Own vs Charter: the site's own MCP server — checker, enquiry (enquiry = a human handoff, not a...
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?
With no annotations, the description carries the behavioral burden. It honestly discloses the core contract — submit an answer, receive the next question or verdict — but does not disclose statefulness, side effects, or how it fits into the broader check flow.
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?
One tight sentence with no filler, front-loaded with the action (answer a question). The clause 'or the final verdict' efficiently signals the terminal condition.
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 no annotations and no output schema, the description gives enough to make a correct call but leaves the flow relationship to siblings implicit. An agent won't know when this tool ends the session versus checker_start/checker_tree, or what the 'final verdict' looks like.
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 meaningfully compensates: it reveals that `question` is an identifier (not the question text) and that `choice` is an index into options. Format and range details for question IDs are left unspecified.
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 verb (return), the resource (next question or final verdict), and the required inputs (question id, choice index). It does not explicitly distinguish itself from sibling checker tools, but the behavior is 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?
Usage is implied: you answer a question and get the next step. The description provides no explicit when-to-use or alternative guidance relative to checker_start and checker_tree, so the agent must infer the tool's role in the flow.
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 Own, fractional, jet card or charter decision guide.
| 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 carries the full burden. It states only that this is the first question, not what invoking it does such as presenting a question, initializing state, or returning choices, and it does not mention side effects or follow-up behavior.
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, compact sentence with no wasted words or repeated schema content. It is front-loaded with the tool's role and the decision-guide 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 parameterless starter tool, the entry-point context is present, and sibling names hint at the surrounding workflow. However, with no output schema and no annotations, the description should have said what the agent receives after calling or what the next step is; that 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 tool has zero parameters and the schema describes them completely, so there is nothing for the description to add. The baseline of 4 applies because no parameter documentation 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 identifies the tool as the entry point to a specific decision guide, 'Own, fractional, jet card or charter', which makes its role clear from the name and title. It does not use a strong action verb or explicitly contrast with checker_answer/checker_tree, so it stops 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?
No explicit guidance is given about when to call this tool versus checker_answer, checker_tree, or the enquiry_* siblings. The phrase 'first question' implies it should be used before the rest of the flow, but the description leaves that inference to the agent.
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 Own, fractional, jet card or charter 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 carries the full burden of behavioral disclosure. It states that the tool contains every question, option, and verdict, but it does not disclose the response format, whether this is a static read-only retrieval, or how much data will be returned. It also does not clarify whether this is the underlying data structure or an interactive step-through flow.
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 and wastes little space. The opening title 'The whole decision tree' immediately communicates scope, and the description elaborates with the specific content categories. A slightly more action-oriented opening would improve it, but it is still efficient.
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 conveys the main content of the tool reasonably well. However, it does not clarify how this tool relates to the sibling checker tools or what an agent should expect in terms of invocation flow, leaving some ambiguity about whether this returns raw data or an interactive prompt sequence.
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 there is little for the description to add to parameter semantics. The baseline for a parameterless tool is 4, and the description appropriately focuses on what the tool provides rather than on input arguments.
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 complete decision tree covering 'Own, fractional, jet card or charter' options, including all questions, options, and verdicts. It lacks a direct verb like 'retrieve' or 'return', and it does not explicitly distinguish itself from the sibling checker tools, but the scope is concretely specified.
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' implies the intended use case of walking through the entire decision tree rather than a single step. However, there is no explicit guidance about when to prefer this over checker_answer or checker_start, and no exclusions or alternative conditions are stated.
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 Own vs Charter: 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 full behavioral burden and does so well: it explicitly states that the enquiry process is free, involves no purchase/order/payment, and does not guarantee a quote. It also discloses what information will be returned, including recipient, consent wording, and confirmation mechanics.
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, front-loaded with "Read first," and every clause adds useful information: the purpose, the non-transactional nature, the free guarantee, and the returned content. No filler or redundant repetition of the tool name or 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?
For a zero-parameter tool with no output schema, the description does a good job explaining what the agent will get: recipient details, consent wording, and confirmation method. It does not define the Own vs Charter distinction, but this is likely part of the tool's output content, so the description is reasonably complete for its low complexity.
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 is empty with 100% coverage, so there is no parameter information the description needs to add. The baseline of 4 applies here because nothing is missing.
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 describes what submit_enquiry does, distinguishing between Own and Charter, and explains that no purchase or payment occurs. It also specifies the returned content (who receives details, consent wording, confirmation method), making the tool's role unambiguous and distinct from actually submitting an 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?
"Read first" is an explicit usage directive that positions this tool as the prerequisite before using submit_enquiry. The description implicitly tells the agent to consult this before acting, though it does not explicitly mention when to prefer or avoid the sibling checker/fields tools.
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 Own vs Charter 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 burden of behavioral disclosure. It does reveal what data will be returned and that the output keys feed into submit_enquiry, but it does not explicitly state that the tool is read-only, how errors behave, or what happens if the enquiry has no 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 only two sentences, front-loads the core purpose, and contains no filler or repetition. Every clause adds value, and the connection to submit_enquiry is placed at the end as a practical tip.
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, the description is sufficiently complete. It lists exactly what information will be returned and explains how that information should be used by submit_enquiry. There is no missing context that would prevent an agent from invoking 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 the schema confirms this, so the baseline is 4. The description adds no parameter details because none are needed, and its mention of using field keys for submit_enquiry is about output semantics rather than input parameters.
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 resource as 'Every field of the Own vs Charter enquiry' and enumerates the returned metadata (key, label, type, required, help text, allowed options). It is specific and actionable, though it does not explicitly differentiate itself from the sibling enquiry_describe, so it stops 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 instruction 'Pass answers to submit_enquiry keyed by field key' gives useful context about how the output should be used. However, it does not explicitly state when to use this tool instead of alternatives, nor does it mention any exclusions or prerequisites.
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 Own vs Charter — 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 relevant aviation adviser or broker, 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 relevant aviation adviser or broker, 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?
No annotations are provided, so the description carries the full burden, and it delivers comprehensively. It reveals the two-step flow, validation and return values, the requirement to display the summary and consent line, the exact consent text, the email with a click-through link, and the condition that providers see the enquiry only after that link is clicked. This is exemplary transparency.
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 but every sentence carries necessary information for the two-step consent workflow. It is front-loaded with the key caveat 'NOT a purchase, NOT a guaranteed quote' and then proceeds logically through the steps. No filler or redundant wording 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?
Given the tool's complexity, the absence of annotations, and the lack of an output schema, the description is remarkably complete. It explains what happens in each step, what step 1 returns, what the agent must show the user, what consent means verbatim, and what the user must do after step 2. An agent has enough context to execute the tool correctly end-to-end.
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 100%, so the baseline is 3. The description adds value by explaining that answers are keyed by field key from enquiry_fields, that consent must be true in both steps, and that the confirmation token comes from step 1 and must accompany the second call. This context goes beyond the raw schema definitions.
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 Own vs Charter'. It also clearly distinguishes the tool from a purchase or a guaranteed quote, making its purpose unambiguous. The title reinforces this with 'two steps; not a purchase'.
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 when-to-call instructions for both steps: call with answers and consent=true first, then call again only after the person agrees, using the same answers and the confirmation token. It also provides an exclusion: not a purchase and not a guaranteed quote. However, it does not explicitly name alternative sibling tools for other scenarios, so it falls just short of full alternative routing.
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.
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 as io.github.acme/server also 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.json on 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.
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The checker tools and enquiry tools are clearly separated by purpose: start/answer/tree handle the decision guide flow, while describe/fields/submit handle the enquiry submission flow. Within each group, each tool has a distinct role with no meaningful overlap.
Most tools follow a clear domain-prefixed naming pattern: checker_start, checker_answer, checker_tree, enquiry_describe, enquiry_fields. The one deviation is submit_enquiry, which reverses the pattern, and checker_tree/enquiry_fields use nouns rather than verbs, but overall the naming remains predictable and readable.
Six tools is well-scoped for this server's two clear workflows: a decision-tree checker and an enquiry submission flow. Each tool serves a necessary function and none feel redundant.
The checker workflow has start, step-by-step progression, and full-tree access, covering all user reasoning paths. The enquiry workflow has description, field schema, and two-step submission with confirmation, leaving no obvious dead ends or missing steps.