Korea Real Estate MCP

by tae0y

Overview Schema Related Servers Score Discussions

Python

Hybrid

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose: separate tools for different property types (apartment, officetel, villa, single house, commercial) for trades and rents, plus region code, current year/month, financial calculators, and subscription info. No overlapping functionality.
Naming Consistency5/5
Naming follows a consistent pattern: 'get_<property_type>_<action>' for data retrieval (e.g., get_apartment_rent, get_apartment_trades) and 'calculate_<function>' for financial tools. All verbs and property types are uniform.
Tool Count5/5
16 tools is well-scoped for a real estate server covering multiple property types, financial analysis, and subscription data. Each tool earns its place without unnecessary redundancy.
Completeness5/5
The tool set provides comprehensive coverage: trades and rents for five property types, financial calculators, subscription info/stats, region lookup, and current date. Minimal gaps; only commercial rent is missing but acceptable.
Average 4.2/5 across 16 of 16 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- 3 of 3 community issues answered or closed in the last 6 months
- 7 commits in the last 12 weeks
- Last stable release on July 18, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, and the description gives no behavioral details beyond the basic calculation. It does not disclose side effects, return value format, or any assumptions (e.g., compounding frequency).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise (one sentence) but at the cost of omitting critical details. It is front-loaded with the purpose, yet too brief to be fully effective.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 4 required parameters, no schema descriptions, and a lack of behavioral context, the description is not complete enough. It does not leverage the existing output schema to explain return values, leaving the agent undersupplied.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description adds minimal meaning: it mentions 'initial capital' and 'monthly contributions' but does not explain they are in units of 10,000. The other parameters (annual_rate_pct, years) are left to inference.
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 calculates compounded asset growth with initial capital and monthly contributions. This verb+resource specification is distinct from all sibling tools, which focus on loans, rents, trades, etc.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool vs. alternatives. There is no mention of prerequisites, limitations, or contexts where another tool would be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral transparency. It discloses the computation type and output unit but omits details on assumptions (e.g., compounding frequency), rounding, error handling, or edge cases (e.g., zero interest).
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 a single concise sentence with no fluff. It efficiently conveys the core function and output unit. However, it could be slightly expanded to include parameter format hints without losing brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of annotations and a need to compensate, the description is too brief. It does not explain the return value (despite an output schema existing), does not clarify the formula (e.g., how interest is compounded), and lacks validation context. An agent could misinterpret the rate format or output structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description must compensate. It implies parameters (principal, rate, term) and clarifies principal unit (10k KRW) but does not explain that annual_rate_pct is expected as a percentage (e.g., 5 for 5%) vs decimal, or that years is an integer. The value added beyond the schema is minimal.
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 calculates an equal principal+interest monthly payment (EMI) and specifies the output unit (10k KRW). It distinctly differs from sibling tools like calculate_compound_growth or calculate_monthly_cashflow.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives, prerequisites, or limitations. The description is purely functional without contextual usage advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It states 'after debt service and costs' but does not explain what costs are included, whether output is positive/negative, or units (parameters are in 10k units). Minimal behavioral disclosure.
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?
Single sentence, 7 words, efficient and front-loaded. However, brevity sacrifices necessary detail for a financial calculation tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 4 parameters, an output schema (unseen), and no annotations, description is too sparse. It does not mention output type, edge cases, or formula logic. Incomplete for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%. Description does not explain parameter units (10k), what 'other_monthly_costs' includes, or how parameters relate to the formula. The names and defaults provide some hint but description adds no value beyond schema.
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 clearly states the verb 'calculate' and resource 'monthly free cashflow after debt service and costs'. It distinguishes from sibling tools like calculate_loan_payment by focusing on cashflow after expenses.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when or when not to use this tool. It implies use for cashflow calculation, but misses comparisons with siblings like calculate_compound_growth or other financial tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses two authentication methods and error handling. No annotations provided, so description carries the burden. Does not mention rate limits, destructive behavior (appears read-only), or performance traits.
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?
Well-structured with sections for keywords, use cases, authentication, arguments, returns. Front-loaded with purpose. Could be slightly condensed but clear and efficient.
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?
Output schema exists and description lists return fields. Covers authentication, error states, and typical usage. For a metadata list tool, it is fairly complete given the schema presence.
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 has 0% coverage, but description adds brief explanations for each parameter (1-based page, default per_page, return_type). Adds some meaning beyond schema titles and defaults but not extensive.
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?
Clearly states it returns APT subscription notice metadata. Provides Korean keywords and example user queries. Distinguishes from sibling tools like get_apt_subscription_results which handles results, not metadata.
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?
Provides concrete example questions that trigger this tool. Notes that it is not tied to region_code, implying context for when to use versus region-specific tools. Does not explicitly state when not to use or list alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It lacks behavioral context such as idempotency, rate limits, authorization needs, or side effects. Return format is detailed, but behavioral traits are missing.
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?
Well-structured with separate sections for purpose, args, and returns. Front-loaded with main action. Could be slightly more concise, but overall efficient.
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?
Given the presence of an output schema (though not shown), the description fully documents return fields, includes error/network failure handling, and covers default behavior. Complete for a data-retrieval tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description fully compensates: explains region_code as a 5-digit legal district code returned by get_region_code, year_month in YYYYMM format with example, and num_of_rows with default. Adds clear meaning beyond the schema.
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 it returns officetel lease and monthly-rent records for a region and month, with specific verb 'Return'. It distinguishes from siblings like get_officetel_trades and other property-specific rent tools.
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?
Provides explicit usage guidance: use alongside get_officetel_trades to compute jeonse ratio and evaluate rental yield. Advises calling get_current_year_month if year_month not specified. However, no explicit when-not-to-use or alternatives for other property types.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Discloses return structure (total_count, items, summary, error handling) and that it fetches from an API. No side effects mentioned, but it's a read operation. Could mention rate limits, but data retrieval tool is well-described.
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?
Description is well-organized with sections for args and returns. Each sentence adds value, though it is longer than minimal. Front-loaded with purpose.
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 parameter count of 3, description covers all parameters with usage hints. Output schema present, but description still details fields. References sibling tools for utility. Could explain officetel more, but sufficient for the agent.
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 0%, so description must compensate. It explains region_code as a 5-digit legal district code from get_region_code, year_month as YYYYMM format with fallback, and num_of_rows as max records with default 100. Adds meaning beyond schema types.
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?
Clearly states it returns officetel sale records and summary statistics for a region and month. The verb 'Return' and resource 'officetel sale records' are specific. Distinguishes from siblings like get_apartment_trades by mentioning comparison with apartment prices.
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?
Explicitly states the use case: compare officetel prices against apartment prices. Provides parameter guidance (use get_region_code for region_code, call get_current_year_month if not specified). Does not explicitly list when not to use, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full behavioral burden. It discloses that the tool returns records and includes error handling. It outlines the return structure (total_count, items, summary, error/message). It could mention rate limits or authorization needs, but overall is transparent.
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 sections (Args, Returns) and front-loads the purpose. It is fairly concise but could be slightly trimmed without losing value. Overall, it earns its length.
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?
Given the tool's complexity (3 parameters, output schema), the description is complete. It covers parameters, return format, error conditions, and usage guidance. Nothing essential is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema description coverage, the description extensively documents all three parameters: region_code as 5-digit legal district code, year_month in YYYYMM format with fallback instruction, and num_of_rows with default. This adds full meaning beyond the schema titles.
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 returns detached and multi-unit house lease/rent records for a region and month. It provides specific Korean keywords and distinguishes house types, making the purpose unambiguous and distinct from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides guidance on when to use the tool (for a region and month) and references helper tools like get_region_code and get_current_year_month. However, it does not explicitly compare to sibling tools (e.g., get_apartment_rent, get_villa_rent) or state when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully covers behavioral aspects: it explains that the response structure differs from residential tools, highlights the share_dealing field, and lists potential error/message fields. This is sufficient for an agent to understand what the tool does and what to expect.
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 a purpose statement first, then parameter details, then returns. It is somewhat lengthy but each sentence adds value. Minor redundancy with listing return fields in both prose and bullet list. Overall effective.
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 description covers all necessary aspects: purpose, usage context, parameter semantics (including default behavior and helper tool), and return structure with error handling. Given the tool has 3 parameters and an output schema, this is thoroughly complete.
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?
Since schema description coverage is 0%, the description adds significant meaning: it explains region_code as a 5-digit code from get_region_code, year_month format with a helper tool reference, and num_of_rows default. This goes well beyond the bare schema types.
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 returns commercial building sale records for a specific region and month, using specific verbs ('return', 'evaluate') and resource type. It distinguishes from residential tools by listing different response fields and Korean keywords, making its purpose unmistakable.
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 advises using this tool to evaluate commercial real estate alongside residential data and provides a practical tip to call get_current_year_month if the user omits the month. However, it does not explicitly state when the tool should not be used or name alternative sibling tools for exclusion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description bears full burden. It explains behavior: return structure (total_count, items, summary, error/message), lack of unit name, and that area_sqm is gross floor area. It does not mention idempotency or rate limits, but for a read tool this is adequate.
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 concise, front-loaded with purpose and keywords, and uses structured bullet points for parameters and output. Every sentence adds value with no redundancy.
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?
Given the complexity (multiple sibling trade tools, 3 parameters, output schema provided), the description is complete. It covers purpose, parameter details, return structure including error handling, and a key characteristic (no unit name).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description fully explains each parameter: region_code as a 5-digit legal district code from get_region_code, year_month in YYYYMM format with a call to get_current_year_month if not specified, and num_of_rows with default 100. This adds significant meaning.
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 'Return detached and multi-unit house (단독/다가구) sale records for a region and month.' It uses a specific verb and resource, includes Korean keywords, and differentiates from sibling tools like get_apartment_trades and get_villa_trades by explicitly naming the property type.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for detached/multi-unit house sales but does not explicitly state when to use this tool versus siblings. It does provide a prerequisite (call get_current_year_month for year_month if not specified) and notes API limitations, but lacks explicit when-not or alternative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It details authentication methods (two options), parameter mappings (e.g., stat_year_month -> STAT_DE), and mentions the use of odcloud cond[...] syntax for filters. However, it does not explicitly state whether the operation is read-only or mention rate limits. Given the lack of annotations, the description is fairly transparent but could be more explicit about safety (though the context suggests a read operation).
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 sections for purpose, examples, differentiation, parameters, authentication, and returns. It front-loads the main purpose. However, it is somewhat lengthy (about 15 lines) but necessary due to the tool's complexity. A slight reduction in detail could improve conciseness without losing clarity.
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?
The description covers input parameters, output summary (total_count, items, etc.), authentication, and sibling tool differentiation. The output schema exists but is not detailed in the description; the 'Returns' section provides a reasonable overview. Given the parameter count (7) and complexity, the description is complete enough though it could touch on error handling or pagination behavior more explicitly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, meaning no property descriptions. The description compensates by providing detailed explanations for each parameter in the 'Args' section, including their mapping to API fields and the meaning of stat_kind choices (e.g., 'reqst_area': 지역별 청약 신청자). This adds significant semantic value beyond the schema's empty property definitions.
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 returns subscription statistics (requests, winners, rates, scores) with specific verb 'Return' and resource 'subscription stats'. It provides Korean keywords and example queries. It explicitly distinguishes from sibling tool 'get_apt_subscription_info' which handles schedules, making the purpose unambiguous.
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 gives explicit usage guidance with three Korean query examples, stating 'Use this tool when the user asks...' and also provides an alternative for schedules ('For schedules... use get_apt_subscription_info'). This clearly indicates when to use this tool versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description fully handles transparency. It details return items (including error handling) and implies read-only behavior. Could mention rate limits or required permissions, but the existing detail is strong.
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?
Well-organized with clear sections (main description, keywords, notes, args, returns). Every sentence adds value. Front-loaded with main purpose. No wasted words.
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?
Given the tool's moderate complexity and presence of an output schema, the description is exceptionally complete. It explains all parameters, return structure with fields and summary, and provides usage tips. Covers edge cases and error handling.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema lacks parameter descriptions (0% coverage). The description compensates fully: explains region_code as a 5-digit code from get_region_code, year_month in YYYYMM with a fallback suggestion, and num_of_rows with default. Adds meaning beyond schema.
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 clearly states it returns row-house and multi-family sale records for a region and month. Includes Korean keywords and distinguishes from sibling tools by specifying housing type (villa, row-house, multi-family).
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?
Provides context on when to use (budget-conscious buyers) and references sibling tools for prerequisites (get_region_code, get_current_year_month). Does not explicitly exclude alternatives but offers sufficient guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It details return structure including error handling and possible fields. However, it does not explicitly state that the tool is read-only or mention rate limits, which would improve transparency.
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?
Description is well-structured and front-loaded with the main purpose. It is slightly lengthy but every sentence adds value. Could be slightly more concise, but remains efficient.
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?
Given the complexity (3 parameters, output schema exists but no nested objects), the description is very complete: covers arguments, return structure, usage with sibling tools, and error handling. No gaps remain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must add meaning. It fully explains each parameter: region_code as a 5-digit code from get_region_code, year_month format YYYYMM with a fallback instruction, and num_of_rows with default 100.
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 'Return apartment lease and monthly-rent records for a region and month.' It also provides Korean keywords and distinguishes from siblings by referencing 'get_apartment_trades' for computing the jeonse ratio.
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 says 'Use this alongside get_apartment_trades to compute the jeonse ratio' and gives a formula. Also instructs to call 'get_current_year_month' if the user doesn't specify the year_month.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It describes the return structure and possible error conditions, but does not explicitly state that the operation is read-only or disclose rate limits or authentication needs. However, the read nature is implied through the language of 'return' and 'summary'.
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 clear sections, but slightly verbose. It front-loads the core purpose and adds valuable usage tips. Every sentence serves a purpose, but could be tightened slightly without losing 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?
Given the tool's moderate complexity, the description fully covers purpose, parameters, usage patterns, and expected output. It even provides query strategies for common analyses. The output schema is described, so the agent knows what to expect.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description has a detailed 'Args' section that explains each parameter's purpose, format, and default behavior (e.g., year_month in YYYYMM, hint to use get_current_year_month, num_of_rows default 100). This adds significant meaning beyond the bare schema.
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 it returns apartment sale records and summary statistics for a region and month, with specific verbs and resource. It distinguishes from sibling tools like get_apartment_rent and get_officetel_trades by focusing on trades.
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?
Provides explicit guidance on using summary.median_price_10k as reference price, how to compute jeonse ratio via get_apartment_rent, query strategies for trend analysis and year-over-year changes, and prerequisite to call get_region_code first.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description discloses the multiple-match behavior and return fields, though it omits potential error handling or auth requirements. Still, it provides good transparency for a simple conversion tool.
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 well-structured with Args and Returns sections, concise yet complete, with no wasted words.
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?
Given the tool's simplicity (one parameter, output schema), the description fully covers purpose, usage, input format, handling of multiple matches, and return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema parameter 'query' has no description (0% coverage), but the tool description compensates fully by explaining it accepts free-form text and providing examples like '마포구', '서울 마포구'.
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 converts user-supplied region names to a 5-digit legal district code for the MOLIT API, which is distinct from sibling tools that handle calculations, trades, or rents.
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 states 'Must be called before any trade or rent tool' and provides guidance on handling multiple matches by showing the matches array and confirming with the user.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but description covers behavior: returns records filtered by region and month, includes error/message field for failures. Missing details on rate limits or authentication, but still transparent about core functionality and return structure.
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?
Well-structured with 'Args' and 'Returns' sections, front-loaded with purpose statement. Every sentence adds value; no wasted words. Appropriate length for complexity.
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?
Output schema exists and description lists return fields in detail. Covers error handling. Given 3 parameters and sibling context, description is fully complete for correct tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description provides full burden. It explains region_code as '5-digit legal district code (returned by get_region_code)', year_month as YYYYMM format with suggestion to use get_current_year_month, and num_of_rows with default 100. Adds crucial meaning beyond schema.
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 states specific verb+resource: 'Return row-house and multi-family lease/rent records for a region and month.' Clearly distinguishes from sibling tools like get_apartment_rent and get_officetel_rent by specifying property type and providing Korean keywords.
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 says 'Use alongside get_villa_trades to compute villa jeonse ratio and evaluate rental investment yield.' This gives clear context for when to use and mentions a companion tool. Includes Korean keywords to clarify property scope.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description fully carries the burden. It accurately describes the behavior: returning a string in YYYYMM format. There are no hidden side effects, permissions, or safety concerns to disclose for this simple read-only operation.
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 concise at ~60 words, with the main purpose stated first. Every sentence adds value: purpose, usage guideline, and return format. No redundancy or wasted words.
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?
For a simple tool with no parameters and an output schema, the description is complete. It explains the return format and when to use it. No additional information is needed for an agent to effectively invoke and understand the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, so baseline is 4. The description adds value beyond the empty schema by explaining the output format and contextual usage, justifying a score of 5.
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 specifies the verb 'Return', the resource 'current year and month', and the output format 'YYYYMM'. It explicitly states the tool's purpose as a utility for trade/rent tools, distinguishing it from sibling calculation and data retrieval tools.
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 states when to use this tool: 'Call this tool when the user asks about current or recent transactions without specifying a year_month.' This provides clear guidance and implies the alternative (use year_month from user input) without needing explicit sibling differentiation.
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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