real-estate-data-mcp-server

Server Quality Checklist

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

Latest release: v0.1.0
Disambiguation4/5
Tools like get_housing_stats, get_cost_of_living, and get_area_demographics overlap in scope but are distinguished by their specific focus on raw housing data, affordability indices, and demographics, respectively. The descriptions clarify these differences, though potential confusion remains between similar state-level data retrieval tools.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern using underscores (e.g., get_area_demographics, compare_areas), with 'get' as the common action verb. No mixing of casing or verb styles is observed.
Tool Count5/5
6 tools is an appropriate scope for a real estate data server, covering key area-level statistics, comparisons, and nearby amenities without being excessive or sparse. Each tool has a distinct role.
Completeness4/5
The tool set covers essential area-level housing, demographic, and cost-of-living data, plus neighborhood search and amenities. Minor gaps include the absence of zip-code-level data and property-specific listings, but these are within the stated domain focus.
Average 3.8/5 across 6 of 6 tools scored. Lowest: 3.2/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 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.
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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description bears full burden. It implies a read-only operation (returns indicators) with no mention of side effects, permissions, or limitations. Minimal behavioral context beyond the operation type.
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 relatively concise with a clear main sentence and a bullet list of return items. The Args block adds minor redundancy with the schema, but overall structure is efficient and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that an output schema exists, the description adequately summarizes return content. However, it lacks guidance on how this tool fits with siblings and does not address edge cases (e.g., invalid state names), leaving completeness moderate.
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 coverage is 0%, but the description explains the 'state' parameter with format examples ('NY', 'New York'). This adds value beyond the schema, but does not provide additional depth (e.g., accepted formats, case sensitivity). Baseline 3 is appropriate.
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 states a specific verb ('Get') and resource ('housing affordability and cost-of-living indicators for a US state'), clearly indicating the tool's purpose. However, it does not distinguish itself from sibling tools like 'compare_areas' or 'get_area_demographics', which may overlap in scope.
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?
The description implies use for US states but provides no explicit guidance on when to use this tool versus alternatives. No exclusions, prerequisites, or contextual hints are given, leaving the agent to infer usage without support.
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 should disclose behavioral traits. It mentions OpenStreetMap as the data source, but does not explain API rate limits, data freshness, caching, or any limitations on results. Lacks details on behavior beyond the basic output.
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 short and front-loaded with the core purpose. It lists parameters in a clear Args block. However, it could be slightly more structured (e.g., bullet points) for readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema present, the description doesn't need to explain return values. However, it lacks context about API dependencies (e.g., rate limits, if radius_m is optional) and does not mention the return format. Adequate but not fully comprehensive for a tool with no annotations.
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 the description must add meaning. It explains lat and lon as location coordinates and radius_m with default and max values. It also lists the types of amenities returned, which are not in the schema. This provides useful context 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 the tool's purpose: find nearby amenities using OpenStreetMap. It lists specific amenity types (schools, hospitals, parks, etc.) and distinguishes itself from sibling tools like compare_areas, get_area_demographics, etc., which focus on other aspects.
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 finding amenities but does not explicitly state when to use this tool versus alternatives like search_neighborhoods or get_housing_stats. No when-not or exclusion criteria are provided.
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?
No annotations exist, so the description carries the burden. It states the tool returns data from the US Census ACS, hinting at read-only behavior, but does not disclose other traits like authentication, rate limits, or data freshness. The behavioral disclosure is minimal.
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 and front-loaded: a single sentence on purpose, followed by a bullet list of data fields and an Args section. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
An output schema exists, reducing the need to explain return values, but the description still lists them. However, it omits details on default behavior when 'county' is omitted (state vs county level), error scenarios, or geographic scope constraints, leaving some completeness gaps.
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 0%, so the description compensates by describing the 'state' parameter as US name/abbreviation with examples, and the 'county' parameter as optional 3-digit FIPS code. This adds meaningful guidance beyond the schema's bare type and title.
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 retrieves demographic data for US states or counties, listing specific data fields (population, median income, etc.). It uses a specific verb ('get') and distinguishes from sibling tools that focus on cost of living, housing, or amenities.
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?
The description does not provide guidance on when to use this tool versus alternatives like compare_areas or get_cost_of_living. It lacks explicit context for selection, leaving the agent to infer without comparative direction.
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?
No annotations provided, so description carries full burden. It discloses metrics compared and number of locations (2-5), but omits behavior on invalid locations, schema permissiveness (additionalProperties: true), and data freshness. Adequate but lacks depth.
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?
Two short paragraphs: first states core purpose, second lists metrics, third describes argument with example. Efficient, no wasted words, but could benefit from bullet points for readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers purpose, metrics, and parameter structure moderately well. Lacks guidance on state format, county FIPS range, limit enforcement, and output format (though output schema exists). Sibling tools exist but no cross-reference.
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% (no property descriptions). Description compensates well: explains locations parameter as list of dicts with required 'state' and optional 'county' FIPS, plus provides an example. Still missing exact format for state and county FIPS.
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 compares housing metrics for multiple US areas side-by-side (median home value, rent, income, population, affordability). Distinguishes from siblings by explicitly targeting multi-area comparison.
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?
Tool's purpose implies when to use (comparing multiple areas), but no explicit guidance on when not to use or alternatives. For instance, does not mention that for single-area data siblings like get_housing_stats or get_cost_of_living are more appropriate.
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?
No annotations provided; description states data source and that county is optional, but lacks details on failure modes (e.g., missing state), auth requirements, rate limits, or side effects. Adequate but not fully 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?
Description is relatively concise, but uses a Python docstring-style argument list which could be more prose-like. Still, it's clear and front-loaded with purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers core purpose and parameters well, but lacks usage guidelines and behavioral details. Since output schema exists, return values are covered. Overall adequate but could be more 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?
Schema has 0% description coverage; description adds meaning: state can be name or abbreviation with examples, county is optional 3-digit FIPS code with example. This is helpful 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?
Clearly states it retrieves housing statistics for US state/county, lists specific metrics (median home value, rent, vacancy rates, owner-occupied percentage), and identifies data source (US Census ACS 5-Year survey). Distinct from siblings like compare_areas or get_cost_of_living.
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?
Implies use case for obtaining housing stats but provides no explicit guidance on when to use this tool vs siblings like compare_areas or get_area_demographics. No 'when not to use' or alternatives mentioned.
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?
Describes the process: geocode city, find state, return counties and places with data. No annotations exist, so the description carries the burden; it does so adequately for a read-only search tool.
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?
Front-loaded with the main purpose and includes args documentation. Slightly wordy with the 'Args:' section but remains clear and efficient overall.
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 output schema exists, the description adequately covers behavioral details and parameter meaning, making it complete for a search tool with one parameter.
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?
With 0% schema description coverage, the description provides crucial format guidance ('City name with state (e.g., Austin, Texas)') that the schema lacks, fully compensating for the gap.
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 searches for neighborhoods/areas in a city with housing stats, distinguishing it from sibling tools like 'get_housing_stats' or 'get_area_demographics' which focus on specific areas.
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?
It specifies the input format (city with state) and implies usage for discovering neighborhoods, but does not explicitly state when to use this vs. alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![real-estate-data-mcp-server MCP server](https://glama.ai/mcp/servers/AiAgentKarl/real-estate-data-mcp-server/badges/card.svg)](https://glama.ai/mcp/servers/AiAgentKarl/real-estate-data-mcp-server)

Score Badge

Copy to your README.md:

[![real-estate-data-mcp-server MCP server](https://glama.ai/mcp/servers/AiAgentKarl/real-estate-data-mcp-server/badges/score.svg)](https://glama.ai/mcp/servers/AiAgentKarl/real-estate-data-mcp-server)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/AiAgentKarl/real-estate-data-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server