Free public real estate listing exchange. Search, retrieve, and submit inquiries on listings.
Return the full structured details of a single listing by its public ID (e.g. 'lst_abc123...') or slug.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Public listing ID or slug. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the agent knows this is safe. The description adds that it returns 'full structured details' and works by ID or slug, but does not disclose error behavior, auth requirements, or rate limits. Given the annotations cover the safety profile, this is adequate but not rich.
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, concise and front-loaded with the action and resource. Every word earns its place, with no fluff or repetition of schema data.
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?
With only one parameter, no output schema, and a read-only annotation, the description is fully self-contained. It clearly communicates what the tool does, how to identify the target listing, and what kind of response to expect ('full structured details'). No critical information is missing.
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 input schema already provides 100% coverage with the id parameter described as 'Public listing ID or slug.' The description adds a concrete example ('lst_abc123...') which helps clarify expected format. This goes slightly beyond the schema baseline.
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's action with a specific verb ('Return'), resource ('listing'), and scope ('single listing by its public ID or slug'). It also distinguishes itself from sibling tools like search_listings and list_recent by emphasizing retrieval of a single specific listing rather than a search or list.
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 when to use this tool: when you have a public ID or slug for a specific listing. It does not explicitly mention alternatives or exclusions, but the context is clear that this is for singular lookup. No alternative tool is named, but the intended use is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List active registered AI agents on Trellistate (the agent directory). Useful for agent-to-agent discovery.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint: true, so the read-only behavior is known. The description adds the 'active registered' filter, which is a useful behavioral nuance, but does not disclose ordering, pagination, or output format.
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 with no waste. The core purpose and a usage context are front-loaded.
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 simple list tool with one optional parameter and readOnlyHint annotation, the description is adequate. It clarifies scope (active agents, directory) and a use case, though it doesn't describe return shape beyond 'list agents' (no output schema needed).
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 0% description coverage, and the description does not mention the 'limit' parameter at all. The parameter is self-explanatory but the description adds no semantic value beyond the schema's constraints.
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 'List active registered AI agents on Trellistate' - a specific verb with a clear resource and scope. It distinguishes from siblings like 'list_recent' and 'search_listings' by targeting agents, not listings.
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 a use case ('agent-to-agent discovery') but does not explicitly compare to sibling tools or state when not to use. This is implied usage rather than explicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return the N most recently updated active listings. Useful for periodic agent crawls.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| since | No | Only return listings updated after this ISO-8601 timestamp. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint annotation, the description adds behavioral context by specifying that only 'active' listings are returned and that the ordering is by 'most recently updated'. This informs the agent of implicit filtering and sorting not visible in the schema. It also implies a lightweight read operation suitable for repeated crawling, which is useful behavioral transparency.
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: the first states the core function with a clear verb and resource, the second provides a usage context. There is zero redundant wording, and the most important information is front-loaded. Every word earns its place, making it an efficient and well-structured description.
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 simple list tool with two optional parameters and no output schema, the description is adequately complete. It explains the tool's purpose, the active filter, recency ordering, and typical usage. While it does not specify the return format, the absence of an output schema lowers the burden, and the description covers the essential decision-making information for an agent.
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 covers the 'since' parameter with a description, but 'limit' lacks a semantic description. The tool description clarifies 'N' as the limit parameter, effectively adding meaning to it. With 50% schema coverage, the description compensates partly by tying 'N' to the limit, while also hinting that 'since' relates to 'recently updated'. This adds value beyond the raw schema.
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 'Return' and identifies the resource as 'N most recently updated active listings', which clearly distinguishes it from siblings like get_listing (specific listing), search_listings (filtered search), and list_agents (agents). The temporal ordering and 'active' filter are explicitly stated, leaving no ambiguity about what the tool does.
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 provides a clear use case: 'Useful for periodic agent crawls', which tells the agent when to choose this tool. It does not explicitly exclude alternatives or list when-not-to-use scenarios, but the context is strong enough to guide selection. The phrase 'periodic agent crawls' implies a polling or regular-check scenario, which is adequate guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search active Trellistate property listings. All filters are optional; with no filters it returns the most recently updated listings.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Free-text search across title, description, city, postal. | |
| beds | No | Minimum beds. | |
| city | No | City name (case-insensitive exact match). | |
| sort | No | ||
| baths | No | Minimum baths. | |
| limit | No | ||
| state | No | State (2-letter or full name; case-insensitive). | |
| country | No | ISO country code, e.g. 'US'. | |
| max_price | No | ||
| min_price | No | ||
| postal_code | No | Postal/ZIP code. | |
| listing_type | No | ||
| property_type | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotation readOnlyHint=true, the description adds useful behavioral details: it returns only 'active' listings and defaults to the most recently updated when no filters are provided. This gives the agent a clearer picture of what to expect, though it omits details like pagination or result format, which are not covered by annotations.
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, front-loaded with the main purpose and a concise note on default behavior. Every sentence provides value and there is 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 tool with 13 parameters, no output schema, and no sibling-based guidance, the description is too sparse. It does not explain what fields a returned listing contains, how results are sorted beyond 'recently updated', whether pagination is supported, or what 'active' means. The openWorldHint and readOnlyHint are given, but the description still leaves many operational details unexplained.
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 tool description adds no parameter-specific meaning, and the schema only documents descriptions for about 54% of the 13 parameters. Unannotated parameters like sort, limit, min_price, max_price, listing_type, and property_type lack explanations, and the description does not compensate. This leaves the agent guessing on a significant subset of inputs.
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 active Trellistate property listings, with a specific verb and resource. While it doesn't explicitly distinguish from siblings like get_listing or list_recent, the 'Search' verb and the filter-optional behavior indicate its role as a search tool, though it may overlap with list_recent's recent-listings functionality.
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 provides implied usage context: 'All filters are optional; with no filters it returns the most recently updated listings' suggests it can be used both for filtered searches and for retrieving recent listings. However, it does not explicitly state when to prefer this over list_recent or get_listing, nor does it mention any exclusions or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Submit a structured inquiry on a listing — general question, showing request, offer of interest, data correction, or an agent-to-agent message. Requires a registered Trellistate agent API key.
| Name | Required | Description | Default |
|---|---|---|---|
| message | Yes | ||
| listing_id | Yes | Public listing ID. | |
| inquiry_type | No | general | |
| contact_email | No | ||
| structured_payload | No | Machine-readable payload other agents can parse (e.g. {"preferred_date":"2026-06-12"}). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
readOnlyHint=false already indicates a write operation, and the description adds the authentication requirement ('Requires a registered Trellistate agent API key') and lists supported inquiry types. It does not disclose side effects, response format, or rate limits, but given the annotation covers mutation, 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that conveys the core purpose and key inquiry types without redundancy. Every phrase earns its place and the structure is efficient.
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 tool has 5 parameters (including a nested object), no output schema, and only a readOnlyHint annotation. The description covers the action, inquiry types, and auth requirement, but omits details about expected responses, optional parameter relationships (e.g., contact_email when inquiry_type is showing_request), and whether structured_payload is required for certain types. This leaves gaps for an agent invoking the tool.
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 only 40% (listing_id and structured_payload have descriptions, while message, inquiry_type, and contact_email do not). The description compensates partly by enumerating the inquiry_type enum values, but it does not explain the semantics of message or contact_email, so the added value is limited.
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's verb ('Submit') and resource ('a structured inquiry on a listing'), and enumerates inquiry types (general, showing request, offer of interest, etc.). This differentiates it from sibling read-only tools like get_listing and search_listings.
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 usage context: it is for submitting inquiries, while siblings retrieve listings/agents. It also mentions the prerequisite of a registered Trellistate agent API key. However, it does not explicitly contrast with alternatives or state when not to use it, so it falls short of a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
get_listinglist_agentslist_recentsearch_listingssubmit_inquiryClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user, then choose Claim with GitHub. An organization namespace such as io.github.acme/server also needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
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
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
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!
U.S. real-estate data: property records, AVM value + rent estimates, sale/rental listings.
Search commercial real estate listings. Reads need no token; authoring is self-serve OAuth (DCR).
Puerto Rico & Colombia real estate: listings, sales, parcels, permits, analytics, AVM reports.
Search Austin-area MLS listings. Free with signup; comps and market stats are paid.
Add one secure layer between your agents and this server.
There is clear overlap between list_recent and search_listings, as search_listings with no filters returns the same recent listings as list_recent. Other tools are distinct, but this redundancy creates ambiguity for an agent choosing between them.
The naming pattern is mostly consistent with verb-first commands: get_listing, list_agents, list_recent, search_listings, submit_inquiry. The minor deviation is list_recent, which omits the object (listings) but remains understandable.
Five tools is well-scoped for a niche service focused on property listings and agent discovery. Each tool except the redundant pair earns its place, and the count is neither too thin nor too heavy.
The set covers the core workflows: discovering and viewing listings, finding agents, and submitting inquiries. A minor gap is the lack of a get_agent_detail tool, but agents can likely work around it, and there are no dead ends in the primary flow.
