angel_recommend_serviceRelaciona una situación de negocio con el servicio de Ángel que parece encajar mejor, explicando que es una orientación inicial.
| Name | Required | Description | Default |
|---|---|---|---|
| goal | No | ||
| stack | No | ||
| situation | 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 establish readOnlyHint and idempotentHint, so safety is covered. The description adds useful behavioral context: the tool is heuristic ('parece encajar mejor') and explicitly explains to the user that this is only an initial orientation, setting expectations about the recommendation's nature.
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 with no filler. It front-loads the core purpose and uses the remaining clause to convey the behavioral nuance of initial orientation.
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 output schema and annotations cover return shape and safety, and the core purpose is clear. However, the description omits guidance on optional parameters and does not help an agent choose between this tool and its siblings, so completeness is only adequate.
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%, and the description only clarifies the meaning of 'situation' as a business situation. The optional 'goal' and 'stack' parameters receive no explanation, leaving the agent to guess their intended content and role in the recommendation.
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 action: relating a business situation to the best-fit Ángel service. It also conveys the 'initial orientation' role, which helps distinguish it from sibling tools like angel_get_service or angel_get_contact, though it does not explicitly name those alternatives.
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 phrase 'orientación inicial' and 'situación de negocio' imply this tool is for early-stage routing when a user is unsure which service fits. However, the description gives no explicit guidance about when to prefer this tool over siblings like angel_search or angel_get_service.
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.