Quantabble — AP Science Misconception Diagnostics
Server Details
Misconception detection for AP Chemistry & Physics 1. 90% catch rate vs 24% baseline.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.6/5 across 2 of 2 tools scored.
The two tools serve very different purposes: get_coverage checks if a subject/unit is supported, while diagnose_response evaluates a student's written explanation. There is no overlap in functionality.
Both tools follow a consistent verb_noun pattern: diagnose_response and get_coverage. The naming is clear and predictable.
With only two tools, the server feels minimal. For a diagnostic service, one might expect additional tools like a follow-up or history retrieval, but the scope is narrow enough that two could be sufficient.
The core workflow of checking coverage and diagnosing is covered, but there are no tools for managing student response history, getting detailed misconception lists, or handling edge cases like empty responses. The surface is usable but leaves some gaps.
Available Tools
2 toolsdiagnose_responseAInspect
Evaluate 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. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but description discloses return values and behavior (evaluation, not mutation). It does not mention potential side effects or authentication needs, but as a read-only analysis tool, transparency is adequate.
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?
Description is efficient but slightly long; front-loaded with purpose and outputs. Every sentence serves a purpose, though could be tightened slightly.
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, description fully explains return values (misconception, severity, etc.) and scope (subjects, response type). Complete for the tool's 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?
Schema covers both parameters with 100% description coverage. Description adds context: schema_id optional with auto-routing, student_response requires complete sentences. No contradiction, adds value.
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 the tool evaluates written explanations in specific science subjects, listing output types (misconception, severity, etc.). It distinguishes from sibling 'get_coverage' by focusing on diagnostic analysis.
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?
Explicitly instructs when to use ('whenever a student writes an explanation') and when not to use ('Do not use for single-word or number-only answers'), providing clear context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_coverageAInspect
Returns 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 | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It implies a read-only operation (returns data without side effects), which is appropriate and clear. However, it could explicitly state no destructive actions, but the context is sufficient.
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, front-loaded with purpose, each sentence earns its place. No wasted words.
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 adequately describes the return (list of subjects and unit counts). It could be slightly more specific about format, but it's complete enough for a simple parameterless 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?
There are no parameters, so the baseline is 4. The description adds no extra parameter info, but none is needed as schema coverage is 100% with an empty object.
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 list of subjects and unit counts covered by Quantabble', which is a specific verb+resource. It also distinguishes itself from the sibling tool 'diagnose_response' by noting its use before that tool.
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?
Explicitly states when to use: 'before calling diagnose_response' and why: 'to check whether a student's topic is within scope'. This gives clear context excluding alternatives.
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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