Redfin listings, sale-comps, and neighborhood market data via natural-language queries.
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Managed credentials
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 4.1/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: search_redfin_properties finds listings, while get_property_details dives into a specific property's full record. There is no overlap or ambiguity.
Both tool names follow the same verb_noun pattern (get_*, search_*), with consistent snake_case and a clear domain prefix. The naming is predictable and uniform.
With only two tools, the server feels minimal and might be underpowered for a full real-estate workflow. The tools cover a basic search-to-detail flow, but the scope is thin compared to typical MCP servers.
The server covers the essential search-then-details pattern for property research. Missing capabilities like market comparisons or saved listings are minor gaps that an agent can work around by chaining the existing tools.
get_property_detailsARetrieve comprehensive details for a specific property from Redfin URL. Returns full description, tax history, HOA fees, walk scores, nearby schools, crime statistics, and property photos/virtual tour link. Use for due diligence, investment research, or detailed listing analysis.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Direct Redfin listing URL or property page link (e.g. 'https://www.redfin.com/CA/San-Francisco/123-Main-St') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint, so the safety profile is clear. The description adds valuable behavioral context by enumerating the returned data types (tax history, HOA fees, walk scores, etc.) and clarifying that the input must be a direct Redfin URL. It does not mention rate limits or failure modes, but the annotation coverage lowers the burden.
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 two sentences: it states the primary action, lists the return content, and identifies use cases. It is front-loaded, scannable, and contains no redundant words.
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?
Given the tool has a single well-documented parameter, read-only annotations, and no output schema, the description is largely complete by explaining the purpose, return fields, and intended use cases. It could optionally mention that Redfin data may be dynamic (openWorldHint) or that the URL must be valid, but these are minor omissions.
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?
The schema has 100% coverage for the single 'url' parameter, including a description and example. The tool description only repeats 'Redfin URL' and adds no new parameter semantics beyond what the schema already provides, so a 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 uses the specific verb 'retrieve' with an explicit resource ('comprehensive details for a specific property from Redfin URL'), making the tool's function immediately clear. It also distinguishes this tool from the sibling search_redfin_properties by focusing on a single property URL rather than searching.
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 states appropriate use cases: due diligence, investment research, and detailed listing analysis. It does not explicitly name the sibling tool as an alternative or provide when-not-to-use guidance, but the use case framing is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_redfin_propertiesASearch Redfin's real estate database for property listings by location and criteria. Returns price, street address, number of bedrooms, bathrooms, square footage, lot size, property type, and listing status. Use for residential property research, investment analysis, or market comparison.
| Name | Required | Description | Default |
|---|---|---|---|
| location | Yes | Property location by address, city, or zip code (e.g. 'San Francisco, CA', '94105', '123 Main St') | |
| max_price | No | Maximum asking price in USD (e.g. 750000 for $750k ceiling) | |
| min_price | No | Minimum asking price in USD (e.g. 250000 for $250k floor) | |
| property_type | No | Type of property to filter (e.g. 'house', 'condo', 'townhouse', 'multi-family') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint and openWorldHint. The description adds value by enumerating the exact output fields (price, address, beds, baths, sqft, lot size, property type, listing status), which helps set expectations. No contradictions.
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, front-loaded with the main action and outputs, followed by concise use cases. Every sentence earns its place, no redundancy or fluff.
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 read-only search tool with simple parameters and no output schema, the description covers purpose, returned data, and top use cases. It doesn't mention pagination, result limits, or data freshness, but the openWorldHint and overall simplicity make this acceptable. A clear and complete tool description.
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 the schema thoroughly documents the 4 parameters with descriptions and examples. The tool description does not add any information about parameters beyond what's in the schema. Baseline 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 the tool searches Redfin's real estate database and lists the returned fields (price, address, beds, etc.). It does not explicitly differentiate itself from the sibling tool get_property_details, but 'search' vs 'details' is implied. Misses the 5 because it doesn't name a sibling alternative.
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 gives explicit use cases: 'residential property research, investment analysis, or market comparison.' It does not mention the sibling tool or scenarios where another tool should be preferred, so it lacks exclusions. Clear context but no when-not-to-use guidance.
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.
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