Extracts an optimal 3-to-5 disc minimalist setup from a larger bag, maximizing shot coverage while detailing necessary compromises.
| Name | Required | Description | Default |
|---|---|---|---|
| discs | Yes | Full disc collection or bag | |
| target_count | No | Target number of discs in the minimal bag (2..6, default 3) |
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?
No annotations are provided, so the description carries the full burden. It mentions 'detailing necessary compromises' which hints at output, but doesn't disclose side effects, return format, or any limitations. For an optimization tool, agents would benefit from knowing whether it only reads the bag or suggests changes.
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, focused sentence that conveys the purpose and value proposition (maximizing shot coverage, detailing compromises). No wasted words, and the core action is front-loaded. Efficient for an agent to parse.
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 complexity of the tool (optimization with a large disc array and a target_count parameter), the description is thin. It doesn't mention the target_count parameter, how the optimization works, or what the output looks like (beyond mentioning compromises). The lack of an output schema and minimal behavioral description leaves gaps an agent may need to infer.
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 100%, so both parameters are fully described in the schema. The description adds no additional meaning beyond what the schema provides, meeting the baseline of 3. It doesn't enrich parameter understanding, but it's not needed given high coverage.
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 action (extracts) and resource (minimalist setup from a larger bag) with a specific goal (maximizing shot coverage). It is distinct from sibling tools like analyze_bag or recommend_bag_fillers, though it doesn't explicitly name them. A verb+resource with clear scope.
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 explicit guidance on when to use this tool versus siblings. It implies usage for reducing a bag to a minimal set, but doesn't state conditions, exclusions, or alternatives. For a tool with siblings that analyze, detect, and recommend, more direction would help.
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 distinct primary purpose, but analyze_bag returns both gap and overlap findings, which overlaps with find_bag_gaps and detect_overlap. This could create ambiguity when choosing between broad analysis and targeted diagnostics.
All tool names follow a consistent verb_noun snake_case pattern: analyze, build, detect, find, recommend. The style is uniform and predictable.
Five tools is well-scoped for a specialized disc golf bag analysis server. Each tool covers a meaningful facet of the workflow without unnecessary bloat.
The tool set covers the full analysis lifecycle: overall assessment, gap detection, overlap detection, fill recommendations, and minimal bag construction. No critical workflow dead ends are apparent for the stated domain.