site
Server Details
Get 409A Valuation: 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 stepBInspect
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 behavioral transparency. It does disclose the core behavior—returning either the next question or the final verdict—which is helpful, but it does not explain side effects (if any) such as state mutation, whether answers are recorded, or what the final verdict format looks like.
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 input and output. Every word earns its place, and it avoids any redundancy with the schema.
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 two-parameter tool with no output schema, the description does convey the basic call flow and expected results. However, the lack of parameter semantics and behavioral details (e.g., what happens with an invalid choice, meaning of 'final verdict') leaves gaps that could cause incorrect invocation, especially given the 0% schema coverage.
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 for the lack of parameter documentation. It mentions 'question id' and 'choice index' but does not clarify that 'question' is a string identifier (not a numeric id) or how 'choice' indexes into available options. This ambiguity is significant for an agent trying to invoke the tool correctly.
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 a specific verb ('Given... return') and names the resource ('question id and chosen option') and the result ('next question or final verdict'), which clearly distinguishes it from the sibling tools like checker_start and checker_tree. However, it does not explicitly compare itself with those siblings, 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 description implies when to use the tool: when you have a question id and an option choice and need to advance to the next step. It does not state when not to use it or mention alternatives, such as checker_start for beginning a session, so the guidance is only implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
checker_startStart: Which 409A safe harbour applies?CInspect
The first question of the Which 409A safe harbour applies? decision guide.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of revealing call semantics. It only states that this is the first question, without indicating whether invocation is read-only, whether it resets or initializes state, or what the agent should expect in the response.
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 short sentence with no wasted words. It is efficiently structured, though it partly restates the title and could have used its brevity to add behavioral or output context.
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 entry point, the description gives a minimal but recognizable sense of purpose. However, because there is no output schema, the description should more explicitly describe what is returned, such as the first question text and available answers, or how the agent should proceed afterwards.
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 input schema is empty, so the schema fully documents the parameter surface. With no parameters to explain, the baseline of 4 applies and the description does not need to add parameter-level detail.
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 tool as the first question in a 409A safe harbour decision guide, so an agent can infer it is the entry point. However, it is phrased as a noun phrase rather than an action ('returns', 'starts'), and it does not explicitly differentiate it from sibling tools such as checker_tree or checker_answer.
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 on when to use this tool versus any alternative. It does not state that this is the recommended starting point, nor does it mention sibling tools for continuing the guide, such as checker_answer or checker_tree. Prerequisites and conditions for use are also absent.
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 409A safe harbour applies? 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 full burden. It discloses that the tool returns the entire set of questions, options, and verdicts in the guide, which is the key behavioral trait. However, it does not mention output format, size, or how the tree is structured, leaving some ambiguity about the exact response shape.
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 immediately communicates the tool's complete scope. It is front-loaded with the most important information and contains 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?
Given the zero-parameter schema and simple resource type (a full decision tree), the description is largely sufficient: it names the content, its purpose, and the scope of coverage. The main gap is the lack of detail about what the returned tree looks like, but this is minor for a tool whose function is straightforward.
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 100% coverage, so there is nothing for the description to add about parameter meaning. The baseline of 4 applies here because the lack of parameters makes parameter documentation trivial.
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 resource ('the whole decision tree' for the 409A safe harbour guide) and its scope: every question, option, and verdict. It is distinct from sibling tools like checker_answer or checker_start because it emphasizes full-tree coverage for end-to-end reasoning, though it does not explicitly name those alternatives.
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 phrase 'for reasoning end to end' clearly implies this tool should be used when an agent needs the complete decision tree rather than a single step or answer. It gives clear context for when to invoke it, though it does not explicitly contrast it with checker_answer or checker_start.
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)BInspect
Read first. States plainly what submit_enquiry does on Get 409A Valuation: 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 of behavioral disclosure. It does reveal the informational nature by saying 'States plainly' and 'Also returns', and it sets expectations about free, non-binding enquiries. But it never explicitly says that enquiry_describe itself is read-only and triggers no external action, which is a meaningful omission given the possible confusion with submit_enquiry.
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 concise and front-loads the main purpose, but the structure is somewhat awkward: the fragment 'Read first.' is terse and the clause 'Also returns...' lacks a clear grammatical subject. The sentence flows could be tightened and reorganized for better readability while preserving the same 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, the description conveys the core purpose and some return details. However, it does not explicitly state that calling this tool is harmless and does not submit anything, and the relationship to submit_enquiry as a pre-submission explanation is only implied by 'Read first.' This leaves room for an agent to misunderstand the tool's side effects and 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?
The tool has zero parameters and the schema coverage is 100% (vacuously), so the baseline is 4. The description adds context about the kind of content returned, which is sufficient for an agent to know what to expect. No parameter explanation is 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's role is to 'state plainly what submit_enquiry does on Get 409A Valuation', identifying it as an explanatory resource for the sibling tool. It differentiates itself by naming submit_enquiry and contrasting with purchase/quote. However, the sentence 'Also returns...' has an ambiguous subject, making it slightly unclear whether the tool itself or submit_enquiry returns that information.
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' gives an explicit usage context, implying this should be consulted before taking any enquiry-related action. It also clarifies that no purchase or guaranteed quote is involved. However, it does not reference sibling tools like enquiry_fields or checker_start, nor does it explicitly state when not to use this tool, leaving some usage guidance 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.
enquiry_fieldsThe questions the enquiry asksAInspect
Every field of the Get 409A Valuation 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?
There are no annotations, so the description carries the burden of explaining behavior. It discloses the exact return contents and the relationship between field keys and submit_enquiry. It does not explicitly state that the call is read-only or side-effect-free, but the zero-parameter metadata nature makes that clear from 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?
Two sentences with no filler. The first sentence states the tool's scope and content, and the second connects it to the next action. Every part 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?
For a zero-parameter tool with no output schema, this is complete. It explains what is returned, the attributes included, and how to use the result with submit_enquiry. Nothing essential for calling this tool 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?
The input schema has zero parameters and schema description coverage is 100%, so there is nothing for the description to add about parameters. The description appropriately focuses on the output and downstream usage instead.
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 Get 409A Valuation enquiry and enumerates exactly what each entry includes: key, label, type, required status, help text, and allowed options. It stops short of an explicit verb like 'lists' or 'returns', and it does not differentiate itself from the sibling enquiry_describe, so it misses 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?
It gives a concrete downstream instruction: pass answers to submit_enquiry keyed by field key. This implies the output should be used to construct a submission, but it never explicitly says when to choose this tool over sibling tools or when not to use it.
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 Get 409A Valuation — 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 409A valuation providers, 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 409A valuation providers, 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, the description carries full behavioral disclosure: it warns that this is not a purchase or guaranteed quote, explains that step 1 only validates, and discloses that submission triggers a customer email with a mandatory click before providers see the enquiry. It also specifies the exact consent meaning.
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 dense and step-organized, with the key non-purchase warning front-loaded. It is longer than minimal because it must encode a two-step workflow and exact consent text, but most sentences carry necessary information; a slight redundancy with the title's 'not a purchase' keeps it from a 5.
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 two-step tool with no output schema, the description covers everything needed: what step 1 returns (summary, consent line, confirmation token), what to show the person, when to make the second call, and the post-submission email-link behavior. Nested answers are explained via field keys, leaving no critical gap for an agent.
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, and the description adds meaningful semantics: answers must be keyed by enquiry_fields keys, consent must match the exact consent statement, and confirmation is the token returned from step 1. This clarifies relationships between parameters beyond the schema field names.
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 Get 409A Valuation') and immediately disambiguates it from a purchase or guaranteed quote. The title reinforces the two-step nature, and the description references enquiry_fields for field keys, making its role in the workflow clear.
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 an explicit two-step invocation sequence: first call with answers and consent=true to obtain a summary/token, second call with the token only after the person agrees. It does not explicitly compare against sibling checker tools, but it clearly states the conditions and decision point for using the tool.
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
Frequently Asked Questions
Claiming 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 asio.github.acme/serveralso 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.jsonon 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.
Discussions
No comments yet. Be the first to start the discussion!
Related MCP Connectors
409A Cost: the site's own MCP server — calculator, enquiry (enquiry = a human handoff, not a...
51Cheap 409A: the site's own MCP server — compare, enquiry (enquiry = a human handoff, not a...
61409A Providers: the site's own MCP server — compare, enquiry (enquiry = a human handoff, not a...
Business Valuation Cost: the site's own MCP server — enquiry (enquiry = a human handoff, not a...
31
Related MCP Servers
- AlicenseNot gradedqualityAmaintenanceProvides startup valuation methods with auditable calculations, readiness checks, and explanations through MCP tools.MIT

mcp-reviewofficial
AlicenseNot gradedqualityAmaintenanceAn MCP server for in-loop design review of web previews. It enables agents to submit a preview URL, receive structured findings with suggested fixes, and recheck after applying changes, while never editing code itself.1MIT- AlicenseNot gradedqualityCmaintenanceAn MCP server that provides live fair-market-value price comparisons and Ed25519 signed-receipt verification for x402/MPP services, enabling agents to sanity-check quotes and verify receipts.40Apache 2.0
- AlicenseAqualityBmaintenanceUK due diligence MCP server — Companies House, corporate research, compliance checks193MIT
Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
The two functional areas are clearly separated: checker_* tools handle an interactive decision tree, while enquiry_* tools handle the enquiry submission flow. Within each group, each tool has a distinct role: start/answer/tree for the checker, and describe/fields/submit for the enquiry.
All names are lowercase snake_case and mostly use domain prefixes (checker_ and enquiry_), which is easy to read and predict. The minor inconsistency is submit_enquiry, which reverses the domain/action order compared to enquiry_describe and enquiry_fields.
Six tools is a well-scoped count: three tools cover the interactive checklist flow and three cover the enquiry submission flow. Each tool has a clear purpose and none feel redundant.
The checker flow is complete: start begins, answer progresses, and tree provides the full decision graph for end-to-end reasoning. The enquiry flow is also complete: describe explains the process, fields provides the schema, and submit_enquiry handles both validation and final submission with confirmation.