US real estate data: home prices, rents, and market trends
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 3.5/5 across 3 of 3 tools scored.
Each tool targets a distinct aspect: home prices, market indicators, and rental data. No overlap in purpose.
All tools use consistent 'get_' prefix with snake_case naming, following a clear verb_noun pattern.
Three tools is a compact but reasonable set for a focused real estate data server, covering key areas without being too thin.
Covers home prices, rental data, and major market indicators. Minor gaps like sales volume or inventory do not significantly hinder common use cases.
get_home_pricesBInspectGet US home price data. Returns national median home price (FRED MSPUS) or metro-level Zillow ZHVI data. Includes current value, YoY change, and 12-month history.
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | Data type: national or metro (default: national) | national |
| limit | No | Number of historical data points (default 12) | |
| metro | No | Metro area name (e.g. "New York, NY", "Los Angeles, CA"). If omitted, returns national data. | |
| state | No | 2-letter state code to get all metros in a state (e.g. TX, CA). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description provides some behavioral context (what data is returned) but lacks details on authorization, rate limits, error behavior, or side effects. It is adequate but incomplete.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single comprehensive sentence that efficiently conveys the tool's main features and output. It is concise without being overly terse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description lacks details on parameter interactions (e.g., how metro and state affect results) and does not explain the structure of the output. Given the tool's moderate complexity and lack of output schema, the description is insufficient for complete understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters. The description adds no additional meaning beyond the schema's brief descriptions, so baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool retrieves US home price data from specific sources (FRED MSPUS for national, Zillow ZHVI for metro) and returns current value, YoY change, and 12-month history. However, it does not differentiate from sibling tools like get_market_indicators or get_rental_data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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. The description does not mention any conditions or prerequisites for use, nor does it exclude scenarios where other tools 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.
get_market_indicatorsAInspectGet key US housing market indicators from FRED: housing starts, building permits, median home price, 30-year mortgage rate, and Case-Shiller index.
| Name | Required | Description | Default |
|---|---|---|---|
| series | No | Comma-separated FRED series IDs to filter (e.g. "HOUST,MORTGAGE30US"). Valid: HOUST, PERMIT, MSPUS, MORTGAGE30US, CSUSHPINSA. Default: all. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It discloses the data source (FRED) and the specific indicators, which is useful, but does not mention potential errors, authentication needs, rate limits, or the exact return format. For a read-only data retrieval tool, this is adequate but not exhaustive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is direct and front-loaded with the action and resource. Every word contributes meaning, with no redundant or filler content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one optional parameter and no output schema, this description is fairly complete. It enumerates the indicators and the data source. The only gap is the lack of explanation about the return structure, but given the absence of an output schema, the description does an adequate job.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% because the input schema fully describes the 'series' parameter with valid values and default. The description lists the indicators and mentions FRED, adding slight context, but does not significantly extend beyond what the schema already provides. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Get') and resource ('key US housing market indicators from FRED') and lists five distinct indicators, clearly distinguishing this tool from siblings like get_home_prices and get_rental_data which likely focus on narrower subsets.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly states the tool is for obtaining housing market indicators from FRED, providing context for when to use it. While it does not explicitly state when not to use it or name alternatives, the sibling context signals and the broad scope imply its usage over more specific tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_rental_dataAInspectGet US rental market data from FRED BLS CPI series. Returns national rent index, YoY change, and monthly history.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of historical data points (default 12) | |
| metro | No | Metro area (optional; national data returned regardless) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It describes output but fails to disclose important traits: data frequency, update schedule, limitations, or side effects. The metro parameter behavior is contradictory (optional but returns national data regardless), leaving agents uncertain.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no waste. Front-loaded with purpose. Efficiently conveys core function and output.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, so description must detail return format. It mentions rent index, YoY change, and monthly history but not structure (e.g., object fields, data types). Effect of parameters on output is unclear. Adequate but incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. Description adds little beyond schema: it repeats that limit is number of data points and metro is optional, but adds context by mentioning output components (rent index, YoY change). However, the metro description is identical to the schema and confusing.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool gets US rental market data from FRED BLS CPI series and specifies the output (national rent index, YoY change, monthly history). It distinguishes from siblings like get_home_prices and get_market_indicators by focusing on rental data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for rental data but does not explicitly state when to use it versus siblings. No guidance on when to specify metro parameter or its effect (national data returned regardless, which is confusing). No exclusions or alternatives mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
No comments yet. Be the first to start the discussion!