Server Details
Industrial Cleaning Quotes: the site's own MCP server — enquiry (enquiry = a human handoff, not...
- Status
- Healthy
- Uptime
- 99.4% over 38 days
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-11-25
- URL
TDQS
Scored across 3 tools
Each tool targets a distinct action: enquiry_describe explains the service and consent, enquiry_fields returns the field schema, and submit_enquiry performs the two-step submission. Although both describe and submit mention consent, their core purposes are clearly separated with no functional overlap.
The tool names mix verb placement: enquiry_describe and enquiry_fields use noun_verb order, while submit_enquiry uses verb_noun. While all are snake_case, the inconsistent verb style reduces predictability.
Three tools perfectly match the minimal workflow: one to explain the service, one to inspect fields, and one to submit. No tool feels extraneous or missing for this focused purpose.
The set covers the entire enquiry lifecycle: understanding the process, retrieving field definitions, and executing a two-step submission with consent and confirmation token. No critical gaps exist for the stated domain.
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 Industrial Cleaning Quotes: 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?
Since no annotations are provided, the description carries the full burden. It explains what the tool returns (who receives details, consent wording, confirmation method) and frames itself as descriptive of submit_enquiry, but it never explicitly states that calling this tool does not itself start an enquiry or have side effects. Given the title's wording, that omission is a meaningful gap.
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 three sentences, front-loaded with the 'Read first' instruction, and wastes little space. It could be slightly tighter since the title already carries some of the same meaning, but overall it is concise and well organized.
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 informational tool with no output schema, the description covers the essential context: what process it explains, what the user can expect, and what information it returns. It is not exhaustively detailed about output formatting, but the low complexity makes the current detail adequate.
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 schema has no properties, so the baseline is 4. No parameter-level explanation is needed, and the description correctly avoids inventing parameter guidance.
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's job: it states what submit_enquiry does, framing it as an informational description rather than the submission itself. It names the resource (Industrial Cleaning Quotes enquiry process) and the sibling it explains, but it does not explicitly contrast itself with enquiry_fields, so it falls just short of a 5.
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 phrase 'Read first' is a clear directive to use this tool before acting on submit_enquiry, which gives an agent a concrete usage cue. It does not explicitly describe when not to use it or compare alternatives, but 'Read first' provides sufficient context for a 0-parameter informational tool.
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 Industrial Cleaning Quotes 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, the description carries the burden, and it does disclose the informational nature of the call and what it returns: every field with its key, label, type, required status, help text, and options. It implies a safe read-only listing and gives no indication of side effects. It does not discuss errors or access restrictions, but for a parameterless metadata tool this is not a significant omission.
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 concise sentences with no waste: the first front-loads the tool's content, and the second gives a practical cross-reference to submit_enquiry. Every sentence adds value and the structure is easy to scan.
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, low-complexity introspection tool, the description fully enumerates the expected return content and explains how to use the returned keys. Nothing essential is missing for an agent to call the tool and act on its result.
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 accepts zero parameters, so the baseline is 4. The description correctly adds no parameter semantics because there are no parameters to document, and the schema already covers everything needed.
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 that the tool exposes every field of the Industrial Cleaning Quotes enquiry and enumerates the field metadata: key, label, type, required flag, help text, and allowed options. This clearly differentiates it from submit_enquiry, though it does not explicitly contrast it with enquiry_describe.
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 usage context: to learn the fields/keys of the enquiry, and it explicitly instructs the agent to pass answers to submit_enquiry keyed by field key. It does not state when-not-to-use cases or explicitly mention enquiry_describe as an alternative, so exclusions are only implied.
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 Industrial Cleaning Quotes — 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: "Send my contact details to up to three industrial cleaning contractors, chosen by Industrial Cleaning Quotes, who'll contact me directly. I'll be emailed each firm's name as my details go to it, and can stop any more firms getting them."
| Name | Required | Description | Default |
|---|---|---|---|
| answers | Yes | the person's answers, keyed by field key | |
| consent | Yes | true only when the person has agreed to: Send my contact details to up to three industrial cleaning contractors, chosen by Industrial Cleaning Quotes, who'll contact me directly. I'll be emailed each firm's name as my details go to it, and can stop any more firms getting them. | |
| 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 the full burden and delivers: it discloses that step 1 validates and returns a summary/consent line/token, that step 2 actually submits, and that the person receives an email with a link they must click before any provider sees the enquiry. It also spells out the exact consent semantics the agent must present.
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 step ordering is front-loaded and every sentence is functional, but the full consent sentence is quoted verbatim here and again in the consent parameter's schema description, which is redundant bulk.
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 complex, stateful two-step tool with no annotations and no output schema, the description covers the full interaction: step 1 return values, the human-approval gate, and the post-submission email-verification requirement. Nothing an agent needs to invoke it correctly 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?
Schema coverage is 100%, so the baseline is 3, but the description adds real flow meaning: answers are keyed by field key from enquiry_fields, consent must be true, and confirmation is the token returned by step 1 and used only in step 2. That maps each parameter to its role in the two-step sequence beyond the schema's own text.
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 states a specific verb and resource ('Submits an enquiry to Industrial Cleaning Quotes') and immediately bounds scope with 'NOT a purchase, NOT a guaranteed quote.' An agent can distinguish it from enquiry_fields and enquiry_describe without opening a schema.
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?
It lays out an explicit two-step protocol with the condition that selects each step: step 1 with answers+consent, step 2 'only if the person agrees' with the same answers plus the confirmation token. Nothing about when to call is left to inference.
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.
1 tool update
- Changed
submit_enquiry1 field changed- changed
Input schema / properties / consent / descriptionPrevious value: -"true only when the person has agreed to: Happy for my details to go to industrial cleaning contractors, who'll quote the work directly."New value: +"true only when the person has agreed to: Send my contact details to up to three industrial cleaning contractors, chosen by Industrial Cleaning Quotes, who'll contact me directly. I'll be emailed each firm's name as my details go to it, and can stop any more firms getting them."
3 tool updates
- First observed
enquiry_describe - First observed
enquiry_fields - First observed
submit_enquiry
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