angel_list_casesLista proyectos y marcas incluidos públicamente en la página de casos de Ángel Moncada.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | ||
| category | No |
| 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 the useful behavioral detail that only publicly included projects/brands are listed, but it does not disclose pagination, filtering behavior, or any other side effects. 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?
A single, well-structured sentence that front-loads the action and resource. Every word contributes meaning, and there is no redundant filler or restatement of the title.
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 no required parameters, has output schema support, and annotations cover read-only/idempotent behavior. Still, the complete absence of parameter semantics and sibling usage guidance leaves meaningful gaps for an agent trying to use filters or decide between this and angel_search.
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 carries the full burden of explaining 'query' and 'category', but it does not mention either parameter at all. The agent receives no guidance on what values are valid, what these filters do, or how they affect the returned list.
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 states a specific verb ('Lista') and a specific resource ('proyectos y marcas' on the 'página de casos'), which clearly distinguishes it from siblings like angel_get_contact, angel_get_profile, and angel_search. Even without naming siblings, the resource is distinct 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 implies the tool should be used when someone wants to list publicly included projects and brands from Angel Moncada's cases page. However, it does not explicitly address when to prefer this over angel_search or other siblings, nor does it mention any exclusions or prerequisites.
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.