Discover current rankings, collections, categories, scenarios and filters.
| Name | Required | Description | Default |
|---|---|---|---|
| locale | No | en | |
| request_id | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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, openWorldHint, idempotentHint, and destructiveHint false, covering the safety profile. The description adds no extra behavioral context beyond these hints, such as potential result size or dynamic nature. Since annotations carry the burden, the description's lack of additional disclosure is acceptable but not distinguished.
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 that is front-loaded with the key terms (rankings, collections, etc.). It is appropriately brief and free of fluff, though slightly sparse. It earns a strong score for efficiency without being verbose.
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 available and clear annotations, the description covers the core purpose but omits usage context and parameter details. For a simple read-only overview, the description is minimally adequate, but it lacks the guidance that would make it fully self-sufficient for an agent to call 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?
Schema coverage is 0%, and the description does not explain the locale or request_id parameters. While locale likely controls language, request_id is ambiguous and could affect behavior. The description fails to compensate for the schema's lack of documentation, leaving the agent without meaningful parameter guidance.
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 discovers current rankings, collections, categories, scenarios, and filters, specifying the resource (radar overview) and the action (discover). While it does not explicitly name sibling tools, the content scope distinguishes it from more targeted tools like browse_radar_projects or compare_ai_projects. The purpose is specific enough for an agent to select it for an overview request.
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 alternatives. It does not mention that for detailed project browsing one should use browse_radar_projects, or that this is a starting point for an overview. The context is purely declarative, leaving the agent to infer usage without explicit direction.
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 has a generally distinct role: browsing radar views, searching projects, getting facts, comparing, composing stacks, and finding alternatives. A couple of tools—notably browse_radar_projects and search_ai_projects—could be confused, but their descriptions clarify exploratory browsing versus requirement-driven search.
All tool names follow a consistent snake_case verb_noun pattern: browse_*, get_*, search_ai_projects, compare_ai_projects, compose_ai_stack, find_alternatives. The naming makes the action and target object immediately clear across the entire set.
Nine tools is a well-scoped size for an open-source AI intelligence and decision-support server. Each tool covers a distinct part of the workflow without feeling bloated or redundant.
The set covers the main workflow well: overview, browsing, search, project facts, license evidence, comparison, stack composition, and alternatives. Minor gaps like project tracking/history or export utilities are non-essential for this kind of intelligence/decision-support surface.