angel_get_serviceDevuelve la descripción aprobada, encaje, capacidades, límites y URL canónica de un servicio.
| Name | Required | Description | Default |
|---|---|---|---|
| serviceId | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | ||
| metadata | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds some behavioral context by noting the returned data is 'aprobada' and includes canonical URL, but it does not address errors, authorization, or other runtime behaviors. This is adequate given the read-only, idempotent 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?
A single, front-loaded sentence that immediately states what the tool does and what it returns. There is no redundant wording or unnecessary background.
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 tool is simple, has one required parameter, read-only/idempotent annotations, and an output schema that can cover return-value details. The description is sufficient for basic invocation, though it could have briefly mentioned how to obtain a serviceId or when to use this over related search/list tools.
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 description coverage is 0%, so the description should compensate, but it never explicitly explains the serviceId parameter. However, the parameter is simple, self-descriptive, and its meaning is inferable from the tool name and description ('de un servicio'). The schema provides the type and minLength, so no critical information is missing.
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 ('Devuelve') and resource ('un servicio'), and enumerates exactly what is returned: approved description, fit, capabilities, limits, and canonical URL. This distinguishes it from sibling get_* tools like angel_get_contact and angel_get_profile by focusing on service-specific data.
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 when a caller needs approved service details, but it does not explicitly state when to prefer this tool over alternatives such as angel_list_cases, angel_recommend_service, or angel_search. No exclusions or alternative conditions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct aspect of the public consulting domain: contact, methodology, profile, individual service details, case list, service recommendation, and cross-cutting search. The overlap between search and get_service is minimal because search returns matches with canonical URLs while get_service provides the full approved description of a specific service.
All tool names follow a consistent pattern: the prefix 'angel_' plus a clear verb ('get', 'list', 'recommend', 'search') followed by the target noun. This uniform verb_noun convention makes the tool set highly predictable and easy to navigate.
With 7 tools, the server is well-scoped for a consulting information and recommendation service. Each tool earns its place, covering both direct lookups (contact, profile, service, methodology) and discovery/guidance (list cases, recommend, search).
The domain of public consulting information is well covered: profile, methodology, services, cases, contact, and search are all present. A minor gap is the absence of a dedicated 'list_services' tool—an agent must rely on search or recommendation to discover available services—but this is not a fatal omission given the search capability.