glama_agentic_analyze

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses that the tool uses LLM sampling via MCP ctx.sample, which implies it may have latency or cost implications. However, it does not clarify if the tool is read-only or creates persistent data, and no safety or permission details are given. Annotations are empty, so the description carries full burden.

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 concise with two sentences. The first sentence captures the core purpose, and the second adds an important implementation detail. No superfluous content.

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 existence of an output schema, the description appropriately omits return value details. It mentions the use of MCP ctx.sample, which is critical context. However, it could briefly note prerequisites (e.g., need for a connected LLM) or how results are presented.

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 100%, and the tool description adds no new meaning beyond what the schema already provides for the single parameter 'repo_name'. The schema already explains the parameter, so the tool description does not enhance understanding.

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 it analyzes Glama scores using LLM sampling and generates fixable todos. This distinguishes it from sibling reporting tools like glama_daily_report or glama_scores_summary, which likely provide summaries without analysis or task generation.

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 provided on when to use this tool versus alternatives like glama_daily_report or glama_generate_reports. The description does not mention use cases, prerequisites, or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.