site
Server Details
Energy Audit Cost: the site's own MCP server — enquiry (enquiry = a human handoff, not a...
- 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 Energy Audit 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?
No annotations are provided, so the description carries the burden. It implies a read-only informational role ('states', 'returns') and details what the output contains, but it does not explicitly declare that the tool has no side effects, does not require permissions, or is non-mutating. For a describe tool, the read-only nature is implicit but not stated outright.
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 with 'Read first' and covers the essential points in a few sentences. It is slightly verbose with redundant phrasing ('Nothing is bought, ordered or paid; no quote is guaranteed; it is free') but remains efficient and well-structured for its purpose.
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 no input schema, no output schema, and is a simple describe operation, the description is sufficiently complete. It explains what the tool does, what it returns, and how it relates to submit_enquiry. It does not mention error conditions, but for an informational tool this is not a 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 baseline for parameter semantics is 4. The description does not need to explain parameters and does not add anything beyond that, which is appropriate 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 explicitly states the tool's purpose: it 'States plainly what submit_enquiry does' on Energy Audit Cost. It distinguishes itself from siblings by being a read-first informational tool, not the submission tool itself, and it enumerates the key points (free, no purchase, no guaranteed quote) that clarify its scope.
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 begins with 'Read first,' which clearly implies this should be used before calling submit_enquiry. It also clarifies what the tool is not (not a purchase, not a guaranteed quote), but it does not explicitly state when not to use it or compare it to enquiry_fields. The usage guidance is strong but not fully explicit about alternatives.
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 Energy Audit 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?
No annotations are provided, so the description carries the full burden. It implies a read-only retrieval of field metadata but does not explicitly state that it has no side effects or that it does not require authentication. The mention of submit_enquiry is a usage hint rather than a disclosure of this tool's behavior. A 3 is appropriate because the description is not misleading but lacks explicit behavioral guarantees.
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 concise sentences. The first sentence defines the tool's output completely, and the second provides a direct usage hint. No redundant wording, and the key information is 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 zero-parameter retrieval tool with no output schema, the description adequately covers the content of the returned fields and how to use them with submit_enquiry. It does not explicitly state the response format (e.g., array of objects), but that is easily inferable. The absence of pagination or error details is minor for such a simple 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?
The tool has zero parameters, so the schema provides no parameter information. Per the rubric, a baseline of 4 applies. The description adds no parameter semantics because there are none; it focuses on the output structure, which is acceptable given the lack 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 returns every field of the Energy Audit Cost enquiry, specifying the exact attributes (key, label, type, required, help text, allowed options). It also names the sibling submit_enquiry and implies this tool is for retrieving field definitions, distinguishing it from the 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 provides clear context: this tool is for fetching field definitions, and it explicitly advises passing answers to submit_enquiry keyed by field key. However, it does not mention the alternative enquiry_describe or state when not to use this tool, leaving some ambiguity about sibling differentiation.
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 Energy Audit 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 relevant local energy assessors, who'll quote 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 relevant local energy assessors, who'll quote 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, the description carries full burden. It discloses the non-purchase/non-guaranteed nature, the two-step requirement, consent meaning, email link prerequisite, and exact consent wording. This is exceptional 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?
Front-loaded with the key distinction ('NOT a purchase'), then logically structured step 1 and step 2. Every sentence earns its place; the length is justified by the tool's complexity.
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?
Despite no output schema, the description explains exactly what step 1 returns (summary, consent line, token) and step 2 outcomes (email link, provider visibility). Complete for an agent to execute the two-step flow correctly.
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%, but the description adds crucial flow context: answers keyed by field key, consent must be true only when agreed, and confirmation token from step 1 used in step 2. This goes well beyond schema descriptions.
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 purpose: submitting an enquiry to human providers, explicitly noting it is not a purchase or guaranteed quote. It distinguishes from siblings (enquiry_describe, enquiry_fields) by being the submission step, and explains the two-step nature.
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?
Provides explicit step-by-step instructions: step 1 call with answers and consent=true to get summary/consent/token, then step 2 call only if person agrees with same answers, consent=true, and token. It also specifies when to use each step and that provider only sees after email link click.
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
EPC Upgrade Cost: the site's own MCP server — enquiry (enquiry = a human handoff, not a...
Commercial EPC Cost: the site's own MCP server — enquiry (enquiry = a human handoff, not a...
Three Phase Cost: the site's own MCP server — enquiry (enquiry = a human handoff, not a...
Site Investigation Cost: the site's own MCP server — enquiry (enquiry = a human handoff, not a...
Related MCP Servers
- AlicenseAqualityBmaintenanceCloud cost management MCP server for Azure. Ask your AI about your cloud bill.15281MIT
- AlicenseAqualityBmaintenanceUK due diligence MCP server — Companies House, corporate research, compliance checks193MIT
- FlicenseNot gradedqualityDmaintenancePaid remote MCP for hosted MCP server providing structured receipts, usage logs, and audit-ready evidence for agent and CI workflows.-
- AlicenseNot gradedqualityDmaintenanceAnalyzes Azure cloud costs, audits for waste, and provides budget insights via natural language through a secure local MCP server.2MIT
Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
Each tool has a clearly distinct role: describe explains the process, fields provides the schema, and submit_enquiry handles submission. There is no overlap in purpose or confusion about which tool to call.
All names use lowercase snake_case and share the 'enquiry' prefix, making them easy to group. Minor inconsistency exists because enquiry_describe and enquiry_fields are noun-first while submit_enquiry is verb-first, but this remains readable and predictable.
Three tools are exactly right for this narrow domain: one for orientation, one for input schema, and one for the actual submission. Each tool earns its place and there is no bloat.
The tool set fully covers the enquiry workflow: understanding the process, retrieving the fields, validating and submitting with consent, and handling the confirmation step. No obvious lifecycle gap exists for the stated purpose.