Returns the full set of 12 readiness questions grouped by category, with answer options (1-4 scale). Call this when the user is ready to begin the assessment. Walk the user through each question conversationally — do NOT dump all 12 at once. Acknowledge their concern first, then ask one question or one category at a time.
| Name | Required | Description | Default |
|---|---|---|---|
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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?
With no annotations, the description carries the behavioral burden and does so well by instructing the agent to 'Walk the user through each question conversationally — do NOT dump all 12 at once' and to acknowledge the user's concern first. It discloses the expected conversational behavior and pacing, though it does not mention side effects or requirements.
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 compact and front-loaded: it starts with the tool's output, then gives the trigger condition and interaction instructions. Every sentence contributes actionable information with no filler.
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 zero parameters and no output schema, the description is fully self-contained. It states what is returned, when to call it, and how to conduct the assessment, making it complete for an agent to invoke correctly.
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 input schema has zero parameters, so parameter-level explanation is unnecessary. The description adds value by explaining what the returned questions contain (grouped by category, 1-4 scale), which is sufficient.
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 the tool returns the full set of 12 readiness questions grouped by category with answer options, identifying the specific resource and action. It does not explicitly name siblings, but the content is distinctive enough to separate it from the related readiness tools.
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?
It provides explicit usage guidance: 'Call this when the user is ready to begin the assessment.' It also instructs the agent to walk the user through questions conversationally rather than dumping them, giving a clear invocation context. It does not mention alternatives or exclusions, but the guidance is otherwise strong.
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.
run_student_protection_readiness_assessment and run_student_safety_checklist accept identical inputs and return near-identical results, creating serious ambiguity about which to call. The other four tools are distinct and clear.
Most names follow a consistent verb_noun snake_case pattern using get_, run_, start_, and submit_. However, the object naming is inconsistent: 'readiness_assessment' and 'safety_checklist' appear to refer to the same assessment, which weakens the overall pattern.
Six tools is a reasonable size for an assessment-focused server and largely appropriate for the scope. The set feels slightly padded because two run tools are essentially redundant, but the overall count is not excessive.
The core journey is well covered: info, start, run, next steps, and lead submission are all present. Minor gaps exist around emergency/crisis resources and clarifying the intended relationship between the two run tools.