Browse/filter the Radar Skills library or open one Skill by ID.
| Name | Required | Description | Default |
|---|---|---|---|
| kind | No | ||
| sort | No | ||
| limit | No | ||
| query | No | ||
| locale | No | en | |
| offset | No | ||
| license | No | ||
| category | No | ||
| skill_id | No | ||
| request_id | No | ||
| installable | 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 carry the safety profile: readOnlyHint, idempotentHint, openWorldHint, and destructiveHint false. The description adds a useful distinction between library browsing and opening a single Skill by ID, but provides no further behavioral context such as result shape or filtering semantics.
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, front-loaded sentence with no filler. Every word adds meaning, and it communicates the core behavior efficiently.
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?
Despite having annotations and an output schema, the tool has 11 undocumented parameters and multiple usage modes. The one-line description does not explain how parameters combine, when to use direct ID lookup versus filtering, or what distinguishes the two modes. An agent would need significant inference to call this correctly in non-trivial cases.
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 tool has 11 parameters. The description only gestures at 'one Skill by ID,' which maps to skill_id, and 'browse/filter' at a high level. It leaves kind, sort, limit, query, locale, offset, license, category, request_id, and installable unexplained.
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 action and resource: 'Browse/filter the Radar Skills library or open one Skill by ID.' It clearly distinguishes the tool from sibling project-focused tools like browse_radar_projects by naming the Radar Skills library explicitly.
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 scenarios: browsing/filtering versus fetching a single skill by ID. However, it does not explicitly say when to prefer this tool over alternatives such as browse_radar_projects or search_ai_projects, leaving that decision to inference.
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.