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CRE Intelligence

by io.github.Zwondra

screen_land_market

Screen a US county as a LAND-INVESTING market (raw-land flip / Podolsky style). Grades the county on the signals that matter for buying cheap rural land and reselling on terms: population growth, demographics, owner share, and affordability.

IMPORTANT: This screens on FREE Census data only (growth + demographics + a home-value affordability proxy). It does NOT include actual land sale prices or comps — those require county records or a paid service, and must be verified per-parcel before buying. Use this to rank/shortlist markets, not to buy.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stateYes2-letter state abbreviation (e.g. "AZ") or 2-digit state FIPS
countyYesCounty name (e.g. "Mohave" or "Mohave County")

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.5/5.0
Behavior5/5

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

With no annotations, the description fully carries the burden. It discloses the tool uses only free Census data, grades on specific signals, and lacks actual sale prices. It clearly states the limitations and that results must be verified before buying. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with two paragraphs: purpose first, then caveats. It is front-loaded with the core action. Could be slightly more concise but is not overly verbose.

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?

Has output schema and 100% parameter coverage. The description explains the grading signals and warns about data limitations, providing sufficient context for an agent to understand the tool's capabilities and limitations. Minor gap: does not explicitly list output fields, but output schema exists.

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?

Input schema has 100% coverage with clear descriptions for 'state' and 'county'. The description does not add extra parameter-level meaning beyond what the schema provides, which is acceptable given the high schema coverage. 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 screens a US county as a land-investing market, specifies the style (raw-land flip / Podolsky), and lists the signals used (population growth, demographics, etc.). It distinguishes from sibling tools like 'screen_parcel_dd' and 'get_cre_market_data' by focusing on county-level land market screening.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use (to rank/shortlist markets) and what it does NOT include (actual land sale prices, comps). It warns that per-parcel verification is needed and that data is from free Census sources only. This provides clear guidance on appropriate use and limitations.

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

A4.3/5.0
Disambiguation5/5

Each tool targets a distinct resource and action: lease documents, rent rolls, DCF models, Excel export, risk flags, deal memos, market data, rates, inflation, demographics at two geographic levels, and land screening at two scales. No two tools have overlapping purposes.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., abstract_lease, get_current_rates, screen_parcel_dd), making the tool surface predictable and easy to navigate.

Tool Count5/5

With 13 tools, the server is well-scoped for CRE investment analysis, covering lease abstraction, rent roll analysis, DCF modeling, market data, demographics, and land screening. The count is sufficient without being bloated.

Completeness5/5

The tool set covers the full lifecycle of CRE deal analysis: document parsing (lease, rent roll), financial modeling (DCF, Excel export), risk assessment (flag risks, parcel screens), market context (rates, inflation, demographics, CRE market data), and output (deal memo). No obvious gaps for the intended purpose.

Resources