Shop Sign Cost: the site's own MCP server — enquiry (enquiry = a human handoff, not a purchase);...
Read first. States plainly what submit_enquiry does on Shop Sign 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 | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for behavioral disclosure. It explicitly states that nothing is bought, ordered, or paid, no quote is guaranteed, it is free, and it details exactly what content 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 four short sentences, front-loaded with 'Read first,' and every sentence adds value: purpose, no-purchase clarification, free status, and returned content. No waste or 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 no-input, no-output-schema tool, the description fully covers purpose, behavior, and return content. It also ties the tool to submit_enquiry, giving an agent enough context to invoke it 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?
The input schema has zero parameters, so there is no parameter semantics to explain. Per the rubric, a zero-parameter tool receives a baseline of 4.
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 function: it 'States plainly what submit_enquiry does on Shop Sign Cost' and details what it returns. This clearly differentiates it from sibling tools like submit_enquiry itself and enquiry_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 opening 'Read first' clearly signals that this tool should be used before submit_enquiry. It doesn't explicitly mention enquiry_fields or when not to use it, but the intended context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Every field of the Shop Sign 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 | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of explaining behavior. It transparently describes what information the tool exposes and how it relates to submit_enquiry. It does not explicitly state that it is a read-only retrieval operation, but the zero-parameter signature and the descriptive content make this reasonably clear.
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, front-loaded with the core purpose, and every sentence earns its place. The first sentence enumerates the returned field metadata; the second gives actionable guidance for the related submission flow.
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 no-parameter tool with no output schema, the description sufficiently explains what will be returned and how the results should be used. It does not describe the exact response JSON shape, but the field enumeration gives an agent enough structure to proceed.
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 there is nothing for the description to explain about parameter usage. Per the baseline for no-parameter tools, this is fully adequate.
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 providing every field of the Shop Sign Cost enquiry, listing exactly what metadata is included: key, label, type, required flag, help text, and allowed options. It also distinguishes itself from submit_enquiry by explaining that answers should be passed to that sibling keyed by field key.
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 clear context: use this tool to discover the enquiry's field structure and keys before submitting via submit_enquiry. It does not explicitly contrast with enquiry_describe, but the guidance about submit_enquiry makes the intended workflow clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Submits an enquiry to Shop Sign 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 local sign makers and installers, 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 local sign makers and installers, who'll quote me directly. | |
| confirmation | No | the confirmation token from step 1, after the person has approved the summary |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It discloses the stateful two-step nature, validation, returned summary/consent line/token, the need to show the person before the second call, the email with a link, and that providers only see the enquiry after the link is clicked. This is thorough and non-misleading.
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 the core exclusion and uses numbered steps, making the workflow clear. However, it is dense and repeats the consent quote already present in the schema, making it slightly longer than strictly necessary.
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 no annotations and no output schema, the description covers the main workflow extremely well, including return values and the email-link prerequisite. It does not explain error behavior, such as what happens if validation fails or the confirmation token is missing, leaving a minor 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?
Schema coverage is 100%, so the baseline is 3, but the description adds substantial workflow semantics beyond the schema. It explains that answers must be keyed by field keys from enquiry_fields, that consent is tied to a specific consent text, and that the confirmation property is the token returned from step 1 and must be supplied on the second call.
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 begins with a specific verb and resource: 'Submits an enquiry to Shop Sign Cost.' It immediately distinguishes the tool from a purchase or guaranteed quote and clearly explains the two-step submission workflow. This makes the tool's purpose unambiguous and separates it from the sibling tools.
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 when-not usage ('NOT a purchase, NOT a guaranteed quote') and a detailed step-by-step calling procedure, including when to call again with the confirmation token. It references enquiry_fields for correct answer keying, but it does not explicitly name sibling tools as alternatives or state when to use them instead.
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.
enquiry_describeenquiry_fieldssubmit_enquiryClaiming 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!
Piling Costs: the site's own MCP server — enquiry (enquiry = a human handoff, not a purchase);...
Underpinning Costs: 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...
Business Valuation Cost: the site's own MCP server — enquiry (enquiry = a human handoff, not a...
Add one secure layer between your agents and this server.
Each tool has a clearly distinct role: one explains the process, one returns the field schema, and one performs the submission. The two-step submit behavior is documented within submit_enquiry itself, so an agent should not confuse it with the informational tools.
The tools share the 'enquiry' root and use snake_case, but the pattern is inconsistent: two are noun-first (`enquiry_describe`, `enquiry_fields`) while the action is verb-first (`submit_enquiry`). This is readable but lacks a single predictable verb_noun convention.
Three tools is appropriate for the narrow enquiry lifecycle: a process explainer, a schema provider, and a submission action. Each tool justifies its existence and there is no bloat.
The surface fully covers the intended workflow: an agent can understand the process, fetch all required fields and options, and submit with the required consent and confirmation token. No additional operations are needed for the stated Shop Sign Cost enquiry purpose.