Learning a codebase generates disproportionate frustration relative to the time spent on it, per the 2025 SO Developer Survey -- largely attributed to inadequate documentation. (reference price: $0.0100 per call)
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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, and the description does not disclose any behavioral traits such as idempotency, side effects, permissions, or return format. The only behavioral hint is a price reference, but it is unclear what the tool does when called. This is a critical gap.
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 short but not concise in a helpful way. The first sentence is a survey factoid unrelated to tool function, and the second is a price. Neither sentence efficiently explains the tool's purpose or usage. The content does not earn its place.
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 has one parameter, no annotations, and an output schema that is not referenced, the description is severely incomplete. It does not explain what the tool does, what inputs mean, or what outputs to expect. The description fails to provide a minimally viable understanding.
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?
There is one parameter 'payload' with no schema description (0% coverage). The description does not mention the parameter at all, so it adds no meaning beyond the bare schema. The tool fails to compensate for the low 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 provides background context about codebase learning frustration but does not specify a clear verb or action. It fails to state what the tool actually does (e.g., get, report, analyze) and does not distinguish it from sibling tools like 'research_pain_points' or 'deployment-process-friction'. Purpose is vague.
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?
There is no guidance on when to use this tool versus alternatives. No mention of prerequisites, context, or exclusions. The description offers zero usage direction.
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.
Many tools have vague or overlapping descriptions, such as multiple 'repetitive task that could be automated' tools that lack clear differentiation. The inclusion of meta-tools (e.g., research_pain_points, develop_tools) alongside domain-specific tools further blurs boundaries, making it hard for an agent to select the correct tool.
Tool names use a mix of hyphens (add-license-information-to-codebase) and underscores (develop_tools, check_tool_health), with no consistent pattern. Some names are verbose and descriptive, while others are terse, creating an inconsistent naming convention across the set.
At 20 tools, the count is borderline but not extreme. However, the set includes several tools that are purely descriptive of problems (e.g., ai-generated-code-debugging-overhead) or are meta-tools for the factory itself, which inflates the count without adding practical utility for end users.
The server's purpose is unclear, mixing codebase operations, documentation, gamification, and support tickets. There are obvious gaps: no tool for updating or deleting, and the meta-tools (research, develop, health) are not exposed as a coherent lifecycle. The surface feels incomplete for any single domain.