Misconception detection for AP Chemistry & Physics 1. 90% catch rate vs 24% baseline.
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Average 4.5/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: get_coverage checks if a topic is within scope, and diagnose_response performs the actual diagnostic analysis. There is no overlap or ambiguity.
Both tool names follow a consistent verb_noun pattern (get_coverage, diagnose_response) using snake_case, making them predictable and easy to understand.
With only two tools, the server feels minimal for the broad scope of AP and college science diagnostics. While it covers the essential workflow, additional tools could be expected, making it borderline.
The tool set covers the core workflow: check coverage then diagnose. Minor gaps might include per-topic unit details or student progress tracking, but overall it is sufficient for the stated purpose.
diagnose_responseAInspectEvaluate a student's written explanation in AP Chemistry, AP Physics 1, high school chemistry, high school physics, college chemistry, or college physics (algebra-based). Returns the specific misconception in their reasoning, severity, confidence score, a ready-to-deliver tutor response, a follow-up probe question, and a step-by-step remediation plan. Use this whenever a student writes an explanation and you need to know what they misunderstand and exactly what to say next. Do not use for single-word or number-only answers.
| Name | Required | Description | Default |
|---|---|---|---|
| schema_id | No | Optional. Target a specific schema (e.g. "AP_CHEM_1_1") if you already know the topic. If omitted, the API routes automatically across all 130 schemas. | |
| student_response | Yes | The student's own words — their explanation, reasoning, or answer in free-response form. At least one complete sentence with a stated reason works best. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the returned outputs (misconception, severity, confidence, tutor response, follow-up probe, remediation plan) but does not mention side effects, authentication, or limitations beyond input constraints. Still, it provides sufficient transparency for safe use.
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?
Two sentences efficiently convey purpose and outputs. While the second sentence lists multiple outputs, it is still clear and front-loaded. Some minor redundancy could be trimmed, but overall good.
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 no output schema, the description lists all return components, which is helpful. It covers subjects, input constraints, and usage conditions, providing enough context for an agent to decide when and how to use the tool.
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 coverage is 100%. The description adds value beyond the schema by explaining the optional 'schema_id' target and that 'student_response' works best with at least one complete sentence. This helps the agent understand parameter usage.
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 evaluates a student's written explanation in specific subjects (AP Chemistry, AP Physics 1, etc.) and returns diagnostic outputs. It distinguishes itself from the sibling tool 'get_coverage' by its specific 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?
Explicit usage guidelines: 'Use this whenever a student writes an explanation and you need to know what they misunderstand and exactly what to say next.' Also includes a clear exclusion: 'Do not use for single-word or number-only answers.'
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_coverageAInspectReturns the list of subjects and unit counts covered by Quantabble. Use this to check whether a student's topic is within scope before calling diagnose_response.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It states the output is a list (subjects and unit counts) but does not explicitly confirm it is a read-only, side-effect-free operation. While likely safe, best practice would be to state it's non-destructive. Lacks explicit behavioral disclosure.
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?
Two sentences: first states what it returns, second gives usage guidance. Front-loaded with primary purpose, no extraneous information. Every sentence earns 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?
For a simple, parameterless tool with no output schema, the description is fully sufficient. It covers what the tool returns, its purpose, and its relationship to the sibling tool. No gaps identified given the complexity.
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?
Tool has no parameters and schema description coverage is 100%, so baseline is 4. The description does not need to explain parameters, but it adds no extra detail beyond what the schema already provides. Adequate.
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?
Description clearly states it returns a list of subjects and unit counts, specifying the resource (Quantabble's coverage). It distinguishes from sibling tool 'diagnose_response' by explicitly linking its use as a prerequisite check, making the purpose and scope unambiguous.
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?
Directly states when to use the tool: 'Use this to check whether a student's topic is within scope before calling diagnose_response.' This provides clear context and an explicit alternative, guiding the agent on proper invocation order.
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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