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Server Details
Charter vs Own: the site's own MCP server — calculator, enquiry (enquiry = a human handoff, not...
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Available Tools
5 toolscalculateCharter cost and the ownership crossoverBInspect
Run the Charter cost and the ownership crossover calculator: Hours actually billed; Hourly charges; Estimated total for this trip; Effective cost per flight hour. Missing inputs fall back to their documented defaults.
| Name | Required | Description | Default |
|---|---|---|---|
| fees | No | Landing, handling and airport fees | |
| hourly | No | Quoted hourly rate | |
| ownVar | No | Variable cost per hour of owning | |
| posHrs | No | Positioning hours billed | |
| fuelPct | No | Fuel surcharge (%) | |
| ownFixed | No | Annual fixed cost of owning a comparable aircraft | |
| annualHrs | No | Hours you fly a year | |
| flightHrs | No | Flight hours for the trip | |
| overnight | No | Crew overnight and duty charges |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry behavioral information. It usefully discloses that missing inputs fall back to documented defaults and indicates that cost figures are computed. However, it does not state whether the operation is read-only, whether any state changes occur, or what the returned result structure is.
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 front-loads the action and then lists the relevant calculated metrics without padding. The semicolon list is a little ambiguous about whether items are inputs or outputs, but overall the description is economical and appropriately sized.
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 nine-input tool with no output schema and no annotations, the description supplies only a terse list of possible outputs and the default-fallback behavior. It does not explain the ownership-crossover result, the output format, or how inputs map to outputs, leaving some interpretation gaps even though the schema fully documents the parameters.
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 full coverage with descriptions and defaults for all nine parameters, so the description does not need to re-explain them. The description adds high-level context about outputs but no parameter-specific semantics, formulas, or mappings beyond what the schema already provides.
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 names a specific action ('Run') and a specific resource (the Charter cost and ownership crossover calculator), and it lists the figures the calculator produces. It is clearly distinct from calculator_describe, though it does not explicitly contrast itself with any sibling.
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 intended use is implied: this tool executes the calculator, while calculator_describe likely describes it. However, there is no explicit when-to-use or when-not-to-use guidance, no stated alternatives, and no mention of conditions that would make a sibling tool more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
calculator_describeWhat Charter cost and the ownership crossover computesAInspect
The inputs this calculator takes (with units, ranges and defaults), the outputs it returns, and the assumptions and tables behind it.
| 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 conveys that the tool is informational and returns details about the calculator, but it does not explicitly state that it performs no calculation, has no side effects, or describe the response format.
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, compact sentence that front-loads the core content. It is efficient, though slightly awkward as a sentence fragment without a main verb.
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 describe-style tool with no output schema, the description adequately lists what the tool will explain: inputs, outputs, assumptions, and tables. It could be more explicit about what the tool itself returns, but the purpose is clear enough for invocation.
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, so the baseline is 4. The description appropriately focuses on the calculator's inputs rather than the tool's own parameters, so no parameter documentation 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 indicates the tool provides information about a calculator: its inputs, outputs, assumptions, and tables. This distinguishes it from a calculation tool, though it does not explicitly contrast it with siblings like enquiry_describe.
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 intended use is implied: an agent would call this to understand the calculator before using calculate. However, there is no explicit statement about when to prefer this over alternatives 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.
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 Charter vs Own: 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 burden and discloses both the nature of the tool (it states/returns information, not a purchase) and the specific return content: who receives details, consent wording, and confirmation method. It stops short of explicitly saying this tool itself makes no changes, but the 'Read first' and 'States plainly' phrasing make that clear enough.
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 four purposeful sentences with no filler: a directive, the core function, the key disclaimers, and the return contents. It is front-loaded with 'Read first' and every sentence 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 informational tool with no output schema, the description fully covers what the agent needs: what the tool explains, the key caveats (free, no guarantee, no purchase), and what it returns. Nothing critical is missing for correct invocation.
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, so there is nothing for the description to explain. Baseline 4 applies; no parameter documentation gap exists.
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 ('States plainly') with a specific subject ('what submit_enquiry does'), and the title reinforces the scope. It clearly positions the tool as a descriptive precursor to submit_enquiry, distinguishing it from the actual action tool and sibling enquiry_fields.
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 directive to invoke this before the action tool, and the description frames it as the explanation of submit_enquiry's behavior on Charter vs Own. It does not explicitly name when not to use alternatives like enquiry_fields, but the context is clear enough for an agent to recognize this as the informational first step.
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 Charter vs Own 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, the description carries the behavioral burden, and it covers the key behavior: the returned field metadata includes requiredness and conditional options. It does not explicitly state that the tool is read-only or describe errors, but the read-only nature is strongly implied by describing a field dictionary rather than a mutation.
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 enumerates exactly what the field metadata includes, and the second sentence connects the output to the submission flow.
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 fully specifies what the tool returns and how that return value should be used. For a zero-parameter metadata getter, nothing material is missing for an agent to call 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, so the schema is trivially complete and there is no parameter burden. The description adds useful guidance by explaining that the returned field keys are the same keys used when submitting answers.
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 precisely what the tool exposes: every field of the Charter vs Own enquiry with key, label, type, required flag, help text, and options. This clearly distinguishes it from submit_enquiry, which receives answers rather than returning field metadata.
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 clear usage context: obtain the field dictionary first, then pass answers to submit_enquiry keyed by field key. It does not explicitly enumerate when not to use it or alternative tools, but the linkage to submit_enquiry makes the intended workflow unambiguous.
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 Charter vs Own — 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 charter brokers or aircraft advisers, 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 charter brokers or aircraft advisers, 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 disclosure burden and does so thoroughly. It reveals the two-step validation-and-submit flow, the consent wording, the requirement to display the summary, and the email link requirement before providers see the enquiry. It also warns that this is not a purchase or guaranteed quote, which prevents false expectations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is longer than average but every sentence earns its place because the tool has a critical consent and two-step workflow. It is front-loaded with the key warning ('NOT a purchase, NOT a guaranteed quote') and organized into clear Step 1 and Step 2 instructions.
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 human-enquiry tool with no output schema and no annotations, the description is complete. It explains what step 1 returns, what to show the person, the exact consent line, what to pass in step 2, and what happens after submission. Nothing essential is left for the agent to guess.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Although schema coverage is 100%, the description adds critical semantic meaning beyond the schema: answers must be keyed by field keys from enquiry_fields, the confirmation token comes from step 1 after approval, and consent must equal the exact quoted consent line. This is exactly the workflow knowledge an agent needs to populate parameters 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 states a clear verb and resource: 'Submits an enquiry to Charter vs Own' and immediately distinguishes what it is not ('NOT a purchase, NOT a guaranteed quote'). This makes the tool's purpose unambiguous and separates it from purchase or quoting tools.
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 guidance: call once to validate and receive a summary/token, show the person the summary, and call again only if they agree. It also says the enquiry is not submitted until the confirmation token is provided and the email link is clicked, which clarifies when the tool should and should not be invoked. It does not explicitly name sibling alternatives, but the workflow guidance is strong enough to avoid misuse.
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.
5 tool updates
- First observed
calculate - First observed
calculator_describe - First observed
enquiry_describe - First observed
enquiry_fields - First observed
submit_enquiry
Frequently Asked Questions
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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_..."
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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.
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Claim ownership of the server listing
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Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
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For server owners:
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Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
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TDQS
Each tool has a clear role: calculate runs the calculator, calculator_describe documents it, enquiry_describe explains the enquiry flow, enquiry_fields defines the form schema, and submit_enquiry submits it. There is no functional overlap between any two tools.
Names are readable and follow snake_case, but the pattern is inconsistent: calculate is a bare verb, submit_enquiry is verb_noun, calculator_describe and enquiry_describe are noun_verb, and enquiry_fields is noun_noun. The mixed conventions are still understandable and semantically grouped.
With only 5 tools, the server is tightly scoped to two features: a calculator and an enquiry submission flow. Each tool serves a distinct and necessary purpose, and none feel redundant or missing at this scale.
The calculator tool is paired with a description tool, and the enquiry flow is fully covered: process explanation, field definitions, and two-step submission with consent and confirmation tokens. There are no obvious dead ends or missing operations for the stated domain.