deal-locator-mcp

by syleedlabs

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a distinct and well-defined purpose. resolve_address handles address parsing, deal_card_search retrieves the latest deal card for a specific parcel, deal_history provides full transaction history, match_explain details matching logic, area_scan offers area-level statistics, and deal_card_create generates a PNG card. There's no functional overlap.
Naming Consistency5/5
All tool names follow a consistent verb_noun snake_case pattern (e.g., resolve_address, deal_card_search, area_scan). The naming is predictable and clearly describes each tool's action.
Tool Count5/5
With 6 tools, the server covers the core workflows of a real estate deal locator: address resolution, individual deal lookup, history, matching explanation, area scanning, and card generation. The count feels appropriate for the domain and avoids bloat.
Completeness4/5
The tool set covers essential operations for locating commercial property deals in Seoul, including address lookup, detailed parcel info, history, matching explanation, and area-wide scanning. A minor gap is the lack of a direct search by specific building name or address beyond the initial resolve, but area_scan with road filter partially addresses this.
Average 4.1/5 across 6 of 6 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 40 commits in the last 12 weeks
- No stable releases found
- 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.
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnly, openWorld, idempotent, and non-destructive hints. The description adds that the output contains comparison data in structuredContent, which gives some behavioral context beyond annotations, but does not detail other traits like rate limits or authentication.
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 relatively concise but uses technical jargon without explanation. It front-loads the purpose but misses parameter information, making it less efficient in providing complete understanding.
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 tool has an output schema and annotations, the description covers the high-level purpose and output content. However, it fails to explain the parameters, which are essential for correct invocation, leaving significant gaps.
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?
The input schema has two parameters (months, address) with schema_description_coverage 0%. The description does not explain the meaning or usage of either parameter, forcing the agent to infer from context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool shows the basis of matching, comparing anchor values vs transaction row values, utterance stage, and parcel set. It is specific about what it does and mentions use for verification/audit, but does not explicitly distinguish from sibling tools like deal_card_search or deal_history.
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 usage guidance by stating it is for verification/audit of estimated matching and recommends checking before citing in reports. However, it does not specify when not to use the tool or mention alternatives among siblings.
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?
Annotations indicate read-only, idempotent, non-destructive. Description adds: scope (Seoul commercial office), months default, and instruction to read structuredContent for accurate data. No contradiction; description adds useful behavioral context beyond 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?
Description is very concise with three short sentences covering purpose, scope, parameters, and a critical usage instruction. No fluff; every sentence earns its place.
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 the output schema exists, description does not need to explain return values. It sufficiently covers scope, parameters, and data usage (structuredContent). Could mention output language or potential pagination, but overall complete for a tool with rich annotations and schema.
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 2 parameters with 0% coverage. Description explains months range (1-60) and default, and that address is a land lot (지번). Adds value beyond schema but could be more detailed on address format or other constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it provides matched real transaction history for a land lot, newest first, with confidence, and specifies scope: Seoul commercial office sales. It distinguishes itself from siblings by its specific data and usage instruction to read structuredContent for trends, but does not explicitly contrast with sibling 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 guidance on when to use: for recalculating trends, use structuredContent.transactions[], not text. Also specifies months range (1-60) and default 24. Does not explicitly state when not to use or alternatives, but the structuredContent instruction is a clear guideline.
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?
Annotations already indicate read-only, idempotent behavior. The description adds context about initialization delay, empty deals in district mode, stats computation from raw data, and reliance on structuredContent for coverage. No contradiction with annotations.
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 well-organized with paragraphs and examples, but it is somewhat verbose. The core purpose is front-loaded, but some explanations (e.g., mode details) could be condensed without losing clarity.
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 (two modes, multiple filters, output schema with coverage), the description covers all essential aspects: usage scenarios, edge cases, performance characteristics, and how to interpret results. It is sufficiently complete for proper usage.
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?
With 0% schema coverage, the description must entirely explain parameters. It covers area, road_contains, min/max_eok_per_pyeong well with examples. However, limit and months are not explicitly described, leaving some ambiguity.
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's purpose: retrieving commercial building market prices by district or neighborhood in Seoul, with two distinct modes (구/동). It distinguishes itself from sibling tools like deal_card_search by focusing on area-based aggregation rather than individual deals.
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 explains when to use each mode (district vs. neighborhood) and when to rely on stats vs. deals for accurate averages. It warns about first-query slowness and limitations to '통건물' only. However, it lacks explicit comparison to sibling tools like deal_history or match_explain.
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?
The description significantly supplements annotations by disclosing that the first call can take tens of seconds to minutes, data is cached for 15 minutes, and structuredContent should be used for calculations with status-dependent fields. No contradiction with annotations.
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 concise with no wasted words, front-loads the main purpose, and provides necessary details in a single paragraph. Could be slightly more structured but remains effective.
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 the presence of an output schema, the description adequately covers purpose, usage, parameter range, and behavioral notes. It does not address error handling but is sufficient for the tool's complexity.
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?
With 0% schema coverage, the description explains the months parameter range (1-60) but does not elaborate on the address parameter format or other constraints. Partial compensation for missing schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides a real estate comprehensive card for a land parcel number in Seoul commercial business use, listing specific outputs like transaction price, average price, sell/buy, and reliability. While purpose is specific, it does not explicitly distinguish from sibling tools like deal_card_create.
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 specifies the geographic and property type limitation (Seoul commercial business only) and mentions first call may be slow with caching after 15 minutes. It does not explicitly exclude alternatives but provides clear context for when to use.
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?
The description transparently discloses key behaviors: creation of a PNG (mutation, consistent with readOnlyHint=false), confidence badge rendering, photo requirement causing PHOTO_MISSING failure, and restriction against promotional wording. This adds context beyond the annotations (which only hint at non-readonly and open-world).
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 moderately concise; all sentences add value but the second paragraph could be trimmed. It is front-loaded with the main purpose and key constraints. Minor redundancy in explaining the photo failure mode.
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 5 parameters, a non-trivial operation, and the existence of an output schema, the description covers the essential workflow, failure modes, and output type. It lacks explicit return value documentation but the output schema fills that gap. Months parameter explanation is missing, slightly reducing completeness.
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?
With 0% schema description coverage, the description must explain parameters. It does well for address, eyebrow, and allow_no_photo, but months and photo are only implicitly mentioned (months as '기간 한정', photo as required). The description partially compensates but leaves some parameters insufficiently explained.
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's purpose: creating a PNG card (4:5 ratio) with real transaction data from a land lot number. It distinguishes itself from sibling tools like deal_card_search and deal_history by specifying the one-to-one mapping from address to card image.
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 provides explicit when-to-use guidance: recommending use of match_explain before presenting the card, handling photo missing by asking user for photo path or allow_no_photo=true, and prohibiting assertive phrases like '최고가/최저가' in the eyebrow. Also notes the one-time prerequisite 'playwright install chromium'.
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?
Goes beyond annotations by explaining a key behavioral nuance: lot_structure='조회실패' means unconfirmed, not single parcel. Also explains backtracing capability. No contradictions with 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?
Very concise: purpose, examples, usage guidance, and a critical behavioral note—all in 4 lines. Front-loaded with key 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 presence of an output schema, the description adequately covers input format, usage context, and a notable return value nuance. No gaps for the intended functionality.
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?
Despite 0% schema description coverage, the description provides examples and format hints for the query parameter, adding essential meaning. Could be enhanced with more detail on allowed characters or structure.
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 resolves address strings into components and checks parcel composition, with concrete examples. It distinguishes from siblings by recommending it as a prerequisite when address is ambiguous.
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 'call this first before using other tools when address is ambiguous'. Lacks explicit when-not scenarios, but the guidance is clear and actionable.
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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