myplot.ai Property Intelligence
Server Details
US property risk data for agents: flood, hazards, taxes, air quality. Credits or USDC.
- Status
- Unhealthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Available Tools
2 toolsget_property_reportAInspect
Get comprehensive property risk data plus an AI-written narrative analysis for any US address. Includes all 22 data sources from get_property_risk_data plus a detailed written assessment highlighting key risks and opportunities for homebuyers or investors. The AI analysis flags only notable outliers and explains their real-world impact. Costs 3 API credits.
| Name | Required | Description | Default |
|---|---|---|---|
| address | Yes | Full US street address (e.g. '123 Main St, Austin TX 78701') |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the cost in API credits, the inclusion of all 22 data sources plus an AI narrative, and that the analysis flags only notable outliers. This provides useful behavioral context beyond the basic purpose, though it doesn't detail the response format or rate limits.
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 concise (3 sentences) and front-loaded with the primary purpose. Each sentence adds value: the first defines the tool, the second details what's included, the third notes the cost and analysis behavior. No redundant or tautological content.
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?
The tool is simple with one parameter and no output schema. The description covers the purpose, scope (US addresses), intended audience, feature set, and cost. It does not specify the output structure, but the tool is straightforward enough that this is a minor gap, making it nearly complete.
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 single parameter (address) has full schema description coverage. The description adds no extra parameter-specific semantics, but the schema already provides a clear example and definition. Baseline of 3 is appropriate since the schema fully documents the parameter.
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: getting comprehensive property risk data plus an AI-written narrative analysis. It also distinguishes itself from the sibling tool by explicitly mentioning it includes all 22 data sources from get_property_risk_data and adds a written assessment, making the purpose specific and unambiguous.
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 implies usage context by addressing homebuyers or investors and notes the tool is a superset of get_property_risk_data, which suggests when to choose this over the sibling. However, it lacks an explicit 'when not to use' or direct alternative comparison, so it doesn't fully meet the 5-level criterion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_property_risk_dataAInspect
Get comprehensive property risk data for any US address from 22 federal data sources. Returns flood zone (FEMA), earthquake risk (USGS), air quality (EPA AirNow), radon zone, crime index, walkability scores, broadband coverage (FCC), solar potential (NREL), natural hazard risk (FEMA NRI), demographics (Census), noise levels (HowLoud), EV charging, transit access, water quality, environmental justice indicators, property tax data, disaster history, and more. Costs 1 API credit.
| Name | Required | Description | Default |
|---|---|---|---|
| address | Yes | Full US street address (e.g. '123 Main St, Austin TX 78701') |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It prominently states the cost ('Costs 1 API credit') and lists numerous return categories, giving agents a realistic picture of the tool's scope. It does not discuss error handling, rate limits, or read-only status, but the '$get$' action implies a safe read.
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 a single, front-loaded sentence that starts with the primary purpose and then systematically lists data categories. It is somewhat lengthy, but every listed item provides meaningful scope information and serves a purpose.
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?
Without an output schema, the description must explain return values, and it does list many data categories. However, it lacks detail on the response structure, units, or failure behavior, which is a notable gap for a comprehensive data 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?
The schema fully describes the address parameter with an example, achieving 100% schema coverage. The description adds only a broad 'any US address' scope and does not provide additional formatting or syntax details beyond what the schema already provides.
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 specifies the action ('Get') and the resource ('comprehensive property risk data for any US address') and even enumerates many data sources. However, it does not explicitly differentiate from the sibling tool get_property_report, so it is clear but lacks explicit sibling differentiation.
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 implies usage when property risk data for a US address is needed, but it provides no explicit guidance on when not to use this tool or when to prefer the sibling tool get_property_report. No alternatives or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
2 tool updates
- First observed
get_property_report - First observed
get_property_risk_data
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TDQS
The two tools are highly overlapping: get_property_report is essentially get_property_risk_data plus an AI summary. Agents may struggle to decide which to use, especially when cost is a factor, though the descriptions do clarify the difference somewhat.
Both tools follow the same get_property_ pattern with distinct suffixes (_report vs _risk_data), making the naming consistent and predictable.
With only 2 tools, the set feels thin for a domain as broad as property intelligence. However, the server may be intentionally narrow, so it is borderline rather than extreme.
The domain of property intelligence would benefit from additional capabilities such as comparing multiple properties, historical data, or batch operations. The current surface is limited to raw data and a report, leaving significant gaps.