Who works the register and where it is short-handed: per-contributor verb vectors, community shape (activity windows, the bus-factor concentration RISK, independence structure among measurers, newcomer return rate) and the scarce verbs. Deliberately NOT a leaderboard — no score, no rank; the served refuses list says what it will not compute and why.
| 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?
With no annotations provided, the description carries the full burden for behavioral context. It mentions that the tool returns computed metrics like 'bus-factor concentration RISK' and 'independence structure', and mentions a 'refuses' list that documents what it will not compute. This is helpful but lacks details on data freshness, computation time, or whether results are cached.
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 relatively concise at two sentences, front-loading the main purpose. The second sentence adds important caveats. Some technical jargon ('verb vectors', 'bus-factor concentration RISK') could be simplified, but the length is appropriate for the complexity.
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 concept (analyzing contributor participation patterns) and the lack of an output schema, the description provides a reasonable overview of what the tool returns. However, it could elaborate on the structure of the output or what 'scarce verbs' means. The context signals show zero parameters and no output schema, which puts more burden on the description.
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?
The tool has zero parameters and 100% schema description coverage, so the schema already fully documents the lack of inputs. The description adds no parameter information, which is appropriate since there are none. The baseline of 4 is justified because the description doesn't need to compensate.
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 that the tool analyzes contributor participation patterns including verb vectors, community shape metrics, and identifies staffing gaps. It explicitly states what the tool is NOT (a leaderboard), which helps differentiate it from potential siblings like get_contributor or whoami. However, the purpose could be more precise about the resource being queried.
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 minimal guidance on when to use this tool versus alternatives. The 'Deliberately NOT a leaderboard' statement helps set expectations but doesn't explicitly suggest alternatives. With 18 sibling tools including get_contributor and how_to_participate, more contextual guidance would be beneficial.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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