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Housing Intel

by io.github.pipeworx-io
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Housing Market Screen

housing_market_screen
Read-onlyIdempotent

Rank US metros for rental cash flow in ONE call — the "which markets are best for a landlord" view. Returns metros sorted by gross rent yield = (Zillow median monthly rent × 12) ÷ Zillow typical home value. No per-metro orchestration and no API key. Use for "best/worst rental markets", "highest-yield metros", "where does rent go furthest vs. home prices". Tune with direction (top = highest yield / best cash flow, bottom = lowest), limit, and optional home-value bounds. rent_as_of and home_value_as_of are two independent Zillow queries and can land on different months; freshness says whether they matched this call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMetros to return (default 25, max 100).
directionNotop = highest gross yield (best cash flow), bottom = lowest. Default top.
max_home_valueNoOptional: only metros with typical home value ≤ this (USD).
min_home_valueNoOptional: only metros with typical home value ≥ this (USD).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNo
marketsNo
analysisNo
directionNo
freshnessNo
ranked_byNo
rent_as_ofNo
home_value_as_ofNo
metros_evaluatedNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "analysis": {
+      "type": "string"
+    },
+    "direction": {
+      "type": "string"
+    },
+    "freshness": {
+      "properties": {
+        "as_of": {
+          "type": "string"
+        },
+        "dates_match": {
+          "type": "boolean"
+        },
+        "statement": {
+          "type": "string"
+        }
+      },
+      "type": "object"
+    },
+    "home_value_as_of": {
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "markets": {
+      "items": {
+        "properties": {
+          "gross_rent_yield_pct": {
+            "type": "number"
+          },
+          "metro": {
+            "type": "string"
+          },
+          "monthly_rent": {
+            "type": "number"
+          },
+          "typical_home_value": {
+            "type": [
+              "number",
+              "null"
+            ]
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "metros_evaluated": {
+      "type": "number"
+    },
+    "note": {
+      "type": "string"
+    },
+    "ranked_by": {
+      "type": "string"
+    },
+    "rent_as_of": {
+      "type": [
+        "string",
+        "null"
+      ]
+    }
+  },
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "direction": "top",
+    "limit": 25
+  },
+  {
+    "direction": "bottom",
+    "limit": 10,
+    "max_home_value": 500000,
+    "min_home_value": 300000
+  }
+]
  3. Added

TDQS

A4.3/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description goes beyond annotations by revealing that rent_as_of and home_value_as_of are independent Zillow queries that can land on different months, and that 'freshness' indicates match status. It also notes no API key is required, which is useful operational context. This adds value without contradicting annotations.

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 dense but every sentence earns its place: it states purpose, formula, use cases, tuning options, and the crucial caveat about independent date queries. It's front-loaded with the core value proposition and avoids fluff. No redundancy with the schema is present.

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?

The tool has an output schema (not shown), and the description explains the output is metros sorted by gross rent yield, including the freshness detail. It covers the essential behavior, input semantics, and edge cases (date mismatch). For a screening tool with zero required parameters and full schema coverage, nothing critical is missing.

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 description coverage is 100%, so each parameter already has a description. The tool description rephrases 'top' as 'highest yield / best cash flow' and 'bottom' as 'lowest', adding a slight interpretive layer. However, it doesn't add new technical details beyond the schema (e.g., default limits, value ranges). Since the schema carries the weight, a 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 states a specific verb and resource ('Rank US metros for rental cash flow') and provides the exact formula used. It clearly distinguishes this tool from siblings like housing_market_snapshot by positioning it as the landlord/best-yield view and explicitly noting 'no per-metro orchestration', which separates it from tools that require multi-step calls. The purpose is unambiguous and actionable.

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 gives explicit example queries ('best/worst rental markets', 'highest-yield metros') and mentions tuning with direction, limit, and bounds. It doesn't explicitly state when not to use it, but the specificity of use cases and the implicit contrast with siblings (e.g., housing_affordability_check, housing_market_snapshot) provides adequate guidance. A dedicated exclusion note would push this to 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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