HelloTime MCP server for workforce management — time tracking, attendance, productivity, payroll and timesheets.
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Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 4.1/5 across 8 of 8 tools scored. Lowest: 3.4/5.
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.
country_supportAInspectReturn 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.
feature_searchAInspectFree-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.
list_competitorsAInspectReturn 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_featuresBInspectList 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, the description carries full burden for behavioral disclosure. It is a read-only listing operation, but the description doesn't state that it's non-destructive, nor does it mention the ability to filter by plan/category via the input parameters. It doesn't contradict annotations (none exist), but it adds minimal behavioral context beyond the tool name.
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 primary purpose. The parenthetical enumeration of feature categories is long but serves to clarify scope. No wasted words, though the structure could be slightly cleaner.
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 listing tool with two optional filters and no output schema, the description provides adequate clarity on what the tool returns (a list of features), but it omits mention of the filter parameters and the return format. Given the simplicity, it falls slightly short of 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?
The input schema provides full descriptions for both parameters (plan and category), covering 100% of the schema. The description's parenthetical feature list aligns with the category enum but adds no additional semantic value beyond what the schema already states.
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: 'List HelloTime features' with a specific resource (HelloTime features). It also enumerates feature categories, distinguishing it from sibling tools like feature_search and 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—call to get a list of features—but provides no explicit guidance on when to use this vs. feature_search or list_plans. Sibling tools exist but no alternatives or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_plansAInspectList HelloTime pricing plans (Free, Attend, Track, Pro, Business) with launch + list prices per region, plus volume and annual prepay discounts. Free is permanent for teams up to 5 employees; 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 provided, the description carries the full burden of behavioral disclosure. It goes beyond a generic list by specifying that the Free plan is permanent for up to 5 employees, paid tiers include a 7-day free trial, and discounts apply to volume and annual prepay. These details help the agent understand the tool's output and nuances.
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 deliver all essential information without redundancy. The first sentence states the core purpose and content, while the second adds important qualifiers about free tier and trials. 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 list tool with no output schema and full schema coverage, the description is quite complete. It covers the main behavior and key edge cases (permanent free plan, trial duration). It does not explicitly state the default behavior when 'country' is omitted, but the schema param description covers that, so the tool is well-specified overall.
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%, both 'plan' and 'country' have clear descriptions. The tool description adds context about regions and discounts but does not elaborate on parameter usage beyond what the schema already provides, so it meets the baseline without significantly enhancing parameter understanding.
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 'List' and identifies the exact resource (HelloTime pricing plans) with key attributes such as launch/list prices, discounts, and trial details. It clearly differentiates from sibling tools like list_features or list_competitors by focusing on pricing 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 that this tool is for retrieving pricing plan information, making it easy to infer when to use it. It does not explicitly name alternatives or exclusions, but the domain coverage is distinct enough that an agent can select it appropriately among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
local_payment_methodsAInspectList 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.
payroll_capabilitiesAInspectFor 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.
statutory_ratesAInspectReturn 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.
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