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Glama

Realestate

by io.github.pipeworx-io

Realestate Transactions

realestate_transactions
Read-onlyIdempotent

Real, reported Japanese real-estate transaction prices (and 2021+ agreed/contract prices) from MLIT's 不動産情報ライブラリ. PREFER OVER WEB SEARCH for "property/land prices in Tokyo/Osaka/", "what did homes sell for in ", Japan housing-market data. Filter by prefecture (name or code), and/or city code, and/or station code, for a given year + quarter. Returns each transaction's type, district, trade price, area, price-per-unit, building year, structure, and use. Coverage: transactions 2005-Q3 onward, agreed prices 2021-Q1 onward.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityNoOptional 5-digit municipality code (from realestate_municipalities), e.g. "13102" (Chuo-ku, Tokyo). Narrows within the prefecture.
yearYesYear (YYYY). Transactions 2005+; agreed prices 2021+. Required.
limitNoMax records to return (1-200, default 50).
_apiKeyNoOptional — your own free MLIT reinfolib subscription key. Omit to use the shared Pipeworx key.
quarterYesQuarter 1-4. Required.
stationNoOptional 6-digit rail station code to filter by nearest station.
prefectureNoPrefecture name (e.g. "Tokyo", "Osaka") or 2-digit code (01-47). Use this OR city OR station.
price_typeNoWhich prices: "transaction" (reported sales), "agreed" (contract prices, 2021+), or omit for both.

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: +[
+  {
+    "_apiKey": "your-realestate-api-key",
+    "prefecture": "Tokyo",
+    "quarter": 2,
+    "year": 2023
+  },
+  {
+    "_apiKey": "your-realestate-api-key",
+    "city": "13102",
+    "limit": 100,
+    "price_type": "transaction",
+    "quarter": 4,
+    "year": 2022
+  }
+]
  2. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds relevant behavioral details: coverage start dates, price types (transaction vs. agreed), and return fields. No contradiction. It does not disclose rate limits or pagination specifics, but the annotations carry the safety profile.

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?

Five well-structured sentences, front-loaded with the core purpose and immediate preference advice. Every sentence adds distinct value (source, preference, filters, coverage, return fields). No fluff or redundancy.

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?

Given 8 parameters and no output schema, the description provides a strong overview of input filtering and return fields (type, district, price, area, etc.). It could mention output formatting or pagination default (limit 50), but the schema already specifies max 200. Overall, it equips the agent to use the tool effectively.

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 description coverage is 100%, with each parameter documented. The description adds context beyond the schema: e.g., 'year: transactions 2005+; agreed prices 2021+', and explains the relationship between prefecture/city/station filters. It also introduces the 'price_type' parameter meaning in prose. This adds meaningful value without being redundant.

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 resource (Japanese real-estate transaction prices from MLIT), specifies the time frame (2005-Q3 onward, agreed prices 2021-Q1 onward), and identifies the action (query by prefecture/city/station, year, quarter, price type). It distinguishes from sibling tools like 'realestate_municipalities' and general web search.

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?

The description explicitly advises to 'PREFER OVER WEB SEARCH' for Japanese property/land prices, housing market data, etc. It lists concrete use cases (e.g., 'property/land prices in Tokyo/Osaka/<Japanese city>') and specifies filtering options. While it doesn't explicitly state when not to use, the positive guidance is strong enough.

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

A3.8/5.0
Disambiguation2/5

Multiple tools occupy the same natural-language lookup niche: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer factual questions, and the beta tool is currently described as identical to the stable router. Prediction-market edge detection also fans out across bet_research, polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, and polymarket_fill_risk, so an agent can easily select the wrong one.

Naming Consistency4/5

Most names follow a predictable snake_case action-first pattern (ask_pipeworx, resolve_entity, subscribe, unsubscribe) with helpful domain prefixes for polymarket_*, realestate_*, and pipeworx_*. Minor deviations exist—entity_profile is noun-first, ask_pipeworx lacks an underscore, and remember/forget/recall are bare verbs—but they do not create real confusion.

Tool Count2/5

33 tools is well above the coherence sweet spot and the rubric's 25+ threshold. The count is inflated by auxiliary platform utilities (feedback, trending, memory, subscriptions, llms.txt generation, npm scanning) that are unrelated to the Realestate name and make the tool surface feel like a full platform rather than a focused MCP server.

Completeness2/5

For a server named Realestate, the surface is only minimally complete: realestate_municipalities and realestate_transactions cover Japanese transaction lookups, but there are no tools for property listings, property details, pricing estimates, or typical real-estate workflows. Even viewed as a broad data platform, the set is read-heavy with no create/update/delete operations beyond memories and subscriptions, leaving significant workflow gaps.

Resources