US state before drilling into a specific property. Returns structured multi-mode profiles.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | Optional mode filter. If omitted, returns all 4 modes. | |
| state_code | Yes | 2-letter US state code (e.g. "NM"). |
| Name | Required | Description |
|---|---|---|
| modes | Yes | Per-mode profile data, keyed by mode name (off_grid, rural_residential, recreational, investment). Contains the requested mode if a filter was provided, otherwise all four. |
| state_code | Yes | 2-letter US state code echoed from the request. |
| shared_facts | No | Cross-mode state-level facts (statute citations, agency names, etc.) that apply across all modes. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive nature. The description adds behavioral detail by noting it 'returns structured multi-mode profiles,' and the content coverage list gives insight into the response scope. It does not contradict annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the primary action, and free of redundancy. Every phrase adds value, balancing action, content, usage, and return structure.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only, low-complexity tool with a rich output schema and comprehensive annotations, the description covers what it does, when to use it, what data it covers, and what to expect in return. It is sufficiently complete without needing to restate schema details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for both parameters, so the schema fully documents state_code and mode. The description's mention of 'multi-mode profiles' provides a minimal hint about the mode parameter but adds no meaning beyond the schema's own descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Retrieve') and resource ('state-level land intelligence data'), listing concrete content areas (regulation, climate, solar potential, water access, building codes). It distinguishes from property-specific sibling tools by stating it provides general state context 'before drilling into a specific property.'
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Call this when the user wants general context about a US state before drilling into a specific property,' giving a clear use case. It implies exclusion of property-specific queries but does not explicitly name alternatives like get_solar_potential or analyze_land.
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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