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Business Value Now: the site's own MCP server — checker, enquiry (enquiry = a human handoff, not...
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Available Tools
6 toolschecker_answerAnswer a question, get the next stepAInspect
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 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavior. It only states the return behavior and does not say whether calling this tool records/commits the answer, advances state irreversibly, or acts as a pure lookup. Error behavior for invalid question ids or choice indices is also omitted.
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 sentence that front-loads the necessary inputs and states the output clearly. There is no filler or redundancy; every part of the sentence contributes to understanding the tool.
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 and the core invocation is adequately described, but there is no output schema and no explanation of how this step fits into the checker flow—such as that checker_start supplies the first question and checker_answer drives progression. This missing flow and return-format context make it adequate but not complete.
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 description coverage is 0%, so the description must compensate. It does add meaning by mapping 'choice' to the chosen option's index and 'question' to a question id, but it does not clarify the id format/source or explicitly state that choice is 0-based. The schema's minimum constraint hints at it, but the description leaves details implied.
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 ('return') and resource ('the next question or the final verdict'), and names the two inputs (question id and choice index). It is clear and actionable, but it does not explicitly differentiate this tool from siblings like checker_start or checker_tree, 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 clear usage condition: you use this tool when you have a question id and a chosen option. It does not mention when not to use it or point to alternatives among the sibling tools, so there is clear context but no exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
checker_startStart: Which valuation approach fits?BInspect
The first question of the Which valuation approach fits? decision guide.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
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 behavioral disclosure. It only says the tool is the first question of a decision guide, without revealing whether calling it is read-only, whether it initiates a stateful flow, or what kind of output to expect. This is minimal behavioral context.
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 short sentence and is easy to scan. It repeats the phrase 'Which valuation approach fits?' from the title, but this redundancy is minor and the sentence still earns its place by specifying 'first question' and 'decision guide.'
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 zero-parameter starter tool, the description gives adequate positioning, but it leaves key context implicit: what the agent should expect upon invoking it, whether it displays a question, and how it relates to the other checker_* tools. Since there is no output schema or annotations, the description is the sole source of this information and could be more explicit.
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 already covers this completely. The description is not expected to add parameter-level detail, and the baseline of 4 for zero parameters applies.
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 is 'the first question' of the 'Which valuation approach fits?' decision guide, clearly identifying it as the entry point to a specific workflow. It is distinct enough from siblings like checker_answer and checker_tree because 'first question' signals its sequential role, though it lacks an explicit action verb.
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 explicit: being 'the first question' suggests it should be invoked before the other checker tools, but no alternatives are named and no 'when not to use' guidance is given. An agent can infer the intended context but is not directed toward or away from any sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
checker_treeThe whole decision treeAInspect
Every question, option and verdict of the Which valuation approach fits? guide, for reasoning end to end.
| 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 of explaining behavior. It does disclose the full scope of content—every question, option, and verdict—but it does not describe the output format, whether the tool is static/read-only, or how interactive guidance tools relate to it. This is acceptable for a content-reference tool but not fully 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?
The description is a single, tightly worded sentence that immediately conveys the tool's scope and purpose. Every word earns its place, and the most important information—'whole decision tree'—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 parameterless tool with no output schema, the description sufficiently explains what the tool provides: the complete set of questions, options, and verdicts for end-to-end reasoning. It could additionally mention the response format or how it relates to the step-by-step checker siblings, but these are minor gaps given the tool's simple nature.
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 confirms this with 100% coverage. The description adds no parameter-specific meaning, but none is needed; the baseline of 4 for a parameterless tool applies.
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 the complete decision tree for the 'Which valuation approach fits?' guide, including every question, option, and verdict. The phrase 'whole decision tree' differentiates it from step-by-step siblings like checker_start and checker_answer, though it lacks an explicit verb.
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 implies the tool is for reasoning through the entire guide end to end, which signals when it is useful. However, it does not explicitly state when to choose this tool over sibling tools such as checker_answer or checker_start, nor does it give exclusions or alternative conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
enquiry_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 Business Value Now: 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?
With no annotations, the description carries the full burden, and it covers the key behavioral caveats: nothing is bought, ordered, paid, or guaranteed, and the tool returns specific information (recipients, consent wording, confirmation method). It stops short of explicitly stating that enquiry_describe itself is read-only and has no side effects, but the informational framing strongly implies this.
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: 'Read first' comes first, followed by the core purpose, the key disclaimers, and the return-value summary. No sentence is redundant; each adds a distinct piece of useful information.
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, this is complete: it says what the tool does, what it returns, and the important domain caveats about purchase and quote guarantees. The sibling context reinforces that this tool is the explainer for submit_enquiry, and the description fully supports that role.
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?
There are zero parameters and schema description coverage is 100%, so there is no parameter ambiguity for the description to resolve. The baseline of 4 applies because the description does not need to add anything 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 explicitly names the resource (submit_enquiry on Business Value Now) and the verb ('states plainly' / 'describes'), and the title frames it as an informational overview. It is clearly distinguished from the sibling submit_enquiry: one explains the enquiry flow, the other performs it.
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 this tool is the preliminary step before submit_enquiry, and the rest explains what an agent will learn. It gives clear context for when to call it but does not explicitly enumerate alternatives or state 'use this instead of X,' though the sibling context makes the intent obvious.
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 Business Value Now 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 provided, the description carries the full burden of behavioral disclosure. It transparently explains what the tool returns (field metadata) and that 'allowed options' appear only where applicable. It also discloses the relationship to submit_enquiry, which is helpful. It doesn't explicitly state read-only behavior, but the descriptive nature of the content strongly implies a safe lookup with no side effects.
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 sentences with no wasted words. The first sentence enumerates the exact content returned, and the second provides a practical link to submit_enquiry. It is well-structured and focused.
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 having no output schema, the description sufficiently specifies the return content and its purpose. It names all field attributes, mentions conditional options, and explains how to use the result (keys for submit_enquiry). Given the tool takes no parameters, the description is complete for an agent to invoke and use 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 tool has zero parameters and an empty input schema, so the description need not explain parameter semantics. The baseline for 0 params is 4. The description's mention of 'field key' indirectly clarifies the meaning of a term relevant to the submit_enquiry tool, but no parameter-specific detail is required 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 tool's purpose: it provides every field of the Business Value Now enquiry, including key, label, type, required flag, help text, and allowed options. The title reinforces this as 'the questions the enquiry asks'. However, it does not explicitly differentiate itself from sibling enquiry_describe or other tools, and it lacks a direct verb like 'lists' or 'returns'.
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 actionable context: 'Pass answers to submit_enquiry keyed by field key.' This tells the agent how to use the retrieved field keys, implying the tool is a prerequisite for submission. However, it does not explicitly state when to use this tool versus alternatives like enquiry_describe, nor does it mention any exclusions or conditions.
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 Business Value Now — 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 business valuation firms, 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 business valuation firms, who'll contact 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 provided, the description carries the full behavioral burden and it does so thoroughly. It discloses the two-step stateful nature, the validation return, the consent line to show the user, the email with a click-through link, and the exact consent text. This is far beyond what the empty annotations provide.
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 long but every sentence earns its place: it front-loads the critical 'not a purchase' caveat, then uses a clear Step 1/Step 2 structure. The consent text is included exactly as required, and no redundant filler is present.
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 two-step tool with nested objects and no output schema, the description is remarkably complete. It covers the full workflow, what to show the user, when to call again, the email link requirement, and the consent condition. Nothing essential to 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%, but the description enriches the parameters by explaining their role in the workflow: answers are keyed by field key from enquiry_fields, consent must be true in both steps, and the confirmation token is the link between step 1 and step 2. This adds operational meaning beyond the schema's field 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: 'Submits an enquiry to Business Value Now' and immediately disambiguates it with 'NOT a purchase, NOT a guaranteed quote.' This aligns with the title and makes it distinct from the sibling tools, which center on checking or describing enquiries rather than submitting them.
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 context: call once with answers and consent to get a summary and confirmation token, then call again with the token only after the person agrees. It also references enquiry_fields as the source of answer keys, implying prerequisite knowledge, but it does not explicitly name alternative tools or state when not to use this one.
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.
6 tool updates
- First observed
checker_answer - First observed
checker_start - First observed
checker_tree - First observed
enquiry_describe - First observed
enquiry_fields - First observed
submit_enquiry
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TDQS
The six tools cleanly split into two domains: the checker_* tools handle the decision guide lifecycle (start, progress, full tree) and the enquiry_* tools handle the enquiry form (description, fields, submission). Each tool has a specific, non-overlapping role, and the submit_enquiry tool's two-step confirmation is clearly documented.
The tool names follow a clear prefix convention: checker_* for the decision guide and enquiry_* for the enquiry workflow. There is slight inconsistency in whether the prefix is followed by a noun or verb (e.g., checker_start vs checker_tree), but the overall pattern is predictable and readable.
With six tools, the server is well-scoped. Each tool serves a distinct purpose in one of the two workflows, and there are no redundant or filler tools.
The checker workflow is complete with start, step-by-step progression, and a full tree for end-to-end reasoning. The enquiry workflow is also complete, covering descriptive context, field schema, and a two-step submission process with explicit consent handling.