45% of developers report debugging AI-generated code is more time-consuming than debugging their own; 66% cite 'AI solutions that are almost right, but not quite' as their single biggest frustration (2025 SO Developer Survey). (reference price: $0.0100 per call)
| Name | Required | Description | Default |
|---|---|---|---|
| payload | 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?
No annotations are present, and the description fails to disclose any behavioral traits. It does not state whether the tool is read-only, whether it has side effects, or what the response contains. The agent is left completely in the dark about the tool's behavior.
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 extremely short (one sentence), but it is not concise—it is underspecified. It lacks any structure (no verb, no action, no context). The inclusion of a price reference is irrelevant and does not help the agent understand the tool.
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 1 parameter with 0% coverage, no annotations, an output schema (unseen), and 18 sibling tools, the description is completely inadequate. It does not explain what the tool returns, how to use the parameter, or when to invoke it.
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%, yet the description provides no information about the single parameter 'payload'. The parameter is vaguely typed as any object or null with no constraints. The description adds zero meaning beyond the schema.
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 is a statistic about developer frustration, not a functional statement. It lacks a verb or any indication of what action the tool performs. The title is null, so there is no clarification. The agent cannot determine the tool's purpose.
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 usage guidance is provided. The description does not mention when to use this tool, what problem it solves, or how it differs from siblings like 'research_pain_points' or 'codebase-learning-friction'. The agent has no context for selection.
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.