Student gamification features (XP, Levels & Streaks), a repetitive task that could be automated with a tool. (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 provided, so the description carries full burden. It fails to disclose behavioral traits such as whether the tool is read-only or destructive, authentication needs, side effects, or return characteristics. The phrase 'repetitive task that could be automated' hints at automation but does not specify system effects.
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 a single sentence with a price note, making it compact. However, it is not 'front-loaded' with a clear action statement. It is under-specified rather than concise, scoring a moderate 3.
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?
Despite having an output schema (not described), no information is provided about return values, error conditions, or required context. The description lacks completeness for a tool with one free-form parameter and no annotations, leaving critical gaps in 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?
The sole parameter 'payload' is a free-form object with no schema descriptions (0% coverage). The description adds no information about expected keys, values, or format. An AI agent has no way to construct a valid payload, making the tool effectively unusable without external documentation.
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 mentions 'Student gamification features (XP, Levels & Streaks)' and calls it a 'repetitive task', but lacks a verb indicating the action performed (e.g., get, update, create). This vagueness makes it unclear whether the tool retrieves, modifies, or automates gamification data. No sibling differentiation is provided.
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 given on when to use this tool versus alternatives. There are no exclusions, prerequisites, or references to sibling tools. The description does not help an agent decide between this and other listed tools.
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.