Stratalize Real Estate
Server Details
Real estate benchmarks: cap rates, NCREIF returns, REIT, construction costs, and climate risk.
- Status
- Healthy
- Last Tested
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- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.2/5 across 6 of 6 tools scored.
Most tools target distinct aspects: climate risk, HUD rents, macroeconomic disaster costs, storm event history, weather delay, and a meta-overview. However, get_noaa_disaster_economics and get_storm_event_history both deal with billion-dollar disasters and could cause confusion despite different emphases.
All tool names follow a consistent 'get_' prefix with snake_case, e.g., get_climate_risk_score, get_storm_event_history. No mixing of styles or irregular patterns.
Six tools is a reasonable count for a specialized real estate intelligence server. It is slightly on the smaller side but not overly sparse for its stated purpose.
The tool set covers climate risk, rents, disasters, and weather delays, but lacks fundamental real estate operations like property valuations, market trends, or demographic data. Significant gaps exist for a comprehensive real estate intelligence platform.
Available Tools
6 toolsget_climate_risk_scoreARead-onlyInspect
Use when pricing physical climate risk for a location — real estate acquisition, commercial property underwriting, construction site selection, or climate-related financial disclosure. Returns a composite risk score across six perils (flood, hurricane, tornado, wildfire, extreme heat, freeze) using the same risk factors embedded in FEMA's National Risk Index. Example: Miami-Dade FL — EXTREME overall, top 2% hurricane exposure, Zone AE flood designation across 40% of commercial parcels, 94 days above 95°F annually — commercial property insurance costs 3.2x national median. Source: NOAA Climate Normals, FEMA National Risk Index, USGS Natural Hazards composite.
| Name | Required | Description | Default |
|---|---|---|---|
| location | Yes | US city and state (e.g. Miami FL or Houston Texas) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds significant behavioral context beyond annotations: it details the six perils, data sources (NOAA, FEMA, USGS), and an example output. This complements the readOnlyHint and destructiveHint annotations without contradiction.
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, front-loaded with purpose, and efficiently includes use cases, peril details, data sources, and an illustrative example. Every sentence adds value without redundancy.
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 (composite risk across multiple perils), the description fully explains its output, data sources, and typical use. No output schema exists, but the description provides sufficient context for correct invocation.
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 'location' is described in the schema as 'US city and state', with 100% coverage. The description includes an example ('Miami-Dade FL'), which adds marginal clarity but does not substantially increase meaning beyond the schema.
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 explicitly states the tool's purpose: pricing physical climate risk for a location, with specific use cases (acquisition, underwriting, site selection, disclosure) and details about the composite risk score across six perils. This clearly distinguishes it from sibling tools, which cover diverse benchmarks and intelligence.
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 starts with 'Use when pricing physical climate risk for a location', providing clear usage context. While it doesn't explicitly state when not to use it or list alternatives, the use cases are specific and the tool's scope is well-defined.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_hud_fair_market_rentARead-onlyInspect
HUD Fair Market Rents by metro area and bedroom count. Used for affordable housing underwriting, Section 8 Housing Choice Voucher compliance, LIHTC income limit calculations, and housing authority budgeting. Source: HUD annual FMR dataset. Free.
| Name | Required | Description | Default |
|---|---|---|---|
| metro_area | Yes | e.g. Chicago, IL or Miami, FL | |
| bedroom_count | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds that the data is free and from HUD's annual dataset, but does not disclose details like update frequency, rate limits, or return format. With annotations providing the core safety info, a 3 is appropriate.
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?
Two concise sentences, front-loaded with purpose and use cases. No wasted words; every sentence adds value.
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 two parameters and no output schema. The description covers source, cost, and use cases, but omits details about the return format (e.g., monthly rent values per bedroom). For a straightforward query, this is adequate but not thorough, hence a 3.
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 50% (metro_area has a description, bedroom_count does not). The description does not add meaning beyond the schema's parameter definitions. Given moderate coverage and no elaboration, a baseline score of 3 is justified.
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?
Clearly states it provides HUD Fair Market Rents by metro area and bedroom count. The description includes specific use cases (affordable housing underwriting, Section 8, LIHTC) which distinguishes it from siblings that cover climate, disaster, or weather data.
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 lists usage scenarios like affordable housing underwriting and compliance. Does not provide negative guidance or mention alternatives, but the context is sufficiently specific for selection among dissimilar sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_noaa_disaster_economicsARead-onlyInspect
Use when establishing the macroeconomic cost of climate risk for board-level ESG reporting, reinsurance negotiations, infrastructure investment decisions, or climate-related financial risk disclosures under SEC or TCFD frameworks. Returns NOAA's official annual billion-dollar disaster economics — event count, total losses, deaths, and historical context showing 10-year trend acceleration. Example: 2023 — 28 events, $92.9B total losses, 12% above the 10-year average — the fifth consecutive year of above-average economic losses. Cited by the Federal Reserve, Treasury, and major reinsurers as the authoritative US climate loss series. Source: NOAA NCEI.
| Name | Required | Description | Default |
|---|---|---|---|
| year | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds that the tool returns annual data with historical context and trend acceleration. However, it omits details on rate limits, data freshness, or pagination, which are beyond annotations but would add value.
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 verbose with multiple sentences and includes credibility statements (e.g., 'Cited by the Federal Reserve'). It is front-loaded with usage context but could be more concise without losing meaning.
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 no output schema, the description explains the output includes event count, total losses, deaths, and historical trend, with a concrete example for 2023. For a tool with one optional parameter, this is mostly complete, though it could clarify the exact output structure.
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?
With 0% schema description coverage, the description partially compensates by mentioning year in the example (2023). It implies the parameter controls which year's data, but does not explicitly state the format or valid range. Baseline of 3 is appropriate given the single required parameter is optional with a default.
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 returns NOAA's annual billion-dollar disaster economics data, including event count, total losses, deaths, and historical trend acceleration. This specific verb-resource pairing distinguishes it from sibling tools like get_storm_event_history or get_climate_risk_score.
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 enumerates use cases: ESG reporting, reinsurance negotiations, infrastructure decisions, and SEC/TCFD disclosures. It implies when to use but does not explicitly name alternatives or state when not to use, leaving room for improvement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_storm_event_historyARead-onlyInspect
Use when quantifying climate-related financial risk for insurance underwriting, real estate acquisition due diligence, ESG climate risk disclosures, or board-level climate briefings. Returns NOAA's official tally of billion-dollar weather disasters — hurricane, flooding, tornado, wildfire, winter storm — with event frequency, total economic losses, deaths, and trend direction. The same dataset cited by reinsurers, the Federal Reserve Financial Stability Report, and the SEC climate disclosure framework. Example: Texas 10-year history — 31 billion-dollar events, $174B total losses, frequency increasing — highest insured loss exposure of any US state. Source: NOAA NCEI Billion-Dollar Disasters.
| Name | Required | Description | Default |
|---|---|---|---|
| state | No | US state name or abbreviation. Omit for national summary. | |
| years_back | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=true and destructiveHint=false. The description adds valuable context: the data source (NOAA), authoritative recognition (reinsurers, Federal Reserve, SEC), and the output structure (event frequency, losses, deaths, trend). No contradictions with 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 concise and front-loaded with use cases. Every sentence adds value: use cases, what it returns, an example, and the source. No redundancy or fluff.
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 only 2 simple parameters and no output schema, the description sufficiently explains the output metrics and provides an illustrative example. It covers the key information an agent needs to 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?
Schema coverage is 50% (state described, years_back not). The description adds the example 'Texas 10-year history' and implies default behavior, but does not provide explicit format or constraints for years_back beyond the schema default. Baseline adjusted for low coverage, but description provides some context.
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 returns NOAA's tally of billion-dollar weather disasters with specific metrics (frequency, losses, deaths, trend). It specifies the resource and action precisely, and the context of use cases distinguishes it from general sibling tools like get_noaa_disaster_economics.
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 first sentence explicitly lists specific use cases (insurance underwriting, real estate due diligence, ESG disclosures, board briefings). While it doesn't explicitly say when not to use or name alternatives, the context is well-defined and implies appropriate scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_stratalize_overviewARead-onlyInspect
START HERE - Returns the complete Stratalize tool catalog: 194 governed MCP tools across 6 namespaces (crypto, finance, governance, healthcare, realestate, intelligence). 72 tools available via x402 (USDC micropayments on Base): $0.02 atomic · $0.10 benchmark · $0.50 synthesis · $1.00 premium; 60 priced tier tools + 12 free reference tools. 64 additional tools accessible via OAuth-authenticated MCP for organizations. Call this first to discover C-suite briefs (CEO, CFO, CRO, CMO, CTO, CHRO, CX, GC, COO), market benchmarks, governance compliance tools (EU AI Act, FS AI RMF, UK FCA), and org intelligence with role-based recommendations. No auth required.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=true, and the description reinforces this by stating 'Returns the complete catalog' and 'No auth required.' It adds extensive context about the tool's contents (194 tools, namespaces, pricing tiers, OAuth access for some) that goes well beyond annotations, providing full transparency.
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 front-loaded with 'START HERE' and the main purpose. It is moderately lengthy but packs necessary details (counts, namespaces, pricing, auth) into a clear prose structure. Could be slightly more structured with separators, but overall efficient.
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 no output schema, the description adequately describes what is returned (tool catalog with specific categories, pricing, and access modes). It lacks a precise output format, but for an overview tool the context is sufficient for an agent to query it first and then select other tools.
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?
There are no parameters (0 params). Schema coverage is trivially 100%. The description adds no parameter-specific meaning but this is appropriate as no parameters exist. Baseline for 0 params is 4.
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 explicitly states 'START HERE - Returns the complete Stratalize tool catalog', clearly identifying the verb (returns) and resource (tool catalog). It distinguishes from siblings by describing a completely different domain (tool overview vs. environmental data), and even specifies the content (C-suite briefs, benchmarks, etc.).
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 says 'Call this first' and implies this tool is an entry point to discover available tools, including pricing and namespaces. It does not explicitly mention when not to use this tool or list alternatives, but the 'START HERE' directive and sibling context make usage clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_weather_delay_riskARead-onlyInspect
Use when scheduling outdoor construction work, planning equipment deployment, or assessing weather risk for any US project site. Analyzes NOAA 7-day forecast data against construction delay thresholds — precipitation probability, wind speed above 25 mph, and freeze events below 32°F — returning a risk tier and specific high-risk days to avoid. Example: Chicago IL project site shows HIGH delay risk Thursday through Saturday — 70% precipitation probability, 2.3 inches rain forecast, 28°F overnight low Friday. Reschedule concrete pours and crane operations. Source: NOAA National Weather Service — official US government forecast.
| Name | Required | Description | Default |
|---|---|---|---|
| location | Yes | US city and state, or full street address (e.g. Chicago IL or 1600 Pennsylvania Ave Washington DC) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint: true and destructiveHint: false. The description adds that it uses official NOAA data and specifies thresholds (precipitation probability, wind >25 mph, freeze <32°F). It also states the output includes risk tier and high-risk days. This adds context beyond annotations without contradicting them.
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: three sentences plus an example and source citation. It is front-loaded with use cases and purpose, then explains analysis, provides a concrete example, and cites the data source. 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?
Despite lacking an output schema, the description fully covers the return value ('risk tier and specific high-risk days') and provides an illustrative example. It addresses the simple input format, data source, and typical use case, making the tool's behavior complete for an AI agent.
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% with a description for 'location' (US city/state or address). The description reinforces this with examples ('Chicago IL', '1600 Pennsylvania Ave') but does not add significant new meaning beyond the schema baseline. Hence a 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 explicitly states the tool analyzes NOAA 7-day forecast data against construction delay thresholds (precipitation, high wind, freeze) and returns a risk tier with high-risk days. It distinguishes itself from siblings by being specific to US outdoor construction weather risk, using clear verb 'Analyzes...returning' and resource 'NOAA 7-day forecast data'.
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 use cases: 'when scheduling outdoor construction work, planning equipment deployment, or assessing weather risk for any US project site' and includes an example with actions like 'Reschedule concrete pours and crane operations.' It does not explicitly state when not to use or compare to alternatives, but the context makes usage clear.
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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