Server Details
UAE Corporate Tax Calculator: the site's own MCP server — calculator, enquiry (enquiry = a human...
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Available Tools
5 toolscalculateUAE corporate tax calculatorBInspect
Run the UAE corporate tax calculator calculator: Taxable income brought into charge; Taxed at 0%; Taxed at 9%; Corporate tax for the period. Missing inputs fall back to their documented defaults.
| Name | Required | Description | Default |
|---|---|---|---|
| sbr | No | Electing Small Business Relief? | 0 |
| income | No | Taxable income for the tax period | |
| status | No | Which describes the business? | standard |
| revenue | No | Revenue for the tax period |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the behavioral disclosure. It usefully discloses that missing inputs fall back to documented defaults and that output includes four labeled figures. It does not explicitly state that the tool is side-effect-free or describe calculation assumptions, but the calculator nature makes this less critical.
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 dense sentence with the action front-loaded and the fallback behavior included. The redundant 'calculator calculator' and the semicolon-separated output list slightly reduce clarity, but it is still compact.
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 description lists the return values and fallback behavior, which is necessary because there is no output schema or annotations. It remains somewhat incomplete: it does not explain output format, units, or how the input parameters map to the tax rates, and no usage example is provided.
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 covers 100% of the parameters, so the baseline is 3. The description adds nothing about how sbr, income, status, or revenue influence the calculation beyond referencing their documented defaults.
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 a clear action ('Run') and resource (UAE corporate tax calculator), and it enumerates the computed outputs. It is distinguishable from sibling calculator_describe and enquiry tools even without naming them, though the duplicate 'calculator calculator' is a minor wording defect.
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 use case is implied: use this tool when you need to run the UAE corporate tax calculation. However, there is no explicit guidance about when not to use it or how it compares to calculator_describe, enquiry_describe, or submit_enquiry.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
calculator_describeWhat UAE corporate tax calculator 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 what the tool returns, and it does: inputs with units/ranges/defaults, outputs, assumptions, and tables. It does not mention response format or side effects, but for a read-only describe tool, the disclosed content is sufficient.
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 core purpose and enumerates the content categories without redundancy. Every clause earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter, no-output-schema describe tool, this is complete: the agent knows it receives an explanation of inputs, outputs, assumptions, and tables. There are no hidden requirements or missing usage constraints.
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 schema, so the baseline is 4. The description adds no parameter detail, but none is needed; it explains what the documented calculator's inputs cover.
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 exactly what the tool does: it describes the calculator's inputs (with units, ranges, defaults), outputs, and underlying assumptions/tables. This clearly distinguishes calculator_describe from its calculate sibling, which actually performs the computation.
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 understanding the calculator, but it never explicitly says when to call it instead of calculate. There is no exclusion or conditional guidance, so the agent must infer the use case from the name and sibling list.
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 UAE Corporate Tax Calculator: 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 provided, the description carries the full burden. It discloses that the tool is read-only (nothing is bought or paid), that it returns specific information (who receives details, consent wording, confirmation process), and that the underlying enquiry is free with no guaranteed quote. This goes beyond a simple 'describes submit_enquiry' and explains the tool's output and the nature of the service. It does not mention rate limits or authentication, but for a describe tool these are not critical. Slight gap: it doesn't explicitly state there are no side effects, but the content implies it.
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, front-loaded with the critical instruction 'Read first.' It then states the purpose plainly, clarifies what it is not, and enumerates the return contents. Every sentence earns its place; there is no filler or redundancy. The title also adds clarity without being excessively long.
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 simplicity (no parameters, no output schema, no nested objects), the description is complete. It explains the tool's purpose, the nature of the enquiry (free, human-provided, no guarantee), and what information the user will receive. The description covers all necessary context for an agent to decide to call this tool and to understand the result. Nothing essential 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, so the schema coverage is 100% by default. The description adds no parameter details because there are none to describe. The baseline for 0 params is 4, and the description adequately compensates by explaining what the tool returns, which is the only relevant information an agent needs. No further parameter explanation is required.
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 ('read first', 'states plainly what submit_enquiry does') and a clear resource (UAE Corporate Tax Calculator). It differentiates from siblings by explicitly contrasting with the actual submission tool and clarifying it is not a purchase or guaranteed quote. The title reinforces the purpose. This is unmistakably a describe tool for submit_enquiry.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The instruction 'Read first' clearly implies this should be used before submitting an enquiry, providing contextual timing. It also clarifies what the tool is not (not a purchase, not a guaranteed quote), which helps agents avoid misusing it as a transaction tool. However, it does not explicitly name alternative describe tools like calculator_describe or enquiry_fields, leaving some ambiguity about when to prefer this over those siblings. The guidance is clear enough but not exhaustive.
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 UAE Corporate Tax Calculator 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 disclosure burden. It discloses the return contents and the field-key mapping requirement for later submission. It does not explicitly claim read-only, but the zero-parameter, enumeration-oriented nature makes that implicit, and there is no contradiction.
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 efficient sentences: the first front-loads the complete output contents, the second states downstream usage. No filler, and each 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 tool with no output schema and no annotations, this is complete. It identifies the domain, lists every returned attribute, notes optionality and allowed options, and connects to submit_enquiry. Nothing needed to call it 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?
There are no input parameters, so per the rubric this is a baseline 4. The description adds the only relevant semantic tie: the returned field keys are what should be used to key submit_enquiry 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 names a specific resource ('UAE Corporate Tax Calculator enquiry') and a specific output ('key, label, type, whether required, help text and allowed options'). This clearly differentiates it from sibling describe tools by scope: it enumerates fields rather than describing the calculator or 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?
It gives clear usage context: retrieve the field definitions, then pass answers to submit_enquiry keyed by field key. It does not explicitly list when-not-to-use or contrast with sibling describe tools, so it stops short of a 5.
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 UAE Corporate Tax Calculator — 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 UAE corporate tax adviser, 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 UAE corporate tax adviser, 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?
No annotations exist, so the description carries full behavioral burden. It discloses that step 1 only validates and returns a token, step 2 actually submits, and an email link must be clicked before any provider sees the enquiry. It also clarifies the exact consent meaning, which is essential 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 dense but mostly well-organized, front-loading purpose and non-purchase disclaimer before the two-step procedure. The repeated consent line in both description and schema is slightly redundant, but the overall structure is efficient for the tool's complexity.
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 submission tool with no annotations and no output schema, the description fully covers prerequisites, flow, expected intermediate result (summary, consent line, token), final outcome (email with click link), and constraints. No critical caller information 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 adds significant meaning: answers must be keyed by field key from enquiry_fields, consent is defined with the exact consent line, and confirmation is explained as the step-1 token. This goes well beyond the bare schema property 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?
Description states a specific action ('Submits an enquiry to UAE Corporate Tax Calculator') and explicitly disambiguates from purchase/quote expectations. The two-step submission flow and tie-in to enquiry_fields make its role distinct from sibling tools like calculate or 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 description provides clear step-by-step guidance: call with answers and consent, show the summary, then call again with the confirmation token only if the person agrees. It does not explicitly compare against sibling tools, but the steps effectively communicate when and how to invoke this 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.
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
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
Expatriation Tax Calculator: the site's own MCP server — calculator, enquiry (enquiry = a human...
Cyprus Non-Dom Calculator: the site's own MCP server — calculator, enquiry (enquiry = a human...
Malta Tax Refund Calculator: the site's own MCP server — calculator, enquiry (enquiry = a human...
Locum Tax Calculator: the site's own MCP server — calculator, enquiry (enquiry = a human...
Related MCP Servers
- FlicenseNot gradedqualityBmaintenanceA public, no-auth remote MCP server that exposes Arc & Ledger's free tax calculators and intake funnel as tools inside AI assistants. Provides tools for IRS notice explanation, FBAR/FATCA, LLC vs S-Corp comparison, quarterly tax estimates, and more.-
- AlicenseAqualityCmaintenanceMCP server exposing UK financial adviser calculations (tax, IHT, pensions, portfolios) to any MCP-capable client.6MIT
- AlicenseNot gradedqualityBmaintenanceRemote MCP server that lets any AI agent submit Saudi Arabia tax invoices to ZATCA (Fatoora Phase 2) — the Zakat, Tax and Customs Authority national e-invoicing gateway.MIT
- AlicenseAqualityAmaintenanceModel Context Protocol (MCP) server for United Arab Emirates Electronic Invoicing (PINT AE / Peppol 5-Corner Model). Provides tools to validate, generate, and explore API specifications for FTA / Peppol interoperability.4171Apache 2.0
Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
Each tool has a clearly distinct role: calculate runs the tax computation, calculator_describe documents the calculator, enquiry_describe explains the enquiry process, enquiry_fields defines the input schema, and submit_enquiry handles submission. There is no meaningful overlap between any two tools.
The naming is readable and grouped by domain, but conventions are mixed: calculate is a plain verb, calculator_describe and enquiry_describe use noun_verb order, enquiry_fields is noun_noun, and submit_enquiry is verb_noun. A consistent verb_noun or describe_* pattern would make the set more predictable.
Five tools is an appropriate size for a focused calculator-plus-enquiry server. Each tool covers a distinct piece of the workflow and none feel redundant or decorative.
The calculator side is complete with calculate and calculator_describe, and the enquiry side has field introspection, process disclosure, and two-step submission. The only minor gap is the lack of any post-submission status or history tool, but the flow is designed around email confirmation so this is not a critical omission.