Catálogo vivo de hosting y servidores cloud en Colombia. Precios en pesos, con fecha de lectura.
Estado del catálogo: cuándo se generó, si está fresco, y si cada fuente se pudo leer. Consúltalo antes de afirmar un precio. «No hay discrepancias» y «no se pudo leer la fuente» son cosas distintas y aquí se ven distintas.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive. The description adds an important behavioral nuance: distinguishing 'no discrepancies' from 'source could not be read'—a subtle trap that could mislead an agent. This goes beyond what annotations state.
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 are packed with essential information: what the tool reports, when to call it, and the critical interpretation caveat. No redundancy or filler.
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 output schema exists and annotations cover safety, the description supplies all necessary operational context. It explains the key semantic distinction and the correct trigger scenario, making the tool fully callable by an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, so schema coverage is effectively 100%. The description correctly focuses on output semantics rather than parameters, and the baseline of 4 applies for a zero-parameter tool.
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 reports catalog status: generation time, freshness, and whether each source was readable. It uses a specific resource (catalog) and provides a concrete use case, distinguishing it from the sibling pricing/ordering tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit guidance on when to use it: 'Consúltalo antes de afirmar un precio' (consult it before asserting a price). It does not list exclusions or alternatives, but no sibling tool overlaps with this status-check function, so the context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compara dos o más planes por nombre, uno al lado del otro. Devuelve además los nombres que no existen en el catálogo, que es información útil y no un error.
| Name | Required | Description | Default |
|---|---|---|---|
| names | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds useful behavioral nuance by stating that non-existent catalog names are returned as useful information rather than an error. This goes beyond 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 two sentences with the core behavior front-loaded. The second sentence adds a valuable, non-obvious output detail without redundancy or filler.
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?
This is a simple one-parameter tool with a rich output schema and strong annotations. The description covers the comparison semantics, the required minimum input, and the notable behavior around unknown names, leaving no critical operational gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage for the only parameter is 0%, so the description must compensate. It clarifies that 'names' refers to plan names and implies at least two are needed ('dos o más planes'). It does not address details like duplicate names or exact matching, but it provides the essential meaning.
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 ('compara'), a clear resource ('planes'), and a concrete method ('por nombre, uno al lado del otro'). This clearly distinguishes it from sibling tools like get_product or search_products, which do not imply side-by-side comparison.
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?
When to use the tool is implied by the comparison verb, but no alternatives are explicitly mentioned and there is no when-not-to-use guidance. It is only inferred that you should use this when comparing two or more plans.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Prepara un pedido y devuelve el enlace del carrito para que lo complete una persona. Este servidor NUNCA cobra ni contrata. Si el catálogo está rancio o alguna fuente no se pudo leer, no devuelve enlace: con el dato dudoso no se vende.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | ||
| quantity | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description goes well beyond the annotations by explicitly stating that the server never charges or contracts, and that it will not return a link if the catalog is stale or a source could not be read. This adds meaningful safety and failure behavior context. It aligns with the readOnlyHint and destructiveHint annotations rather than 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 concise: two sentences with the primary action and output front-loaded, followed by a short safety/failure policy. Every sentence adds value, and there is no filler.
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 low-complexity tool with an output schema and rich annotations, the description covers the main outcome, safety guarantees, and a key failure mode. It lacks explicit parameter semantics and sibling differentiation, but the tool is simple enough that an agent can still 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?
The schema has 0% description coverage, and the tool description does not explain what 'name' or 'quantity' mean. An agent must infer that 'name' likely refers to a product or item and 'quantity' to the count, but the description provides almost no clarification or constraints beyond the schema defaults.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: preparing an order and returning a cart link for a person to complete. It also adds a distinguishing purpose ('for a person to complete') and emphasizes that it never charges or contracts. It doesn't explicitly name a sibling alternative, but the behavior is specific enough to identify.
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 should be used when a human needs to complete a purchase, and it mentions conditions under which no link is returned. However, it does not explicitly compare with alternatives like 'quote' or 'search_products', nor does it specify when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Dónde está la infraestructura y qué se puede afirmar sobre ella. Llámalo ANTES de decir nada sobre el datacenter o la ubicación: incluye afirmaciones frecuentes que son falsas y que conviene no repetir.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description explains that the tool surfaces common but false assertions that should not be repeated. This is valuable behavioral context that helps the agent use the output critically, not just mechanically.
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 short, front-loaded with the core purpose, and every sentence adds value. The directive about when to call it is placed prominently and aids correct usage without filler.
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 has no parameters, a rich annotation set, and an output schema, the description fully covers what an agent needs: what the tool returns, why it matters, and when to invoke it. Nothing critical is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and an empty input schema, so there is no parameter documentation burden. The description does not need to explain parameters, and the baseline for no-param tools applies.
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 purpose: it provides information about where the infrastructure is located and what claims can be made about it. It also distinguishes itself from the sibling tools by focusing specifically on infrastructure facts versus products, policies, or SLAs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit, actionable advice: call this tool BEFORE making any statement about datacenter or location. It does not name sibling alternatives or provide exclusions, but the directive is strong and context-specific.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Términos y condiciones, política de tratamiento de datos y demás documentos legales, con sus direcciones canónicas.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety profile is well covered. The description adds that the return value includes legal documents and their canonical addresses, which is useful context, but it does not disclose more about response format, language, or whether full content or just references are returned.
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 concise sentence with no filler. It front-loads the specific legal documents and includes the useful detail about canonical addresses. Every word contributes.
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 parameterless getter with an output schema and strong annotations, the description is nearly complete. The main gap is the absence of usage guidance, but nothing essential for invoking the tool correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and 100% schema description coverage, so there is nothing the description needs to explain about parameters. This is a baseline-4 case where parameter semantics are trivially satisfied.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies a specific resource: legal documents including terms and conditions, data processing policy, and other legal documents, along with their canonical addresses. It lacks an explicit verb like 'retrieves' or 'lists,' but combined with the tool name it is clear and distinguishable from siblings like get_sla.
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?
There is no guidance on when to use this tool versus alternatives. It does not mention exclusions, alternatives, or context such as 'use this for legal documents and get_sla for service agreements.' The usage context is only implied by the content of the description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Ficha completa de un plan por su nombre exacto: precio, ciclo, recursos, de qué fuente salió cada dato y cuándo se leyó. Si el plan no está en el catálogo devuelve null: eso significa que no se puede comprar, aunque alguna página lo anuncie.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, and openWorldHint, so the bar is lowered. The description adds genuine value beyond annotations: the null-return semantics ('no se puede comprar, aunque alguna página lo anuncie') and data provenance disclosure (source and read time per datum), which are materially important for agent decisions.
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, zero waste. The first is front-loaded with the purpose and return fields; the second handles the failure contract. Every clause earns its place, and the most decision-relevant fact (null means not purchasable) is given prominent placement.
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?
A single-parameter read tool with an output schema (which documents return fields), safety annotations, and a description that covers returned data plus null semantics is nearly complete. The only gap is explicit sibling routing, which overlaps dimension 2 rather than a missing behavioral fact.
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 0%, so the description must compensate. 'nombre exacto' adds the critical requirement of exactness, which warns the agent that approximate names will fail, but it does not add format details such as case sensitivity or whether a full display name or slug is expected. Partial but meaningful compensation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description states a specific verb+resource ('get' a plan's full record) and defines the scope precisely: price, cycle, resources, data source, and read timestamp. It also establishes the null-return contract for non-catalog plans, which distinguishes it from search_products (discovery) and quote (pricing proposal).
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?
'por su nombre exacto' clearly implies this tool requires an exact name, which routes agents away from search_products when they lack it, but the alternative is never named and no explicit when-not-to-use guidance is given. The null interpretation is useful, but discovery routing is left implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
El SLA de soporte técnico: dónde está y cómo usarlo. El documento es la fuente única de las promesas de servicio; no resumas de memoria ni prometas por encima de él.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds context about the document being the authoritative source, which is useful but doesn't disclose additional behavioral traits like return format or potential caveats. The bar is lower due to annotations, and the description meets it without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short, with two sentences that convey purpose and usage guidance efficiently. It is front-loaded and contains no filler, though it could be slightly more structured with a clear verb.
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 has no parameters, a rich set of annotations, and an output schema (not shown but present), the description provides sufficient context for the agent to understand the tool's purpose and constraints. The emphasis on the document being the single source is valuable. Minor gap: it doesn't explicitly state what the response will contain, but the output schema likely covers that.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the schema provides no parameter semantics. The description doesn't need to explain parameters. Baseline for zero parameters is 4, and the description fulfills this adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the subject as the technical support SLA and states its purpose ('dónde está y cómo usarlo'), indicating the tool provides information about the SLA location and usage. However, it lacks a direct verb like 'get' or 'retrieve', so it's clear but doesn't fully match the highest bar.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit guidance: the document is the single source of service promises, and the agent should not summarize from memory or promise beyond it. This tells the agent when to rely on this tool and what to avoid, though it doesn't reference sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Cotiza un plan por cantidad y devuelve la aritmética, no sólo el total. IMPORTANTE: el subtotal NO incluye impuestos y por lo tanto no es un precio final al consumidor; la respuesta explica por qué. Al presentarlo hay que decirlo.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | ||
| quantity | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations indicate read-only, open-world, idempotent, and non-destructive behavior, which are already transparent. The description adds that it returns arithmetic and that the response explains why the subtotal excludes taxes, but it does not fully detail the output format or any other side effects. This is a moderate addition 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?
The description is concise, front-loaded with the main purpose, and includes a clear caveat about taxes. It avoids unnecessary verbosity, though the repeated emphasis on taxes could be seen as slightly redundant, but overall it is well-structured.
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 provides some context (returns arithmetic, tax exclusion, presentation instruction) but lacks details on the expected response structure, error behavior, or how the 'name' parameter affects the quote. Given the simplicity of the tool, it is not fully 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 schema defines two parameters (name, quantity) with no descriptions, and the description only indirectly refers to 'cantidad' (quantity) but does not explain the 'name' parameter or clarify what values are expected. With 0% schema coverage, the description fails to compensate, leaving parameter semantics unclear.
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 that the tool quotes a plan based on quantity and returns arithmetic rather than just a total, which distinguishes it from simple total calculators. It does not explicitly name alternatives, but the purpose is specific and comprehensible.
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 no guidance on when to use this tool versus the sibling tools (e.g., catalog_status, compare_products). It only mentions the tool's behavior and a presentation instruction, not the conditions or context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Busca planes por categoría, presupuesto y recursos mínimos.
Categorías: · 'cloud' — Servidores Cloud, máquinas virtuales con IP colombiana y acceso completo al servidor. · '-lang-customline0060-' — Cloud Web Hosting, los Sitios Elásticos. NO es un fondo de recursos compartido: cada cuenta corre en su propio entorno aislado con CageFS y con memoria, CPU, procesos y entrada/salida asignados por CloudLinux, y se sube o baja de plan al instante sin costo adicional. · 'cloud-web-cluster' — el sitio repartido en varios nodos con réplica y balanceo. · 'cpanel-reseller' — recursos para repartir entre clientes propios desde WHM. · 'web-hosting-windows' — plataforma Windows con Plesk. · 'ssl' y 'admindns'.
El precio va en pesos colombianos, y el ciclo NO es el mismo en todas las categorías: mira el campo cycle antes de comparar dos.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| category | No | ||
| min_vcpu | No | ||
| max_price | No | ||
| min_price | No | ||
| min_ram_gb | No | ||
| min_disk_gb | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds genuinely useful behavioral context beyond that: prices are in Colombian pesos, billing cycles vary by category, and the `cycle` field must be inspected before comparison. No contradiction with 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?
The description is front-loaded with the main purpose and then detailed category bullets, all of which earn their place. The odd '-lang-customline0060-' placeholder is a minor blemish, but overall the structure is scannable and efficient.
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 read-only search tool with no required parameters, an output schema, and strong annotations, the description covers the important semantics: category meanings, filter dimensions, currency, and billing-cycle caveat. It is complete enough for an agent to call the tool correctly; explicit sibling routing is the main missing piece.
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?
With 0% schema description coverage, the description carries the parameter-meaning burden. It maps 'categoría' to the category parameter, 'presupuesto' to min/max_price, and 'recursos mínimos' to min_vcpu/min_ram_gb/min_disk_gb. It also provides the actual category values, which the schema omits. It does not explicitly discuss `limit`, but that parameter is self-explanatory.
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 concrete action and resource: 'Busca planes por categoría, presupuesto y recursos mínimos.' It specifies the exact search dimensions and then enumerates the meaningful categories, making its purpose unmistakable even without reading the schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear contextual guidance for choosing categories and for interpreting prices, especially the warning to check the `cycle` field before comparing plans. It does not explicitly name sibling tools as alternatives, so it stops short of full when/when-not guidance, but the context is strong.
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
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.
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Live VPS, bare metal and GPU hosting prices across ~75 providers, rescraped daily.
izHost public read-only MCP: service pricing and live domain availability lookup.
Hosted Argentine commerce MCP: real AFIP invoicing, MercadoPago, logistics, catalog & WhatsApp.
264 emisoras de radio colombianas en vivo: busca por ciudad, genero o dial y escuchalas.
Add one secure layer between your agents and this server.
Cada herramienta tiene un propósito claramente diferenciado: estado del catálogo, búsqueda, comparación, ficha, cotización, pedido, infraestructura, políticas y SLA. Aunque varias empiezan por get_, sus dominios no se solapan y las descripciones refuerzan cuándo usar cada una.
La mayoría sigue el patrón verbo_sustantivo en snake_case (search_products, compare_products, create_draft_order, get_product). Se desvían quote, que es solo un verbo, y catalog_status, que parece un sintagma nominal en lugar de get_catalog_status; son desviaciones menores y no generan confusión.
Nueve herramientas es un tamaño adecuado para un catálogo de hosting: cubren consulta, comparación, cotización y pedido sin llegar a ser un servidor de administración. Cada herramienta tiene un rol claro y ninguna sobra.
El flujo principal está completo: conocer la frescura del catálogo, buscar, comparar, obtener fichas, cotizar y crear un pedido en borrador, además de las consultas legales, de SLA e infraestructura. No se echan en falta operaciones de escritura porque el servidor es explícitamente de consulta y no cobra ni contrata.
