Server Details
Texas Foundation Repair Cost: the site's own MCP server — enquiry (enquiry = a human handoff,...
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Available Tools
3 toolsenquiry_describeWhat you get: an ENQUIRY with a human (not a purchase, not a guaranteed quote)AInspect
Read first. States plainly what submit_enquiry does on Texas Foundation Repair Cost: it starts an enquiry with human providers who quote directly. Nothing is bought, ordered or paid; no quote is guaranteed; it is free. Also returns who receives the details, the consent wording, and how the person confirms.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure and does so excellently. It explicitly states that nothing is bought, ordered, or paid, that no quote is guaranteed, that the service is free, and what information is returned.
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 compact and front-loaded with the critical guidance 'Read first.' Every sentence earns its place: it explains the tool's purpose, explicitly negates purchase/payment/guarantee assumptions, and summarizes the returned content without redundancy.
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 zero-parameter descriptive tool with no output schema and no annotations, the description is fully self-sufficient. It tells the agent what the tool does, what it doesn't do, and what information will be returned, leaving no critical gap.
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 has zero parameters, so the schema fully covers the parameter space by definition. The description adds meaningful context about what the tool returns and its non-action nature, exceeding the baseline expectation for a parameterless tool.
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 identifies the tool as an explanatory companion to submit_enquiry, stating that it 'describes what submit_enquiry does' rather than performing the submission. It is immediately distinguishable from submit_enquiry (which actually starts an enquiry) and enquiry_fields (which likely handles field details).
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?
'Read first' explicitly signals that this tool should be used before interacting with submit_enquiry, giving the agent clear context for when to call it. It does not spell out exclusions or compare against enquiry_fields, but the intended usage is evident.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
enquiry_fieldsThe questions the enquiry asksAInspect
Every field of the Texas Foundation Repair Cost enquiry: key, label, type, whether required, help text and the allowed options where there are any. Pass answers to submit_enquiry keyed by field key.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of explaining behavior. It discloses what the tool provides: field metadata including allowed options where present, and it connects the result to the submit_enquiry workflow. It does not explicitly state that the operation is read-only or free of side effects, but the metadata-focused content strongly implies it.
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 compact sentences with no filler. It front-loads the core scope, enumerates the relevant field attributes, and ends with a practical, actionable instruction. Every sentence earns its place.
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 absence of an output schema, the description does a good job specifying what the response contains: field keys, labels, types, required flags, help text, and allowed options. It also situates the tool within the larger enquiry workflow. A small gap is the lack of explicit context about how enquiry_describe differs, but this does not prevent correct invocation.
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 has zero parameters and the input schema is empty, so the description has no parameter semantics to add. The baseline for zero parameters is 4, and the description usefully clarifies that the returned field keys are the keys to use when calling submit_enquiry.
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 identifies the tool as returning every field of the Texas Foundation Repair Cost enquiry along with attributes like key, label, type, and required. It lacks an explicit verb such as 'lists' or 'returns', and it does not distinguish itself from the sibling enquiry_describe beyond the implied focus on fields.
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 actionable usage guidance: pass answers to submit_enquiry keyed by the field key. This makes the intended workflow clear. However, it does not explicitly state when to use this tool versus enquiry_describe or provide exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
submit_enquirySubmit an ENQUIRY to human providers (two steps; not a purchase)AInspect
Submits an enquiry to Texas Foundation Repair Cost — NOT a purchase, NOT a guaranteed quote. Step 1: call with the answers (keyed by field key from enquiry_fields) and consent=true; it validates and returns a summary, the consent line and a confirmation token — show the person the summary and the consent line. Step 2: only if the person agrees, call again with the same answers, consent=true and the confirmation token; the enquiry is then submitted, and the person receives an email with a link they must click before any provider sees it. Consent means the person has read and agreed to: "Happy for my details to go to Texas foundation repair companies, who'll contact me directly."
| Name | Required | Description | Default |
|---|---|---|---|
| answers | Yes | the person's answers, keyed by field key | |
| consent | Yes | true only when the person has agreed to: Happy for my details to go to Texas foundation repair companies, who'll contact me directly. | |
| confirmation | No | the confirmation token from step 1, after the person has approved the summary |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden, and it does so excellently. It discloses the two-step stateful behavior, validation and returned artifacts (summary, consent line, token), the requirement for explicit person agreement, and that the enquiry is only visible to providers after the person clicks the emailed link. It also supplies the exact consent wording.
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 front-loaded and dense, with no filler. It is somewhat long, but the complexity of the two-step flow and the need to state the exact consent line justify the length. Minor redundancy with the title's 'two steps; not a purchase' is acceptable and reinforcing.
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 no output schema and no annotations, the description fully compensates by explaining both step outputs, the required sequencing, the consent condition, and the post-submission email verification flow. An agent has enough information to call the tool correctly through both steps.
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?
Even though schema coverage is 100%, the description substantially enriches parameter meaning. It explains that answers must be keyed by field key from enquiry_fields, that consent must be true, and that the confirmation token is the artifact from Step 1 required only in Step 2. This maps each parameter to the exact phase of the workflow.
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 opens with a specific verb and resource: 'Submits an enquiry to Texas Foundation Repair Cost'. It immediately clarifies what the tool is not ('NOT a purchase, NOT a guaranteed quote') and that it is a two-step process, which clearly distinguishes it from a simple submission tool.
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 step-by-step invocation guidance: Step 1 with answers/consent, then Step 2 with the confirmation token only after approval. It references enquiry_fields for constructing keys, which helps route the agent to the sibling tool. However, it does not explicitly state when to avoid this tool or directly contrast it with enquiry_describe/enquiry_fields beyond that reference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
3 tool updates
- First observed
enquiry_describe - First observed
enquiry_fields - First observed
submit_enquiry
Frequently Asked Questions
Claiming 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 asio.github.acme/serveralso 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.jsonon 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.
Discussions
No comments yet. Be the first to start the discussion!
Related MCP Connectors
Oklahoma Foundation Repair Cost: the site's own MCP server — enquiry (enquiry = a human handoff,...
Missouri Foundation Repair Cost: the site's own MCP server — enquiry (enquiry = a human handoff,...
Texas Water Damage Cost: the site's own MCP server — enquiry (enquiry = a human handoff, not a...
Texas HVAC Replacement Cost: the site's own MCP server — enquiry (enquiry = a human handoff, not...
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceMCP server for qualifying and responding to inbound leads in seconds using a multi-agent AI pipeline.1MIT
- AlicenseAqualityCmaintenanceMCP server that helps users identify which trade to call during home emergencies by analyzing natural-language problem descriptions and providing ranked matches with costs, sources, and dispatch lines.3MIT
- AlicenseNot gradedqualityCmaintenanceMCP server providing 12 tools for Seattle-area home remodeling: real-time cost estimation across 8 project types, contractor business info, project portfolio, blog content, and quote submission. Connects via Streamable HTTP — no auth required.MIT
- AlicenseAqualityCmaintenanceMCP server for a production-shaped service-business agent, enabling grounded knowledge search, intake collection, and human escalation with deterministic safety boundaries.3MIT
Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
Each tool has a clearly distinct role: enquiry_describe provides the plain-language overview, enquiry_fields provides the exact field schema, and submit_enquiry handles the actual two-step submission. There is no functional overlap between them.
The names are readable and share the 'enquiry' concept, but the pattern is mixed: enquiry_describe and enquiry_fields use an enquiry_ prefix, while submit_enquiry uses a verb-first convention. This makes the naming slightly inconsistent even though it is not confusing.
Three tools is a well-scoped set for this simple enquiry workflow: orientation, field definitions, and submission. Each tool earns its place and there is no redundancy.
For the stated purpose of submitting an enquiry, the set covers everything needed: understanding the process, retrieving the required fields, and performing the validated two-step submission with consent. There are no obvious gaps or dead ends.