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Glama

Housing Intel

by io.github.pipeworx-io

Housing Metro Demand

housing_metro_demand
Read-onlyIdempotent

Demand + rent-durability signals for a shortlist of US metros in ONE call — population & 5-year growth, renter share, median household income, and unemployment, straight from Census ACS. Deterministic by metro (CBSA-keyed) — NO FRED series-ID guessing. Pass metros ("City, ST", e.g. the top results from housing_market_screen). This is the Stage-2 "is the demand real?" filter on a yield shortlist — high yield in a shrinking metro is a trap. No API key needed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax metros to enrich (default 25, max 100).
metrosYesMetro names to enrich, "City, ST" form, e.g. ["Lubbock, TX","Pittsburgh, PA"] — match the housing_market_screen output. Required.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "metros": [
+      "Lubbock, TX",
+      "Pittsburgh, PA"
+    ]
+  },
+  {
+    "limit": 10,
+    "metros": [
+      "Denver, CO",
+      "Austin, TX",
+      "Boise, ID"
+    ]
+  }
+]
  2. Added

TDQS

A4.7/5.0
Behavior4/5

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

Annotations (readOnlyHint, idempotentHint) already indicate safety and idempotency. Description adds extra behavioral context: deterministic by metro, uses Census ACS data, no API key needed. 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.

Conciseness5/5

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

Two sentences with a concise closing line. Front-loaded with main purpose and value proposition. No redundant information.

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?

Covers all essential context: data sources (Census ACS), use case (demand validation), parameter hints, and integration with sibling tool. Despite no output schema, the description adequately informs what the tool returns.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. Description adds value by specifying format of metros ("City, ST") and connection to housing_market_screen output, plus default and max for limit. Provides examples in schema but context reinforces usage.

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?

Description specifies the tool provides demand and rent-durability signals for US metros, including specific data points (population growth, renter share, income, unemployment). It clearly distinguishes from sibling tools by positioning it as a Stage-2 filter after housing_market_screen.

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: as a Stage-2 filter on a yield shortlist after housing_market_screen. Provides a warning ('high yield in a shrinking metro is a trap') and contrasts with alternatives that require FRED series-ID guessing.

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

A3.6/5.0
Disambiguation2/5

Several tools intentionally overlap: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded share the same router, with the beta variant explicitly matching stable behavior right now. entity_profille/recent_changes/compare_entities and the multiple polymarket scanning tools also cover closely related jobs, so an agent must read carefully to avoid picking the wrong variant.

Naming Consistency3/5

All tools use lowercase snake_case, which is a consistent base style. However, the naming grammar is mixed: proper verb_noun tools like compare_entities and validate_claim sit beside noun-phrase/domain tools like housing_market_screen and polymarket_edges, plus the awkward compound case_shiller_metro_compare. The housing_ and polymarket_ prefixes help, but the pattern is not uniform enough for a 5.

Tool Count2/5

41 tools far exceeds the 25+ threshold and the typical well-scoped 3-15 range. Many tools pertyain to Polymarket, npm scanning, llms.txt generation, and memory, which have little to do with Housing Intel, so the count is not earned by the server's stated domain.

Completeness4/5

For the housing domain specifically, the coverage is strong: market snapshot, affordability, employment, mortgage history, rental/property analysis, metro demand, signal scanning, and Case-Shiller comparisons cover the main data needs. The generic ask_pipeworx and deep_research tools also backfill specialized queries. The weakness is scope blur, not obvious missing housing operations.

Resources