Browse rankings, collections, categories, scenarios, topics or filtered projects.
| Name | Required | Description | Default |
|---|---|---|---|
| role | No | ||
| layer | No | ||
| limit | No | ||
| query | No | ||
| topic | No | ||
| locale | No | en | |
| offset | No | ||
| license | No | ||
| ranking | No | ||
| category | No | ||
| scenario | No | ||
| use_case | No | ||
| collection | No | ||
| deployment | No | ||
| request_id | No | ||
| radar_topic | No | ||
| github_topic | No | ||
| resource_type | 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 non-destructive behavior. The description adds context about the facets being browsable (rankings, collections, categories, etc.) but does not disclose matching logic, pagination behavior, or result-level details. It is consistent with the annotations and does not contradict them, but also adds only modest behavioral context beyond the 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?
The description is a single concise sentence and front-loads the verb, but the brevity crosses into under-specification. The list of browse facets is useful, yet the structure leaves no room for usage guidance or parameter clarification, so the conciseness is achieved at the expense of completeness.
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?
Given the tool's complexity (18 optional parameters) and the presence of several closely related siblings, the description is too thin. It does not explain how 'filtered projects' relate to the listed facets, what the ranking/scenario/topic fields mean in practice, or when to prefer this over search_ai_projects. The output schema mitigates return-value explanation, but other contextual gaps remain.
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 burden of explaining 18 parameters. It clarifies a few categories (ranking, collection, category, scenario, topic, query), but leaves many parameters ambiguous (role, layer, license, deployment, radar_topic, github_topic, resource_type, request_id, use_case). The description does not compensate for the schema's lack of parameter-level documentation.
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 names a specific verb ('Browse') and resource ('radar projects'), and enumerates the key entry points: rankings, collections, categories, scenarios, topics, or filtered projects. It is distinguishable from the sibling browse_radar_skills (skills vs projects) and search_ai_projects (browse vs search), though it does not explicitly contrast itself with them.
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?
No guidance is given about when to use this tool versus search_ai_projects, get_radar_overview, or browse_radar_skills. The description only implies browsing/filtering use; it states no exclusions, prerequisites, or alternative selection criteria.
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.