site
Server Details
Leave Tracker: the site's own MCP server — calculator, enquiry (enquiry = a human handoff, not a...
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Available Tools
5 toolscalculateStatutory leave trackerAInspect
Run the Statutory leave tracker calculator: Statutory leave for the leave year; Leave accrued in hours, at 12.07% of hours worked; Of that, filled by bank holidays your employer counts; Days left for the worker to choose. Missing inputs fall back to their documented defaults.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | How are the hours arranged? | regular |
| taken | No | Days of leave already taken | |
| booked | No | Days booked but not yet taken | |
| averageDay | No | Length of a normal working day, in hours | |
| daysPerWeek | No | Days worked in a normal week | |
| hoursWorked | No | Hours actually worked so far this leave year | |
| bankHolidaysCounted | No | Bank holidays your employer counts inside the 5.6 weeks |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It does disclose useful behavior: missing inputs fall back to documented defaults, and it outlines the calculation outputs. It does not state whether the tool is side-effect free or describe error/validation behavior, leaving some behavioral uncertainty for a pure calculator.
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 action is front-loaded and the output list is compact. The semicolon-separated list is slightly dense but every element contributes useful information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a calculation tool with fully described optional parameters and no output schema, the description names the key computed outputs and the default-fallback behavior, which is enough to invoke it. It does not specify the exact response shape or calculation rules for statutory leave beyond the 12.07% formula, but the schema covers input expectations.
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 schema already documents all 7 parameters. The description only adds a general 'missing inputs fall back to defaults' note and references hours worked and bank holidays; it does not add material meaning beyond the parameter descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the resource (Statutory leave tracker calculator) and the action (Run), and enumerates what it computes: statutory leave, accrued hours at 12.07%, bank-holiday fill, and remaining days. It does not explicitly contrast with sibling calculator_describe, but the intended calculation role 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?
The instruction to run the calculator implies use when a statutory-leave calculation is needed, and the mention of defaults gives practical context. However, it never states when not to use it or points to alternatives such as calculator_describe, 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.
calculator_describeWhat Statutory leave tracker 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?
There are no annotations, so the description carries the burden. It tells the agent what content is available (inputs with units/ranges/defaults, outputs, assumptions/tables) but does not explicitly state that the tool is read-only, returns documentation text, or performs no calculation. Still, the name and empty schema make this low-risk.
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 compact noun phrase with no filler, front-loading the inputs and then listing outputs and assumptions. Every element 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 simple description tool with no parameters and no output schema, the definition covers the key content areas. It could be more explicit that this tool returns documentation rather than performing a calculation, but nothing critical is missing at this 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 input schema is empty with 0 parameters, so the baseline is 4. The description adds value by stating that input units, ranges, and defaults are covered, which orients the agent to lookup purposes, even though those inputs are not this tool's own 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 identifies the artifact as covering the calculator's inputs, outputs, assumptions, and tables, and the title frames it as 'what the Statutory leave tracker computes.' This distinguishes it from the sibling calculate tool, though it lacks an explicit operational verb such as 'returns' or 'describes.'
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 when-to-use or when-not-to-use guidance is stated. It is only implied that this tool should be consulted before calling calculate to understand the calculator's inputs and outputs; it does not name alternatives or exclusions.
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 Leave Tracker: 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 and meets it well. It explicitly states what does NOT happen ('nothing is bought, ordered or paid'), clarifies cost ('it is free'), and reveals the return contents (who receives details, consent wording, confirmation method). This is strong behavioral disclosure.
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 three focused sentences with no wasted words. It front-loads the 'Read first' instruction, then quickly covers what the tool does, what it does not do, and what it returns. 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?
Given the tool's low complexity (zero parameters, no output schema), the description provides enough context: it explains its purpose, its subject (submit_enquiry), its safety profile ('free', 'nothing is bought'), and the categories of information returned. It could be slightly more explicit about its own return format, but this is not a significant gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and an empty input schema, so there are no parameter semantics to document. The baseline for zero-parameter tools is 4, and the description appropriately focuses on behavior and return value 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 states a specific action ('states plainly what submit_enquiry does') and names the exact resource and scope. It clearly distinguishes this from the related 'submit_enquiry' tool by explaining this is the read-first explanation, not the submission action itself.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The opening 'Read first' gives clear contextual guidance to use this tool before taking action. It also identifies the target behavior (submit_enquiry) and what the tool will clarify. However, it does not explicitly discuss alternatives like enquiry_fields or when not to use this tool.
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 Leave Tracker 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 full burden of behavioral disclosure. It describes the content returned and implies a read-only listing, but it does not explicitly state that the tool has no side effects or that it only returns static field definitions. This is adequate but not fully explicit.
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 pack all essential information: what is returned and how to use it. There is no filler, redundancy, or unnecessary detail. The most useful information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter, no-output-schema tool, the description is complete. It enumerates the output fields and gives explicit downstream usage instructions. An agent knows exactly what to expect and how to act on it without needing more context.
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 100% schema coverage, so the baseline is 4. The description adds no parameter details because there are none, but it does explain how the output should be used to construct parameter keys for submit_enquiry.
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 what the tool provides: every field of the Leave Tracker enquiry with its key, label, type, required flag, help text, and allowed options. The title 'The questions the enquiry asks' reinforces the resource, and the phrase 'Pass answers to submit_enquiry keyed by field key' differentiates it from the submit_enquiry 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?
It gives explicit workflow guidance: after retrieving fields, pass answers to submit_enquiry keyed by field key. This tells the agent how to use the output in relation to a specific sibling. It does not mention when to use it versus enquiry_describe or calculate, but the primary alternative is addressed.
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 Leave Tracker — 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 leave and absence software suppliers, 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 leave and absence software suppliers, 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 burden of behavioral disclosure. It explains the two-step validation/submission flow, the email link the person must click before providers see anything, and the exact consent wording. It also explicitly disclaims purchase and guaranteed quote 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?
The description is long but densely informative, organized by Step 1 and Step 2. Every sentence earns its place, and key warnings are front-loaded ('NOT a purchase, NOT a guaranteed quote').
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 lacking an output schema and annotations, the description tells the agent what to expect (summary, consent line, confirmation token), what to do with it, and the required next action. It also covers the consent requirement and privacy-critical email-link step, making it complete for a two-step submission tool.
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 already 100%, the description adds meaning beyond the schema: answers are keyed by field key from enquiry_fields, consent has the exact consent text, and confirmation is the token returned from step 1 and is only used after the person approves.
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 Leave Tracker.' It immediately distinguishes the tool from a purchase or guaranteed quote, and the two-step workflow clearly separates it from the sibling calculator/describe 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: call first with answers and consent=true, show the summary, and only call again with the confirmation token if the person agrees. It also tells the agent when not to proceed and what consent means.
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
Leave Loading Calculator: the site's own MCP server — calculator, enquiry (enquiry = a human...
Payroll Dates: the site's own MCP server — calculator, enquiry (enquiry = a human handoff, not a...
Notice Period Calculator: the site's own MCP server — calculator, enquiry (enquiry = a human...
Employer Cost Calculator: the site's own MCP server — calculator, enquiry (enquiry = a human...
Related MCP Servers
- FlicenseBqualityCmaintenanceMCP server for managing employee leave requests, balances, and types with natural language, using a dummy in-memory database.6-
- FlicenseNot gradedqualityCmaintenanceEnables natural-language-based employee leave management including leave balance checks, leave applications, approvals, and history retrieval through an MCP-compatible client.-
- FlicenseBqualityDmaintenanceA Model Context Protocol server that enables users to manage employee leave through natural language. It provides tools to check leave balances, apply for leave, and view leave history via Claude integration.3-
- AlicenseAqualityCmaintenanceManages employee leave with Claude Desktop via natural language. Enables checking balances, applying, approving, and reviewing leave requests through a Supabase-backed MCP server.10MIT
Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
Each tool targets a distinct aspect: running the calculator, describing calculator inputs/outputs, explaining the enquiry flow, listing enquiry fields, and submitting an enquiry. The only mild ambiguity is between the two '_describe' tools, but their descriptions clearly separate calculator metadata from enquiry process/schema.
The names are readable and consistently lowercase snake_case, but they mix conventions: 'calculate' and 'submit_enquiry' put the action first, while 'calculator_describe' and 'enquiry_describe' put the noun first with a verb suffix, and 'enquiry_fields' has no verb. A consistent verb_noun pattern would improve predictability.
Five tools is well-scoped for this server's two related purposes: the leave calculator and the enquiry submission flow. Each tool earns its place, including the metadata and instruction tools that prevent misuse.
The tool surface fully covers the apparent domain: calculate provides the calculation, calculator_describe explains its contract, and enquiry_fields/enquiry_describe/submit_enquiry cover the two-step consent and submission lifecycle. There are no obvious dead ends or missing operations.