HelloTime MCP server for workforce management — time tracking, attendance, productivity, payroll and timesheets.
Return per-country features, default currency, and product positioning for a supported country (IN, AU, GB, US, CA, AE, SG, NZ).
| Name | Required | Description | Default |
|---|---|---|---|
| country | No | Single ISO country code. Omit for the full matrix. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool 'returns' data, which implies read-only behavior, but does not disclose details like behavior when the country parameter is omitted (though the schema covers that) or the return format. No contradictions.
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, front-loaded sentence that states the action and expected output with no filler. Every word 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 1-parameter tool with no output schema, the description covers the key purpose, return content, and supported countries. The parameter details and the option to omit for the full matrix are present in the schema, so the description is sufficiently complete.
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%: the parameter's description already explains 'Single ISO country code. Omit for the full matrix.' and the enum lists all values. The tool description adds the same country list, providing no additional semantic value.
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 ('Return') and identifies the exact resource: per-country features, default currency, and product positioning. It lists supported country codes, which distinguishes it from siblings like feature_search, list_features, and statutory_rates.
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 clearly implies when to use this tool: when you need country-specific configuration data. It does not explicitly name alternatives or exclusions, but the country-scoped context differentiates it adequately from sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Free-text search across plan features, product features, country features, payroll engines, statutory rates, competitor positioning, and local payment methods. Queries like "PF rate", "ESI threshold", "PT slab Maharashtra", "vs Truein", "Deputy alternative", "UPI cap", or "BACS payroll" surface the matching entry near the top.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results to return (default 20). | |
| query | Yes | Free-text query, e.g. "geofence clock-in", "PF rate", "ESI threshold", "PT slab Maharashtra", "vs Truein", "Deputy alternative", or "UPI payroll cap". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool is a search and that matches 'surface near the top,' hinting at ranking/fuzzy matching. However, it does not disclose ordering details, pagination, or explicitly confirm read-only behavior, which is a moderate gap.
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 well-structured sentences. The first sentence immediately states the core purpose and scope, while the second provides concrete, illustrative examples. Every word earns its place with no redundancy.
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 broad scope of the search tool, the description adequately covers what it searches and gives realistic examples. It lacks return format or pagination details, but since no output schema exists and the parameter schema is complete, the description is sufficiently complete for most agents to invoke 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?
Schema coverage is 100%: both 'query' and 'limit' have descriptive comments, including example queries for 'query'. The tool description adds no new parameter information beyond what the schema already provides, so the schema does the heavy lifting, warranting the baseline score.
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 a specific verb ('search') and enumerates the exact resource types covered (plan features, product features, statutory rates, competitor positioning, etc.), which distinguishes it from sibling list tools. Example queries further solidify the purpose.
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 usage context by listing the searchable domains, signaling it is the go-to tool for cross-cutting free-text queries. It does not explicitly state when to use it over siblings like list_features or statutory_rates, but the 'free-text search across' phrasing strongly implies it is for when you don't have a dedicated list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return competitor positioning entries (Truein, Deputy, When I Work, Connecteam, Hubstaff, Keka) with where HelloTime wins, where the competitor wins, and pricing notes. Optional country, tier (primary / secondary), and id filters.
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | Return a single competitor by id (e.g. "truein", "deputy", "when-i-work"). | |
| tier | No | Filter to head-on rivals (primary) or adjacent / segment-specific overlaps (secondary). | |
| country | No | Only return competitors whose primary market is this country, or who are also evaluated in this market. |
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 clearly frames the operation as a read-only 'Return' action and lists the output fields, but it does not explain behavior like how multiple filters combine, whether the id filter returns a single item vs an array, or the default output when no filters are applied. The absence of any side-effect disclosure is acceptable for a list operation, but richer context would improve 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 two sentences, front-loaded with the core purpose and content, then lists the optional filters. Every sentence earns its place with no filler or redundancy. It is well-structured for quick parsing by an agent.
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 read-only list calculator with three optional filters and no output schema, the description sufficiently covers what is returned, the competitors included, and the filter categories. It does not explicitly state the default behavior when no filters are applied, but 'Return competitor positioning entries' implies all entries by default, which is adequate given the tool's simplicity.
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% and baseline is 3, but the description adds meaningful context beyond the schema: it enumerates valid id values (e.g., 'truein', 'deputy') and clarifies tier semantics with 'head-on rivals' vs 'adjacent overlaps'. This helps agents map the domain and select correct values, going beyond the schema's generic string/enum 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 states the tool returns competitor positioning entries, explicitly listing the competitors (Truein, Deputy, etc.) and the exact content (where HelloTime wins, where the competitor wins, pricing notes). This specific verb-plus-resource structure distinguishes it from sibling list tools like list_features or list_plans.
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 usage context by describing the tool's purpose and optional filters, but it does not explicitly state when to prefer this over sibling tools or when not to use it. The sibling names (e.g., feature_search, list_features, list_plans) suggest different domains, yet the description offers no direct comparison or exclusion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List HelloTime features (shifts, rosters, leave types, timesheets, time tracking, productivity, GPS / geofence, biometric kiosk, payroll, invoicing, analytics, projects, reports, integrations).
| Name | Required | Description | Default |
|---|---|---|---|
| plan | No | Only return features available in this plan tier. | |
| category | No | Filter to one feature category (shifts, rosters, leave, timesheets, gps-geofence, biometric-kiosk, etc.). |
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 accurately describes a read-only listing operation with optional parameter filtering. However, it does not disclose return format, potential pagination, or error behavior, which would be helpful for a tool with no output schema.
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, well-front-loaded sentence that efficiently lists the feature categories. It wastes no words, though the list is long and could benefit from a brief summary phrase before enumeration.
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 (2 optional enum parameters, no output schema, no nested objects), the description is reasonably complete. It covers the core purpose and the breadth of what is listed. However, for a tool that might support multiple plan tiers, it could explicitly state that the full list is returned when no plan is specified.
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% and all parameters have enum constraints with descriptions. The tool description reiterates the feature list already in the schema, adding no new semantic detail beyond what's in the schema. Baseline 3 is appropriate.
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 lists HelloTime features and provides an exhaustive enumeration of feature categories. It effectively distinguishes from siblings like list_plans or feature_search by indicating this is a broad listing of all available features.
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 that this tool is for retrieving a list of feature categories, but does not explicitly contrast with sibling tools like feature_search or list_competitors. However, the sibling tool names suggest different purposes, so a clear usage context is inferable but lacks explicit when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List HelloTime pricing plans (Free, Attend, Track, Business) with the charged price and a standing list-price anchor per region, plus volume and annual prepay discounts. Free is permanent for a single user; paid tiers each include a 7-day free trial.
| Name | Required | Description | Default |
|---|---|---|---|
| plan | No | Restrict the response to a single plan tier. | |
| country | No | ISO country code. Filters prices to one country. Omit to return all 8 markets. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full responsibility. It discloses that Free is permanent for a single user and paid tiers have a 7-day free trial. It does not mention read-only nature, but the verb 'list' strongly implies it. Lacks details on rate limits or authentication, but the core behavior is well covered.
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, no wasted words. The first sentence delivers the core purpose and outputs; the second adds critical nuance (free trial, permanent free tier). Information is front-loaded and easy to parse.
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 no output schema, the description adequately explains what the tool returns: pricing details, discounts, free trial info. It covers the key aspects for a listing tool with two optional parameters. No missing critical information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds value by explaining that the response includes 'charged price and a standing list-price anchor per region' and 'volume and annual prepay discounts', which contextualizes the parameters without explicitly repeating their 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 clearly states the tool lists HelloTime pricing plans with specific details (charged price, list-price anchor, volume/annual discounts). It names the tiers (Free, Attend, Track, Business) and distinguishes from siblings like feature_search and list_competitors by focusing on pricing.
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 usage for retrieving pricing information but does not explicitly state when to use this tool versus alternatives (e.g., country_support for regional support, list_competitors for competitor comparison). No when-not-to-use or exclusion criteria are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List local bank-rail / wallet payment methods relevant to HelloTime payroll and contractor payouts (UPI, IMPS, NEFT, RTGS, BACS, FPS, Faster Payments, Interac e-Transfer, EFT, PayID, PayTo, NPP, EFT/BECS, ACH, Same Day ACH, Fedwire, RTP, WPS-SIF, PayNow, FAST, GIRO, NZ Direct Credit, etc.). Returns rail (instant / same-day / next-day / multi-day), use-cases, issuing authority, HelloTime support level, and operational notes (per-transaction caps, settlement windows, retirement timelines). Filter by country, useCase, rail, or id.
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | Return a single payment method by id (e.g. "in-upi", "au-payid", "us-rtp"). | |
| rail | No | Filter by settlement rail (instant, same-day, next-day, multi-day). | |
| country | No | Filter to one country (IN, US, CA, GB, AU, AE, SG, NZ). | |
| useCase | No | Filter by payment use-case. Defaults to HelloTime's payroll + contractor-payout scope; pass an explicit value to widen. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses that the tool returns rail, use-cases, issuing authority, support level, and operational notes, and implies read-only behavior via 'List'. It does not mention auth or rate limits, but none are critical for this read-only tool.
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 efficiently structured: purpose, return content, and filters. The long list of examples is dense but valuable, and each sentence contributes meaningful information without fluff.
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, the description enumerates exact return fields (rail, use-cases, authority, support level, operational notes) and provides expressive examples. Given the 4 optional params and no annotations, this is strong contextual coverage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds domain context with extensive examples of payment methods and clarifies filter usage, going beyond the schema's individual param 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 states the tool lists payment methods with specific scope (HelloTime payroll and contractor payouts) and gives concrete examples (UPI, IMPS, BACS). It distinguishes itself from sibling tools by focusing on payment methods, not features or plans.
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 context (payroll/contractor payouts) and explicit filter options (country, useCase, rail, id). It does not explicitly name alternative tools, but the scope is distinct enough to infer when to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
For a given country, return the supported payroll engines (e.g. AU STP2 + super, IN PF/ESI/TDS/Form 24Q, US W-2/1099) with status (live/beta/coming-soon).
| Name | Required | Description | Default |
|---|---|---|---|
| country | Yes | Required ISO country code. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full transparency burden. It discloses that the operation returns data (read-only) and specifies the status values returned, but it does not mention error handling, edge cases, or authentication requirements.
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, well-structured sentence that is front-loaded with the purpose and gives concrete examples without extraneous words. Every part of the sentence adds value.
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 simple one-parameter tool with no output schema, the description adequately explains the input, the shape of the output (list of engines with status), and possible status values. It is complete enough for an agent to select and invoke the tool 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 schema already describes the country parameter as 'Required ISO country code' with 100% coverage. The description adds context with examples but not much semantic detail beyond what the schema provides, so the baseline of 3 is appropriate.
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 function: for a given country, it returns supported payroll engines with their status. Examples (AU, IN, US) clarify the scope and distinguish it from sibling tools like country_support or statutory_rates.
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 used when needing payroll engine support information for a country, but it does not explicitly provide usage guidance, alternatives, or exclusions compared to sibling tools. Usage is inferable but not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return statutory payroll-rate entries with rate, ceiling, slab, authority, and verification status. India block (PF / EPS / EDLI / PF admin / ESI / Professional Tax by state / TDS slabs) is internally-reviewed against EPFO / ESIC / state notifications. Australia and US entries are public-source-unreviewed. Filter by country, scheme, category, state, party, verification, or id.
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | Return a single rate by id (e.g. "in-pf-employee", "au-super-guarantee-fy2526"). | |
| party | No | Filter by who pays the contribution. | |
| state | No | For India professional tax, the state name (e.g. "Maharashtra", "Karnataka", "Tamil Nadu"). Case-insensitive substring match. | |
| scheme | No | Match a scheme key like "PF", "ESI", "PT", "TDS", "SuperGuarantee", "FICA-SS", "FICA-Medicare", "FUTA", "401k", "MedicareLevy". Case-insensitive substring match. | |
| country | No | Filter to one country. IN is the comprehensively-verified block; AU and US are public-source-unreviewed. | |
| category | No | Filter by statutory category (social-security, health-insurance, income-tax, professional-tax, pension, unemployment, state-payroll-tax). | |
| verification | No | Filter by verification status. Use "verified" to restrict to internally-reviewed rates (IN PF/ESI/PT). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the verification status discrepancy between countries and mentions the verification filter. This is valuable behavioral insight for a read-only tool, though it stops short of detailing pagination or return structure.
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?
Four sentences, each providing necessary information: purpose, data quality caveats, and filter options. No filler or repetition; efficient and 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?
The description covers the return contents (rate, ceiling, etc.), data provenance differences, and filters. Without an output schema, it gives a solid overview, but does not fully specify return format or list all possible schemes, leaving some ambiguity.
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 description adds little beyond listing filter names. The schema already explains each parameter including the 'IN' country verification nuance, making the description redundant in this dimension.
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?
Clearly states the tool returns statutory payroll-rate entries with specific fields (rate, ceiling, slab, authority, verification status). This distinguishes it from sibling tools, which cover features, plans, or country support, not payroll rates.
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?
Provides useful context by explaining that India rates are internally-reviewed while AU/US are not, and lists available filters. It does not explicitly mention alternatives or exclusions, but the tool is clearly the sole option for statutory rates.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 or an account that owns the GitHub organization, then choose Claim with GitHub.
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
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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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Each tool targets a distinct resource: features, plans, competitors, country support, payroll engines, statutory rates, payment methods, and a free-text search across all of them. There is no overlap in purpose; even feature_search complements the list tools by handling ad-hoc queries.
Tool names are inconsistent: three use a 'list_' prefix (list_competitors, list_features, list_plans), while the rest are noun phrases like 'country_support', 'feature_search', 'local_payment_methods', 'payroll_capabilities', and 'statutory_rates'. There is no uniform verb_noun pattern, making names less predictable.
Eight tools is a well-scoped number for a product information server covering features, plans, competitors, countries, payroll, payments, and rates. Each tool has a clear role and the count is within the ideal range.
The tool surface covers all major categories one would expect for the HelloTime product domain: features, plans, competitor positioning, country-specific support, payroll engines, statutory rates, and payment methods. The feature_search tool further fills gaps by enabling cross-category queries, leaving no obvious dead ends.