Cost-of-living & quality-of-life comparison: take-home pay, equivalent salary, safety-net deltas.
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
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 4.6/5 across 6 of 6 tools scored.
Each tool serves a distinct purpose: listing cities, summarizing one city, comparing two, ranking many, looking up tax regimes, and batch safety nets. Descriptions explicitly guide when to use each, eliminating ambiguity.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., compare_cities, get_city_summary). The verbs are appropriate for the action, and there is no mixing of conventions.
With 6 tools, the count is well-scoped for a cost-of-living comparison server. It covers essential operations without being too few or too many, fitting the typical 3-15 range.
The tool set covers discovery, summary, comparison, ranking, tax regimes, and safety nets. A minor gap is the lack of a detailed single-city cost breakdown (only headline costs in get_city_summary), but overall it's fairly complete.
compare_citiesCompare two cities for a householdACompare two cities for one household: take-home pay, full cost breakdown, the equivalent target salary needed to match source net cash, non-cash lifestyle deltas (vacation, parental leave, healthcare), and a 0-100 quality score on five weighted dimensions. Use this for a head-to-head between two named cities; for a single city call get_city_summary, and to rank many cities call rank_cities. Read-only, no side effects; returns a text summary plus structured JSON. RSU income is NOT a parameter; RSU is treated as source-only because grants typically do not follow you across employers.
| Name | Required | Description | Default |
|---|---|---|---|
| living | No | Housing and spending assumptions. Omit to use defaults: rent, 2 bedrooms, typical spending. | |
| advanced | No | Optional fine-tuning: retirement contribution, age bracket, trips home, transit mode, and inbound tax regime. Omit to use defaults. | |
| household | No | Family structure: working-age partner and dependent children. Omit for a single person with no kids. | |
| source_city | Yes | City slug for the user's current city. Use list_cities to discover valid slugs (e.g. 'nyc', 'seattle', 'tokyo'). | |
| target_city | Yes | City slug for the destination city. | |
| gross_salary | Yes | User's annual gross salary in the SOURCE city's local currency (e.g. USD for Seattle, NOK for Oslo). | |
| score_weights | No | Override the composite-score weights. Defaults: financial 30, healthcare 20, vacation 15, childcare 15, safety_net 20. |
| Name | Required | Description |
|---|---|---|
| meta | Yes | |
| source | Yes | |
| target | Yes | |
| equivalence | Yes | |
| quality_score | Yes | |
| non_cash_deltas | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint. The description adds value beyond that by disclosing the RSU exclusion rule ('RSU income is NOT a parameter') and noting the output format ('returns a text summary plus structured JSON'), both of which are meaningful behavioral details not captured in annotations.
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?
Three sentences each serve a distinct purpose: summarizing outputs, giving usage alternatives, and noting a key limitation. Dense but not padded; front-loaded with the core purpose. No unnecessary repetition of schema details.
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 complexity (nested objects, 7 parameters, output schema) and strong schema/annotation coverage, the description supplies the remaining critical context: when to use, what to expect, and a domain-specific edge case. It is fully sufficient for an agent to invoke 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%, with detailed descriptions for every parameter including defaults, enums, and conditional requirements. The description contributes an extra semantic caveat about RSU income not being a parameter, which helps avoid misuse. This pushes it above the baseline 3 for fully covered schemas.
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 opens with 'Compare two cities for one household' and enumerates specific outputs (take-home pay, full cost breakdown, equivalent target salary, lifestyle deltas, quality score). It explicitly distinguishes from siblings by naming get_city_summary for single cities and rank_cities for ranking, making the tool's scope 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?
It gives direct when-to-use guidance: 'Use this for a head-to-head between two named cities' and names alternatives: 'for a single city call get_city_summary, and to rank many cities call rank_cities.' This is exactly the kind of explicit triage expected.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_city_summaryGet a one-city profileAConcise profile of one city: currency, tax shape (bracket count + top rate + payroll/national insurance), headline costs (rent / groceries / transit / childcare), safety-net values (parental leave, vacation, universal healthcare), and data freshness. Lighter than compare_cities; use when the user is asking about one place rather than a comparison. On parental leave, quote safety_net.parental_leave_summary rather than building your own sentence: some countries (Ireland, Australia, the UK, Czechia, part of Sweden) pay a flat weekly cash sum instead of a share of salary, so parental_leave_paid_pct is 0 or partial for them and stating it alone would wrongly call their leave unpaid. safety_net.avg_commute_minutes is a ONE-WAY commute, a single leg rather than a daily round trip: report it as "about N minutes each way", and double it yourself if the user asked what they spend commuting per day. It is door to door, averaged over every commuter regardless of mode, for the metro area, and excludes people who work from home. Read-only, no side effects; returns a text summary plus structured JSON.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | City slug. Use list_cities to discover valid slugs. |
| Name | Required | Description |
|---|---|---|
| tax | Yes | |
| name | Yes | |
| slug | Yes | |
| country | Yes | |
| currency | Yes | |
| safety_net | Yes | |
| cost_snapshot | Yes | |
| govt_benefits | Yes | |
| data_year_range | Yes | |
| methodology_url | Yes | |
| compensation_index | Yes | |
| inbound_tax_regime | Yes | |
| data_last_updated_field | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, but the description adds critical behavioral context beyond that: how to handle parental leave data (quoting the summary field instead of relying on percentages) and how to interpret avg_commute_minutes (one-way, door-to-door, excluding WFH). The description also explicitly denies side effects ('no side effects'), reinforcing the annotations without contradicting them.
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 paragraph of moderate length and front-loads the key purpose. Each sentence adds necessary context, but the length could be slightly trimmed without loss. Still, it is efficient given the behavioral nuances it must convey.
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?
With an output schema present and only one parameter, the description fully covers the tool's purpose, when to use it, key behavioral quirks, and guidance for agent response generation. No gap is evident 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 description coverage is 100% for the single parameter, so the baseline is 3. However, the description adds value by implying the need for a city slug validated via list_cities, which helps the agent avoid invalid inputs. It does not repeat the schema type or length constraints, earning a 4.
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 specific verbs ('profile of', 'lighter than') and explicitly names the resource ('one city'), clearly distinguishing itself from sibling tools like compare_cities. It lists the exact data categories (currency, tax shape, costs, safety-net) so the agent knows precisely what to expect.
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 explicitly states when to use this tool ('when the user is asking about one place rather than a comparison') and provides a direct alternative ('Lighter than compare_cities'). This clearly guides the agent away from using it for comparisons, with no ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_inbound_tax_regimeLook up inbound-worker tax regime for a cityALook up a city's inbound-worker tax regime, the special reduced-tax schemes for relocating workers that general cost-of-living tools do not surface. Returns regime=null when the country has no modeled regime, which is most cities. Modeled across 13 regimes in Belgium, Denmark, Germany, Greece, Ireland, Italy, Netherlands, Norway, Poland, Portugal, Spain, Sweden and the UK. Roughly half grant an actual reduction (a reduction_pct or a flat_rate) and the rest are note-only: the regime exists but grants a relocating employee nothing, so reduction_pct and flat_rate are null and the take-home is computed on ordinary tax. Each entry returns the mechanic, duration, income cap if any, and a note stating the salary floor where one exists and which eligibility conditions we do not test. Read-only, no side effects; returns a text summary plus structured JSON.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | City slug. Use list_cities to discover valid slugs. Most cities return regime=null because their country has no special inbound-worker tax regime. |
| Name | Required | Description |
|---|---|---|
| city | Yes | |
| slug | Yes | |
| regime | Yes | |
| country | Yes | |
| disclaimer | No | |
| methodology_url | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses significant behavioral details beyond the annotations, including that regime=null for most cities, the distinction between actual reduction and note-only regimes, and that reduction_pct and flat_rate are null for note-only regimes. It also states the tool is read-only with no side effects, and explains the return format (text summary plus structured JSON). This exceeds what annotations alone provide.
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 every sentence contributes essential information: the core purpose, null semantics, country coverage, regime types, and return content. It is front-loaded with the main action and progresses logically without redundancy, making it appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity, the description thoroughly covers edge cases and return values, explaining null behavior, note-only regimes, what fields are null, and what each entry contains. With an output schema present, the description still adds necessary context about the tool's limitations and eligibility conditions not tested, making it complete for correct selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single city parameter, so the baseline is 3. The description does not add additional semantics beyond the schema; it only reiterates that the tool looks up a city. The schema already provides the city slug hint and the reference to list_cities, so the description adds minimal parameter-level 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 clearly states the tool looks up a city's inbound-worker tax regime, using the verb 'look up' and specifying the resource. It distinguishes from siblings by noting it surfaces special reduced-tax schemes that general cost-of-living tools do not, which differentiates it from get_city_summary or get_safety_net.
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 for when to use the tool: when you need a city's inbound-worker tax regime, especially the special reduced-tax schemes not covered by general cost-of-living tools. It also notes that most cities return null, implying this tool will often yield no data, but it does not explicitly name alternative sibling tools or state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_safety_netLook up family safety net for several citiesABatch lookup of safety-net values for 1 to 20 cities at once: parental leave weeks + how it is paid, universal healthcare flag, vacation days, public holidays (public_holidays is the count the statute lists; public_holidays_effective is how many of those fall on a working day once you account for whether the country replaces a weekend holiday, so quote the effective figure whenever you compare two places on time off), plus each city's safety_net dimension score (0-100) for relative strength. This is the only tool that accepts many cities in one call: use it to line several places up on family / leave / healthcare benefits. On parental leave, quote parental_leave_summary rather than composing your own line from parental_leave_paid_pct: Ireland, Australia, the UK, Czechia and part of Sweden pay a flat weekly cash sum instead of a share of salary, so their headline percentage is 0 or partial and reading it alone would wrongly call their leave unpaid; Germany pays a percentage up to a monthly cap (parental_leave_capped) and pays nothing at all above a household-income ceiling. Check parental_leave_basis (percentage / flat / capped / mixed / unpaid) before making any claim about pay. On sick pay, quote sick_leave_policy rather than composing your own line from sick_leave.phases: it is the STATUTORY FLOOR for someone in their first year of service, phases run one AFTER another rather than stacking, and the durations carry units that differ by country (Slovenia counts working days, Denmark counts calendar days), so sick_leave.paid_weeks is deliberately null wherever the phases are measured against different reference periods and no single total exists. For one city's full profile (tax shape + costs + safety net together) call get_city_summary instead; for a two-city head-to-head that includes cost-of-living use compare_cities. Read-only, no side effects; returns a text summary plus structured JSON.
| Name | Required | Description | Default |
|---|---|---|---|
| cities | Yes | Array of city slugs (1-20). Use list_cities to discover valid slugs. |
| Name | Required | Description |
|---|---|---|
| cities | Yes | |
| not_found | No | |
| score_basis | No | |
| methodology_url | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is covered. The description adds 'Read-only, no side effects; returns a text summary plus structured JSON', which reinforces the annotations and clarifies the output format. This provides useful behavioral context beyond what annotations alone convey, warranting a score above the midpoint.
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 front-loaded with purpose ('Batch lookup of safety-net values...') and structured logically: first states scope and fields, then provides detailed interpretation notes, then gives sibling alternatives, and ends with behavioral summary. While somewhat lengthy due to necessary caveats (parental leave basis, sick leave notes), every sentence serves a purpose. It loses one point for verbosity in parts, but remains well-organized.
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 that an output schema exists (reducing need to document return format), the description covers all critical aspects: major fields, interpretation nuances (public_holidays vs effective, parental leave percentages vs summary, sick leave policy), limits (1-20 cities), and relationships to sibling tools. It fully equips an AI agent to use the tool correctly and avoid common mistakes, making it highly 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 already has 100% coverage with a description for the 'cities' parameter, including constraints (1-20 items, string slugs) and a reference to list_cities for valid slugs. The description does not add significant new semantic meaning about the parameter itself; it repeats the valid slug source. Given baseline 3 for high schema coverage, the description adds marginal 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 clearly states this tool performs 'batch lookup of safety-net values for 1 to 20 cities at once', listing specific fields (parental leave, healthcare, vacation, holidays, safety_net score). It distinguishes itself from siblings by noting it is 'the only tool that accepts many cities in one call' and explicitly names alternatives (get_city_summary, compare_cities) for other use cases. The verb 'Look up' and resource 'safety-net for several cities' are specific and unambiguous.
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 explicit guidance on when to use this tool ('line several places up on family / leave / healthcare benefits') and when not to (for one city full profile use get_city_summary, for two-city head-to-head with cost-of-living use compare_cities). It also includes detailed instructions on how to interpret fields correctly, such as quoting parental_leave_summary instead of composing from percentages, checking parental_leave_basis, and using sick_leave_policy. These caveats prevent common misinterpretations and make the usage clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_citiesList supported cities by countryAList all city slugs supported by cityparity, grouped by country. Call this first to discover valid slugs before any other tool; the optional country filter narrows to one country. Read-only, no side effects; returns a text summary plus structured JSON.
| Name | Required | Description | Default |
|---|---|---|---|
| country | No | Optional case-insensitive country filter. Use the canonical name as stored in city_data.country (e.g. "Norway", "USA", "United Kingdom"). If omitted, returns all countries. |
| Name | Required | Description |
|---|---|---|
| count | Yes | |
| groups | Yes | |
| country_count | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint and destructiveHint. Description adds 'no side effects' and 'returns a text summary plus structured JSON', which adds value beyond annotations.
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 concise sentences with front-loaded purpose and efficient additional guidance. No unnecessary words.
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 low complexity, complete annotations, and existing output schema, the description covers purpose, usage order, safety, and return type sufficiently.
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 has 100% coverage with informative description for the country parameter. Description echoes the filter purpose but does not add new details beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states verb 'list', resource 'city slugs', and grouping 'by country'. It distinguishes itself from siblings by noting it should be called first to discover valid slugs.
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?
Explicitly says 'Call this first to discover valid slugs before any other tool' and explains the optional country filter. Provides clear context for when to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
rank_citiesRank cities by composite quality scoreARank supported cities by composite quality-of-life score across five weighted dimensions (financial, healthcare, vacation, childcare, safety_net). Returns top N (default 10), with filters for country, countries, region (europe/asia/north_america/south_america/oceania), has_universal_healthcare, include_cities, and exclude_cities. Use this to shortlist across many cities; for a head-to-head between two named cities use compare_cities. The same scenario is applied to every city so scores are directly comparable (default: single person, 2BR rent, $100k USD-equivalent gross). Read-only, no side effects; returns a text summary plus structured JSON.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results to return. Default 10. | |
| filter | No | Restrict which cities are ranked, by country, region, universal-healthcare flag, or explicit include/exclude slug lists. Omit to rank all supported cities. | |
| weights | No | Override the composite-score weights. Defaults: financial 30, healthcare 20, vacation 15, childcare 15, safety_net 20. | |
| scenario | No | Override the default ranking scenario. The same scenario is applied to every city so scores are comparable. |
| Name | Required | Description |
|---|---|---|
| ranked | Yes | |
| skipped | Yes | |
| weights | Yes | |
| considered | Yes | |
| scenario_used | Yes | |
| methodology_url | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds value by stating 'The same scenario is applied to every city so scores are directly comparable' and disclosing the return format as 'a text summary plus structured JSON.' No contradiction; these traits are consistent with read-only 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 three sentences, front-loaded with the central action, and every sentence contributes either purpose, usage, or return format. No 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?
Given the tool's moderate complexity (4 top-level params, nested objects) and the presence of a full output schema, the description covers the essential context: ranking logic, filtering, scenario comparability, defaults, and return shape. It is complete enough for an agent to select and exercise 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?
Schema description coverage is 100%, so the baseline is 3. The description merely lists filter names and default values already present in the schema; it does not add new parameter-level meaning beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and scope: 'Rank supported cities by composite quality-of-life score across five weighted dimensions'. It also lists the two main use cases and explicitly contrasts with compare_cities, making the tool's purpose crisp and distinct from siblings.
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 explicitly says 'Use this to shortlist across many cities; for a head-to-head between two named cities use compare_cities,' which is a clear when-to-use and alternative. It also names the filter dimensions, giving context on how to narrow the ranking.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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
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.
No comments yet. Be the first to start the discussion!
Sign in to create a connector for this server.