MOT Software: the site's own MCP server — compare, enquiry (enquiry = a human handoff, not a...
The criteria and any filters of the UK garage management software for MOT stations and workshops comparison.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, yet the description discloses no behavioral traits: it does not say whether the tool is read-only, what it returns, whether it filters data, or any side effects. The description carries the full burden and fails to address 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 only one sentence, which is economical, but the phrasing is awkward and ambiguous ('of the UK garage management software for MOT stations and workshops comparison'). It is short but not clearly structured enough to convey a precise meaning.
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 no output schema and no annotations, the description needed to explain what the tool produces or how it behaves. It only names the subject matter, leaving return format, behavior, and integration with sibling tools completely unaddressed.
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 an empty schema, so there is nothing for the description to explain. The mention of 'criteria and any filters' adds slight contextual meaning, though it refers to the tool's content rather than 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 identifies the subject matter ('criteria and any filters of the UK garage management software for MOT stations and workshops comparison') but lacks an explicit verb such as 'returns', 'shows', or 'defines'. It partially distinguishes itself from siblings by naming 'criteria' vs 'options' or 'table', but does not state the action the tool performs.
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 no guidance on when to use this tool over its siblings, no context for the comparison workflow, and no exclusions or alternatives. An agent would have to infer usage solely from the tool name and title.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The options in the UK garage management software for MOT stations and workshops comparison (name, what it is best for, tags), optionally filtered by a tag value.
| Name | Required | Description | Default |
|---|---|---|---|
| filterKey | No | ||
| filterValue | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It discloses one behavioral trait: the result can be optionally filtered by a tag value, which implies a query/read operation. It does not explicitly confirm read-only behavior, what happens without a filter, or how the returned options are structured beyond the listed fields.
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 one compact sentence with no filler and front-loads the domain and resource. However, the noun-phrase grammar is structurally awkward and reads more like a definition than an instruction, which reduces clarity.
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 is simple (two optional parameters, no output schema), and the description gives the main data fields plus the optional-tag filter. It remains incomplete because filterKey is left unexplained, so an agent cannot confidently construct a filtered call without guessing.
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 0%, so the description must compensate for the undocumented filterKey and filterValue parameters. It partially does by explaining that filtering is by a 'tag value' and mentioning tags as an attribute, but it never defines what filterKey is or how the key and value combine.
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 identifies a concrete resource ('options') in a specific domain ('UK garage management software for MOT stations and workshops comparison') and lists its fields (name, what it is best for, tags). It is not a tautology, but it is written as a noun phrase rather than a clear verb statement like 'lists' or 'returns', and it does not explicitly differentiate from sibling tools such as compare_criteria or compare_table.
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 no guidance on when to use compare_options versus the sibling tools. The word 'comparison' weakly implies a role in the comparison workflow, but there are no conditions, exclusions, or alternative tool names provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each option's value on every criterion of the UK garage management software for MOT stations and workshops comparison — the full table.
| Name | Required | Description | Default |
|---|---|---|---|
| option | No | one option's name, else all |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility for behavioral disclosure. It only states the content (a full table of values) but omits any behavioral details such as output format, potential size, read-only nature, or side effects. For a tool that likely returns a large dataset, this is insufficient.
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 that directly conveys the core content without wasted words. It is appropriately concise and front-loaded with the essential information. However, it could be slightly clearer as a standalone sentence rather than a noun phrase.
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 one optional parameter and no output schema, the description provides the basic content but lacks context on how the 'option' parameter affects the result, what the criteria and options are, or how the table is structured. Given the sibling tools, more context about relationships would improve completeness, but the description is not misleading.
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 already fully describes the single parameter 'option' with 'one option's name, else all' (100% schema coverage). The tool description adds no additional meaning or context beyond what the schema provides, so it meets the baseline for high coverage but does not enhance it.
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: it provides a comparison table showing each option's value on every criterion, specific to UK garage management software for MOT stations and workshops. It distinguishes itself from sibling tools like compare_criteria and compare_options by emphasizing the full table rather than a focused view.
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 no guidance on when to use this tool versus alternatives. It does not mention any conditions or exclusions, nor does it reference sibling tools or suggest when compare_table is preferred over compare_criteria or compare_options. Agents are left to infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Read first. States plainly what submit_enquiry does on MOT Software: 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, the description carries the transparency burden, and it does a good job: it reveals that the tool 'returns who receives the details, the consent wording, and how the person confirms', and it corrects the misconception that anything is bought or that a quote is guaranteed. It does not explicitly state whether calling enquiry_describe itself has side effects, but the 'read first' framing strongly signals a safe informational call.
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 'Read first'. Each clause adds either a caveat ('nothing is bought... free') or a return-content detail. It is not overly long, though it could be tightened slightly by removing the repetition between title and 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 zero-parameter, no-output-schema informational tool, the definition is nearly complete: an agent knows why to call it, what it will convey, and what information it returns. The main gap is that it does not specify the output form (text vs structured message), but no output schema exists to supply that.
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 already covers everything for input. Per the baseline for parameterless tools, no additional parameter explanation is needed; the description adds no parameter semantics because there are none to add.
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: to explain 'what submit_enquiry does' and to return the consent wording before a user acts. The title adds that this describes an enquiry rather than a purchase, so it is not a tautology. It does not explicitly contrast against sibling tools like compare_criteria or enquiry_fields, which keeps it from 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?
'Read first' is an explicit timing cue: this should be used before proceeding with submit_enquiry. It also frames the content as the plain explanation of the submit flow. However, it does not name alternative tools or state when not to use this one, so it is clear context without full exclusion guidance.
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 MOT Software 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?
No annotations are present, so the description must carry the behavioral disclosure burden. It does so by stating exactly what the tool returns (field metadata, including the conditional 'where there are any' for allowed options) and implying a read-only, schema-inspection behavior. It does not discuss auth or error cases, but for a zero-parameter description tool this is not a major 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?
Two sentences: the first front-loads the complete list of returned attributes, and the second adds actionable usage guidance. Every sentence earns its place; no filler or repetition of the title.
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 zero parameters, no annotations, and no output schema, the description is complete enough for an agent to select and call the tool correctly. It names the resource, the full set of returned attributes, and the downstream consumer (submit_enquiry).
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 full schema coverage, so there is no parameter documentation burden. The description adds useful cross-tool semantics by instructing that the returned field keys should be used as the keys 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 resource ('the MOT Software enquiry') and enumerates what will be returned (key, label, type, required, help text, allowed options), so an agent can tell it is a field-schema/survey-definition tool. It lacks an explicit verb such as 'list' or 'fetch' and does not differentiate it from the sibling enquiry_describe, so it falls 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 description gives a concrete usage context: results should be used to pass answers to submit_enquiry keyed by field key. It does not explicitly state when not to use it or compare it with enquiry_describe, but the instruction 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 MOT Software — 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 a garage software supplier, 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 a garage software supplier, who'll contact 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?
With no annotations, the description fully carries behavioral disclosure. It reveals validation, return of a summary and consent line, the confirmation token flow, the email requirement, and that providers see the enquiry only after the person clicks the link. It also quotes the exact consent text, which is critical for correct invocation.
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 longer than average but every sentence serves a purpose, and the most important scoping information ('NOT a purchase') is front-loaded. The two-step procedure is presented in a clear, numbered, actionable structure with no filler.
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 a two-step flow, a nested answers parameter, and no output schema, the description provides complete operational context: what step 1 returns, what triggers step 2, how consent is defined, and what the user experiences after submission. 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?
Although schema coverage is 100%, the description adds meaningful semantics beyond the schema: answers must be keyed by field keys from enquiry_fields, consent must be true only after reading the quoted line, and confirmation is the token returned from step 1. This clarifies how the three parameters interrelate across the two steps.
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 ('submits an enquiry'), the target (MOT Software human providers), and immediately distinguishes this from a purchase or guaranteed quote. It also clarifies the two-step nature of the operation, making the tool's purpose unmistakable.
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 usage: call first with answers and consent=true, show the summary, then call again only if the person agrees, including the confirmation token. It also states what the tool is NOT for ('not a purchase'), which helps an agent avoid misusing it.
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.
compare_criteriacompare_optionscompare_tableenquiry_describeenquiry_fieldssubmit_enquiryClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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The compare_* tools are clearly distinct: criteria, options, and the full table each target a different aspect of the comparison data. The enquiry_* tools also separate explanation, schema, and submission. No two tools appear to do the same thing.
All names use snake_case and are grouped into compare_ and enquiry_ prefixes, which is readable and predictable. The main deviation is enquiry_describe and enquiry_fields placing the noun first, while submit_enquiry and compare_* follow verb_first, but this is minor and does not cause confusion.
Six tools is a well-scoped set for this server: three tools cover the comparison table surface and three cover the enquiry flow. Every tool earns its place with no redundant or filler tools.
The comparison domain is fully covered: users can list criteria, list options, and view the full criteria-by-option table. The enquiry flow is also complete with an explainer, field schema, and a two-step submit process including consent and confirmation.