Food Hygiene Alerts: the site's own MCP server — checker, enquiry (enquiry = a human handoff,...
Given a question id and the chosen option (its choice index), return the next question or the final verdict.
| Name | Required | Description | Default |
|---|---|---|---|
| choice | Yes | ||
| question | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full disclosure burden. It discloses the return behavior (next question or final verdict) but does not state that submitting a choice advances or mutates the check state, whether answers are reversible, or how invalid indices are handled. For a flow-advancing tool, this is a notable 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?
A single 21-word sentence with no filler. The conditional structure ('Given X and Y, return Z') front-loads the essential input-output contract and every clause 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?
With no output schema or annotations, the description should carry workflow context; it hints at a sequence (answer → next question or verdict) but never states that a check must be started via `checker_start`, how the returned question relates to prior ones, or what a final verdict contains. Adequate for a simple invocation, but incomplete for navigating a multi-tool flow.
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%, and the description compensates by defining `question` as a question id and `choice` as the index of the chosen option. This adds meaning beyond the bare schema properties (string, integer with minimum 0), though it does not explain where valid options come from or what an invalid index yields.
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 action: given a `question` id and a `choice` index, return either the next question or the final verdict. It clearly identifies the resource (the answer step of a check) and its role in a flow, though it never explicitly names the siblings it differs from.
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?
No guidance is given about when to use this tool instead of `checker_start` or `checker_tree`. An agent must infer that answering happens after a check has been started, but there are no explicit conditions, exclusions, or references to alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The first question of the Which food hygiene position are you in? decision guide.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden of disclosing behavior. It only says 'first question' and does not explain what the tool returns, whether it advances state, what the user sees, or whether it requires any prior steps. This is minimal behavioral disclosure.
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 concise sentence that is easy to parse. However, it largely restates the title, which limits the added value, though it remains clear and 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 decision-guide entry point with no output schema and no annotations, the description is too thin. It does not say what the agent should do with the response, how to proceed to checker_answer, or what the user should expect. An agent likely needs more context to invoke and handle the tool 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 tool has zero parameters and 100% schema coverage, so the description adds no parameter details and none are needed. Baseline for 0 params is 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 states the tool presents the first question of the 'Which food hygiene position are you in?' decision guide, which clearly identifies its role and resource. It does not explicitly distinguish itself from siblings like checker_answer or checker_tree, but the phrase 'first question' implies the entry point.
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?
No when-to-use guidance or alternatives are given. It is implied that this is the starting point of the decision guide, but the description does not say when to use it versus checker_answer or checker_tree, nor does it describe how to proceed after the first question.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Every question, option and verdict of the Which food hygiene position are you in? guide, for reasoning end to end.
| 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 must carry the behavioral disclosure burden. It does communicate that the tool returns the complete set of questions, options, and verdicts for end-to-end reasoning. It does not, however, disclose the output format, size, or how the tree is structured, leaving some behavioral uncertainty.
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 efficient sentence that front-loads the key facts: every question, option, and verdict, the specific guide, and the purpose. There is no filler 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 parameterless informational tool with no output schema, the description is mostly complete: it states coverage and intended reasoning purpose. A minor gap is the lack of detail about the output representation or traversal structure, but the sibling names and simple nature of the tool reduce the need for more.
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?
This tool has zero parameters, so the input schema is trivially complete and the baseline is 4. The description appropriately adds content-level meaning rather than parameter-level details, which are unnecessary here.
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 as the complete 'Which food hygiene position are you in?' decision tree and names its contents: every question, option, and verdict. The phrase 'whole' and 'end to end' helps distinguish it from the stepwise checker_start/checker_answer siblings, though it does not name them explicitly.
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?
Usage is implied rather than stated: an agent can infer this tool is for full end-to-end reasoning over the whole guide, versus a single step in the checker flow. However, the description does not explicitly say when to prefer checker_tree over checker_start or checker_answer, nor does it list any exclusions.
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 Food Hygiene Alerts: 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?
No annotations are provided, so the description carries the full burden, and it meets it well: it states that nothing is bought, ordered, or paid, that no quote is guaranteed, that the enquiry is free, and that the tool returns recipient, consent, and confirmation details. This gives an agent a clear behavioral and side-effect profile without any structured annotation support.
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 directive 'Read first,' then moves immediately into the core action. Each sentence contributes either a behavioral boundary or return-value detail, though the title and opening sentence do overlap somewhat.
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 covers the essential ground: purpose, side-effect profile, and the contents of what is returned. It falls short of a 5 because it does not explicitly specify when in the workflow to call this tool, and the recipient details are named only generically.
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 and schema description coverage is 100%, so there is no parameter meaning for the description to add. With no parameters present, the baseline of 4 is appropriate.
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 uses the specific verb 'States' with a clear object: what submit_enquiry does, making this tool's role as an explanatory, pre-submission resource obvious. The title and body also explicitly distinguish an ENQUIRY from a purchase or a guaranteed quote, which separates it from siblings like submit_enquiry.
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' signals that this tool should be consulted before taking action, and the 'not a purchase / not a guaranteed quote' framing provides useful exclusions. However, it never explicitly tells the agent when to pick this tool over named alternatives such as submit_enquiry or enquiry_fields, so the routing guidance remains implied rather than explicit.
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 Food Hygiene Alerts 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. It discloses what the tool returns and how the result should be used, but does not state whether the operation is read-only, whether there are auth requirements, or how the output is structured. This is adequate but not richly transparent.
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, each earning its place. The first focuses on the output contents, the second on downstream usage. No filler or redundant restatement.
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, read-only metadata retrieval tool, the description fully covers what is needed to call it correctly: what the response contains and how to use it with submit_enquiry. No output schema exists, so explaining the return content here is essential, and the description does exactly 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 baseline is 4. The description adds meaningful semantics by explaining that the returned field keys are the correct keys for submit_enquiry payloads, which gives the agent actionable knowledge beyond the empty schema.
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 ('Every field of the Food Hygiene Alerts enquiry') and enumerates the attributes returned (key, label, type, required, help text, allowed options). It lacks an explicit verb like 'lists' or 'returns', but the meaning is unambiguous. The final sentence also distinguishes it functionally from submit_enquiry.
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 usage context: the returned field keys should be used when passing answers to submit_enquiry. It implicitly tells the agent when this tool is relevant, though it does not explicitly contrast it with sibling tools like enquiry_describe or checker_*.
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 Food Hygiene Alerts — 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 food safety consultants, 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 food safety consultants, 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 carries full behavioral disclosure. It reveals the two-step validation flow, the requirement to show consent line, the confirmation token, the email link click before provider visibility, and the exact consent wording. This is exceptionally transparent for a multi-step submission tool.
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?
Though longer than average, it is dense and every sentence serves a purpose: purpose, step 1 behavior, step 2 behavior, consent definition. The two-step structure is front-loaded and logically ordered, avoiding wasted words while delivering all necessary procedural detail.
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 output schema, no annotations, and a nested object parameter, the description is remarkably complete. It covers the input source, return values from step 1, the second-call condition, the email-link behavior, and the consent requirement. Nothing critical for invoking the tool 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 meaningful usage semantics: 'answers keyed by field key from enquiry_fields', 'confirmation' as the step-1 token used only in step 2, and consent's exact meaning. This goes beyond the schema's static parameter 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 states the specific action ('Submits an enquiry to Food Hygiene Alerts') and reinforces what it is not ('NOT a purchase, NOT a guaranteed quote'). It clearly differentiates from the sibling enquiry_fields tool by referencing it as the source of field keys, making the tool's role unambiguous.
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 two-step usage instructions: first call with answers and consent=true to get a summary/token, then a second call after person approval. It also states the when-not ('NOT a purchase') and implicitly guides the agent to use enquiry_fields for key names, which covers alternatives and conditions well.
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.
checker_answerchecker_startchecker_treeenquiry_describeenquiry_fieldssubmit_enquiryClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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Each tool has a clearly distinct role: checker_start begins the guide, checker_answer advances it, and checker_tree provides the full map for reasoning. The enquiry tools are similarly separated into describe, fields, and submit, with no meaningful overlap.
The two prefixes checker_ and enquiry_ create clear grouping, but suffix patterns vary: start/answer are verbs while tree is a noun, and describe/fields/submit mix verb and noun forms. This is readable but not fully consistent.
Six tools is well-scoped for the server's two purposes: a decision guide and an enquiry submission flow. Each tool is necessary and none feel redundant.
The checker workflow is fully covered with start, step-by-step answer, and full tree access. The enquiry flow is also complete, including field metadata, explanation, and the two-step consent-confirmed submission process.