CampusMind-AI

Overview Schema Related Servers Score Discussions

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools target distinct resources and actions, but some overlap exists between get_review_due and suggest_review_plan (both about review scheduling) and between ask_question and explain_concept (both about learning support). Descriptions help clarify boundaries, but an agent might occasionally pick the wrong tool without careful reading.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case, with verbs like get, list, log, set, start, and end. The naming is uniform and predictable, making it easy to infer functionality from the name.
Tool Count3/5
With 22 tools, the server is on the heavier side of the typical range. While each tool serves a purpose in the learning assistant domain, some consolidation could reduce redundancy (e.g., merging or streamlining review-related tools), making the count feel slightly bloated.
Completeness4/5
The tool set covers the core learning workflow well: authentication, course viewing, concept mastery, questioning, explanations, quiz logging, study sessions, review scheduling, goals, and voice interaction. Minor gaps exist, such as no way to list or update study goals and no direct way to view all logged topics beyond search, but these are workable.
Average 3.9/5 across 22 of 22 tools scored. Lowest: 3.2/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 42 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden of behavioral disclosure. It explains the prioritization logic but does not clarify whether the operation is read-only, what the output format is, or how 'prolonged neglect' is measured. This lack of detail is a significant gap for an unannotated tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief and front-loaded, using two sentences to convey purpose. The second sentence ('Prioritizes topics that need urgent intervention') somewhat restates the first, creating minor redundancy, but the overall structure is efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the single parameter and no output schema, the description adequately conveys the tool's core purpose. However, it omits details about return format and behavior when no risky topics exist, leaving it only minimally complete for an agent deciding whether to invoke this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already fully documents the single studentId parameter with a clear description, so the tool description adds no additional parameter meaning. With 100% schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs 'Surface' and 'Prioritizes', identifying topics at risk due to low confidence, deadline pressure, or prolonged neglect. This distinguishes it from sibling tools like get_review_due or get_mastery_heatmap by focusing on urgent intervention. However, it could be slightly more explicit that it returns a list for a given student.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use when needing to identify urgent at-risk topics, but it does not explicitly state when to choose this over alternatives such as get_review_due or suggest_review_plan. No exclusions or conditional guidance are provided, leaving the agent to infer the appropriate context from the phrasing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the burden. 'Get' implies a read-only operation, and the mention of 'current mastery' discloses a key output detail. However, the description does not explicitly state the absence of side effects, authentication requirements, or error behavior, which is a moderate gap for a tool without annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that directly states the tool's purpose and key output. There is no superfluous information, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with two required parameters and no output schema, so the description is mostly adequate for invocation. However, it does not clarify the exact return format or other details beyond 'current mastery', and it lacks usage context relative to sibling tools. This leaves some gaps but remains usable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema fully describes both parameters (studentId and conceptId) with clear descriptions, achieving 100% schema coverage. The description adds no extra meaning beyond what the schema already provides, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the clear verb 'Get' and identifies the resource as 'details about a specific concept' with the added scope 'for a student' and 'including current mastery'. It is specific enough to differentiate from siblings like explain_concept and get_mastery_heatmap, though 'details' is somewhat vague about what other information is returned.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no explicit guidance on when to use this tool versus alternatives such as get_mastery_heatmap or explain_concept. The phrase 'a specific concept' implies single-concept use, but no exclusions or alternative tools are mentioned, leaving the agent without sufficient context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It implies a read-only operation but does not disclose behavior such as authentication requirements, error conditions, pagination, or what 'basic info' includes. The description adds minimal behavioral context beyond the tool name.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that immediately states the action and scope. There is no wasted text or unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with one parameter, so the description is largely sufficient for invocation. However, it does not explain what 'basic info' means or describe the response format, which could be relevant since there is no output schema. Moderate completeness for a low-complexity tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, with studentId described as 'The student ID'. The description mentions 'a student' but adds no additional meaning beyond the schema. Baseline 3 applies because the schema adequately documents the parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'List all courses a student is enrolled in' with a specific verb and resource. It distinguishes itself from sibling tools like get_concept or get_progress_summary, which focus on different aspects of student data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It simply states the function without any context, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description discloses that it stores topics and duration and updates study streak analytics, which is a useful side effect. However, it doesn't mention prerequisites, idempotency, or what happens on repeated calls, leaving the agent with limited behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the main action and then provides supporting details, with no unnecessary verbiage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple 3-parameter tool with no output schema, the description covers what it does and its main side effect. However, it doesn't mention what the tool returns, which the agent would need to know since there is no output schema, making it incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All three parameters are fully described in the schema (100% coverage), and the description doesn't add additional parameter semantics beyond restating that topics and duration are involved.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Record' and resource 'completed study session', and further details what is stored and the analytics update. However, it doesn't explicitly contrast with sibling tools like log_topic or log_quiz_result, so it lacks differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for after a session is completed, but it gives no explicit guidance on when to use it versus alternatives such as log_topic or log_quiz_result, and no exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It discloses useful behavior: the result is limited to upcoming assignments, sorted by due date, and includes course context. However, it does not mention authentication requirements, error cases, pagination, or the exact return structure, leaving notable transparency gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences, front-loaded with the action and resource. Every phrase contributes: the destination widget, the sorting, and the course-context addition. No filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description provides the essential purpose and return summary, but lacks details on the return field names, date format, ordering direction, and edge cases. This is adequate for selecting the tool but not fully complete for confident result processing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the sole parameter studentId is already described as 'The student ID'. The description adds no additional parameter-specific semantic, such as ID format or scope of the student, so it does not improve beyond the schema baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'Get deadlines organized for the timeline widget', clearly specifying the verb and resource. It also states the output ('upcoming assignments sorted by due date with course context'), which distinguishes it from sibling tools like get_review_due or get_daily_briefing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'for the timeline widget' implies a UI-specific use case, but the description does not explicitly say when to use this tool instead of related tools such as get_review_due or get_progress_summary. No exclusions or alternative references are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears the transparency burden. It discloses that the plan is generated from mastery data and recency and prioritizes weak or long-unreviewed concepts, but does not explicitly state read-only behavior, side effects, or dependencies on student data. For a suggestion tool the risk is moderate, but the description does not fully characterize behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that is front-loaded with the action ('Generate') and clearly states the resource and criteria. No redundant or filler content exists.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With only two parameters, 100% schema coverage, and no output schema, the description conveys the core function and prioritization logic. However, it does not mention expected output format, potential limitations, or how it relates to sibling tools like get_review_due, leaving some contextual gaps for an agent making invocation decisions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters (studentId, maxTopics) already described. The description adds context about mastery data and recency but does not elaborate on how maxTopics affects the plan or the expected format of studentId. It does not compensate beyond the schema, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates a spaced-repetition review plan using mastery data and recency, with prioritization of weak and long-unreviewed concepts. This distinguishes it from sibling tools like get_review_due, which likely lists due items rather than generating a prioritized plan.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is used when a review plan is needed, and mentions data sources (mastery data, recency) and prioritization criteria, but does not explicitly compare with alternatives such as get_review_due or get_daily_briefing. No when-not-to-use guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals the adaptive behavior based on mastery level and indicates a read-like operation ('Get'), but it does not mention potential side effects, prerequisites such as existing mastery data, or how the depth parameter interacts with the adaptive logic. The description provides some behavioral context but lacks completeness.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two concise sentences, front-loaded with the primary purpose. The first sentence defines what the tool does, and the second elaborates on the adaptive behavior. Every word earns its place, with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a relatively simple tool with all parameters documented in the schema, the description effectively conveys the core functionality and adaptive nature. However, it does not describe the return format or the interaction between the depth parameter and mastery-based adaptation, which could be relevant for an agent. Since there is no output schema, a bit more detail would be ideal, but overall it is adequately complete for a read-only explanation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so all parameters are already documented with descriptions. The tool description adds context about mastery level influencing explanation depth, but it does not explicitly reference the 'depth' parameter or clarify how it interacts with the adaptive system. Since the schema fully covers parameter meaning, a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: to get an adaptive explanation of a concept based on mastery level. It distinguishes itself from siblings like get_concept (static retrieval) and ask_question (interactive Q&A) by emphasizing the personalization aspect. The verb 'Get' is specific enough, and the resource 'explanation of a concept' is explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool should be used when an adaptive explanation is needed, but it does not explicitly mention when to prefer it over get_concept or ask_question. No exclusions or alternative tools are named, so usage guidance is only inferred from the adaptive wording.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It states the return format ('courses with their concepts and confidence scores') but does not mention read-only nature, data freshness, prerequisites, or potential edge cases. Meets a basic threshold but leaves gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences, both informative and front-loaded with the key action and output. No fluff or redundant phrasing; every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter read tool with no output schema, the description adequately covers the purpose, output structure, and intended widget context. It lacks explicit usage alternatives, but overall provides sufficient context for selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already describes the only parameter (studentId) with 100% coverage. The description adds no additional parameter-specific meaning, only clarifies the output structure. Baseline of 3 is appropriate given the schema's completeness.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Get') and resource ('mastery data organized by course'), explicitly stating the tool's purpose for the mastery heatmap widget. This clearly differentiates it from sibling tools like get_progress_summary or get_concept.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context ('for the mastery heatmap widget') but does not explicitly state when to use this tool over alternatives, nor does it provide exclusions or mention related tools. It's adequate but lacks direct guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure and does well by stating concrete side effects: 'Resets its review clock, applies a confidence boost, and increments awareness.' However, it omits details like reversibility, rate limits, or return values, which are minor gaps given the tool's simplicity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with three meaningful clauses, front-loaded with the primary action. It is concise and free of redundant information; every phrase adds value, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity (two simple parameters, no output schema, no annotations), the description adequately covers the core purpose and side effects. It lacks usage guidance, but that is a separate dimension. Overall, it is sufficient for an agent to understand the tool's role and invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already covers both parameters with clear descriptions (100% coverage). The description does not add parameter-specific meaning beyond referencing 'by id,' so it neither detracts nor enhances what the schema provides. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's specific action: 'Mark a concept (by id) as reviewed right now.' It uses a strong verb ('mark'), identifies the resource ('concept'), and lists additional effects (resets review clock, confidence boost, awareness), which distinguishes it from siblings like recall_topic or log_topic.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no explicit guidance on when to use this tool versus alternatives such as recall_topic or log_quiz_result. The only implied usage is from the verb 'mark,' but no exclusions or recommended contexts are provided, leaving the agent to infer selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses that the tool logs the start and returns a greeting tailored to the student's context, but it does not specify permissions, state changes beyond logging, failure behavior, or whether it is a safe read operation. This is moderate transparency for a simple tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, using two sentences to convey the core action, the logging side effect, and the return value. There is no fluff or redundant information, making it easy for an agent to quickly grasp the tool's behavior.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with no output schema, the description covers the main purpose and return value well. However, it omits potential prerequisite details (e.g., whether the student must be logged in) and edge cases (e.g., what happens if a session already exists), leaving a few gaps in completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema fully describes the single studentId parameter, and the description adds no additional parameter-specific details. The reference to tailoring the greeting to the student's context is contextual but does not explain format, constraints, or how the ID is used beyond identifying the student. Baseline 3 is appropriate given 100% schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: to create a new voice interaction session. It uses a specific verb ('creates') and resource ('voice interaction session'), and adds detail that it logs the start and returns a personalized greeting, distinguishing it from related session tools like end_voice_session and process_voice_input.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is used to start a voice session but provides no explicit guidance on when to use it versus alternatives like process_voice_input or end_voice_session. There is no mention of exclusions or prerequisites, so usage context is only implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden and does disclose one behavioral trait: goals appear in daily briefings and help the planner prioritize. However, it omits side effects like overwriting existing goals, idempotency, and permission requirements, leaving partial transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences: the first states the action, the second explains the downstream effect. Both sentences add value with no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for a simple 3-param mutation tool, but missing return/output behavior and whether setting a goal overwrites an existing one. Since there is no output schema and no annotations, the description could be more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema documents all 3 parameters with 100% coverage, so the description need not add syntax details. It reinforces the goal and deadline but adds no new semantic information beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Set') and resource ('study goal') with a required deadline. It clearly distinguishes this tool from siblings like log_quiz_result or get_daily_briefing by naming a unique action.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Clear context is provided: the goal is for a student with a deadline and affects briefings/planner. No explicit exclusions or alternatives are given, but no sibling tool performs this function, so the context is sufficient for use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool mutates the last-reviewed timestamp and is for testing only, which is useful. However, it doesn't specify whether the change is reversible, impacts other data, or requires special permissions, leaving some behavioral ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that front-loads the demo/testing intent and concisely states the function and rationale. Every word adds value, with no redundancies.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity and lack of output schema, the description provides sufficient context for its intended use: it explains the purpose, the mutation, and the testing scenario. However, it omits any mention of return values or side effects, which is a minor gap for a write operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds the purpose of the daysAgo parameter in context, but it doesn't provide additional semantic meaning beyond what the schema already states. The description itself doesn't elaborate on the parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: artificially aging a concept's last-reviewed timestamp by N days. It specifies the resource (concept) and the action, and explicitly mentions the use case of testing get_review_due, which distinguishes it from sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly labels the tool as 'DEMO/TESTING ONLY' and explains that it exists to enable testing of get_review_due without waiting real time. This provides clear usage context, though it doesn't name alternative tools for non-testing scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the side effect of logging a summary interaction and the return of session statistics, which are behavioral traits. However, it does not mention error handling, state requirements (e.g., session must be active), or whether it is idempotent. Still, it provides substantive transparency beyond a bare 'Ends a session'.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that front-loads the primary action and efficiently covers side effects and return values. Every clause adds value without redundancy or wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is relatively simple, and the description adequately covers the core action, side effect, and return values. Since there is no output schema, the explicit mention of returned statistics (duration, interaction count, topics covered) is useful. It lacks details about failure modes or preconditions, but those are not critical for basic usability.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear descriptions for both parameters ('The voice session ID to end' and 'The student ID ending the session'). The description adds no additional parameter-specific meaning, so a baseline score of 3 is appropriate when the schema already handles semantics effectively.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool ends an active voice session, with specific verb 'Ends' and resource 'voice session'. It also distinguishes itself from siblings like 'start_voice_session' by describing the ending and additional logging/statistics behavior.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The intended usage is implied (when you want to end a voice session), but there is no explicit guidance on when to use this tool vs alternatives, prerequisites like having an active session, or exclusions. No alternatives are mentioned, so it lacks direct usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It explains what the briefing includes (due items, review topics, weak areas) and implies a read-only aggregation, but does not explicitly state that it makes no changes or whether it requires prior login. The output content is disclosed, but deeper behavioral details are absent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one succinct sentence, front-loading the main purpose and followed by a usage hint. Every word earns its place, with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no output schema) and its role as an aggregate briefing, the description adequately covers the main components and the proactive session-start context. It does not detail output formatting, but for a briefing tool this is arguably unnecessary; the listed item types provide enough for an agent to present the result.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single parameter (studentId), providing a basic 'The student ID' description. The tool description adds no parameter-specific meaning beyond what the schema already states, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Build') and resource ('daily academic summary'), and enumerates the content (due soon, review today, weak topics, urgent items). It distinguishes itself from siblings like get_review_due or get_deadline_timeline by presenting a consolidated proactive briefing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly instructs to call 'at session start for a proactive briefing', providing clear timing context. It does not list alternatives or exclusions, but the proactive briefing framing effectively implies it precedes more targeted queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly indicates this is a read-only retrieval operation ('Get') and specifies what data is returned (confidence trends, study hours, etc.). It does not mention any side effects, permissions, or return format details, but for a simple analytics summary tool, the description sufficiently discloses its non-destructive nature and core output.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the action ('Get a progress analytics summary') and immediately lists the key components. Every word serves a purpose, with no redundancy or unnecessary details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a simple read-only tool with only two parameters, both fully documented in the schema. The description explains what the summary includes, which is sufficient for the agent to understand the tool's role. There is no output schema to clarify return values, but the description's enumeration of summary contents partially compensates. Overall, the description is complete enough for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%: both studentId and days have descriptive text in the input schema. The description adds context about the summary content but does not add additional meaning to the parameters themselves. Baseline 3 is appropriate since the schema already documents both parameters adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: to retrieve a progress analytics summary. It lists specific content areas (confidence trends, study hours, completed sessions, weak areas) that distinguish it from sibling tools like get_mastery_heatmap or get_daily_briefing. The verb 'get' plus resource 'progress analytics summary' is specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by explaining what the summary includes, but it does not explicitly state when to use this tool versus alternatives like get_mastery_heatmap or get_daily_briefing. There are no exclusions or alternative tool mentions, so the guidance is implicit rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility for behavioral disclosure. It implicitly indicates a read-only operation by using 'find' and 'returns', and adds context about spaced-repetition logic. However, it does not explicitly state the absence of side effects, permissions needed, or edge cases like empty results, leaving some gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, consisting of two sentences that are front-loaded with the core purpose and immediately followed by return details. No unnecessary words or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a relatively simple tool with two parameters and no output schema, the description covers the primary purpose and return value. It lacks details on sorting, limits, or meaning of 'effective confidence', but is adequate for the tool's complexity. Sibling tools suggest a broader context, but the description stands on its own.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already describes both parameters (studentId and daysThreshold) with 100% coverage, so the baseline is 3. The description adds minimal extra meaning beyond clarifying that daysThreshold corresponds to 'N days', but does not provide syntax or additional semantics beyond what the schema offers.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool finds concepts not reviewed in at least N days, with a specific verb ('find') and resource ('concepts'). It also notes the return of overdue concepts with effective confidence and days since review, distinguishing it from sibling tools like suggest_review_plan by focusing on overdue detection rather than planning.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool: proactively identifying overdue concepts for spaced repetition. However, it does not explicitly mention alternatives or when not to use it, leaving some ambiguity relative to similar sibling tools like get_daily_briefing or flag_at_risk_topics.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It states the core behavior (authenticate and return token) but does not disclose failure modes, token expiration, or any side effects (e.g., invalidating existing sessions). This is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no unnecessary words. The first sentence starts with the action verb, and the second explains the return value. Perfectly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple authentication tool with no output schema, the description covers the essential purpose and return value. It could add more detail about token usage or error handling, but it is not required for tool selection given the simplicity of the operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for both parameters, so the schema fully defines 'email' and 'password'. The description adds no additional parameter-level meaning beyond what is already in the schema, matching the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Authenticate' and identifies the resource (email and password) and the outcome (session token). It clearly distinguishes this from all sibling tools, which are focused on courses, concepts, and study activities, not authentication.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'for subsequent requests' implies this should be used before other API calls, giving clear context. However, it does not explicitly mention when not to use it or mention any alternative tools, though none exist among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses that matching is fuzzy and within specific fields (subject, topic, note), which is valuable behavioral context. However, it does not mention return format or any read-only guarantees, though the search nature is evident.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the primary action, and contains no redundant information. Every word contributes to conveying the tool's purpose and behavior.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with no output schema or annotations, so the description must convey enough. It lacks explicit information about return values or how studentId scopes the search, and does not mention any side effects. While the purpose is clear, the description could be more complete for an AI agent deciding invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the description adds meaning to the 'query' parameter by explaining the fuzzy match targets (subject, topic, note), going beyond the schema's brief 'Keyword or vague phrase' description. It does not add much for 'studentId' but the schema already describes it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's specific action ('Search previously logged topics') and resource scope, distinguishing it from sibling tools like log_topic (which logs) and get_review_due (which retrieves due topics). It also specifies the matching mechanism (fuzzy match) adding precision.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when needing to find previously logged topics via keyword or vague query, but does not explicitly state when to prefer this over alternatives or provide exclusions. It lacks the explicit 'use this instead of X' guidance seen in higher-scoring examples.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the disclosure burden. It transparently states that asking a question logs an interaction and updates mastery records, and also mentions memory reads and concept identification. It does not mention prerequisites like authentication or error/return behavior, but the key mutating side effects are disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two focused sentences. The first gives the primary action, and the second compactly lists the system's internal steps. Every phrase earns its place with no unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is complex (memory reads, interaction logging, mastery updates) and has no output schema or annotations. The description covers the main behavior and side effects, but it does not specify return format, prerequisites, or validity conditions for the IDs, which would improve completeness for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All three parameters have descriptions in the schema (100% coverage), so the description does not need to add much. It provides no additional parameter-level meaning beyond the schema, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function ('Ask a question about a course topic') and enumerates its internal behaviors (reads memory, answers using course context, identifies relevant concepts, infers confusion, logs interaction, updates mastery), which differentiates it from siblings like explain_concept or get_concept by emphasizing the interactive and mastery-updating nature.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context: use this when a student asks a question about a course topic. However, it does not explicitly contrast with alternatives such as explain_concept or get_concept, nor state when not to use it, so it lacks explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full disclosure burden. It reveals the key side effect: updating mastery confidence and wrong count. It does not mention reversibility, prerequisites, or failure modes, but the primary behavior is clearly disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded, and every sentence provides relevant information without redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple 3-parameter tool with a clear side effect, the description is fairly complete. It lacks a mention of return values or preconditions, but given the simplicity and schema coverage, it is sufficient for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with all three parameters described in the input schema. The description adds no additional parameter-specific detail, so it does not go beyond the structured fields.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Log a quiz or quick-check result for a concept') and the effect on mastery. This distinguishes it from sibling tools like log_topic or record_study_session by focusing specifically on assessment results.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for logging quiz/quick-check outcomes and explains the resulting mastery update. However, it does not explicitly mention alternatives or when-not-to-use, especially relative to sibling tools like record_study_session or log_topic.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of explaining behavior. It discloses that the tool stores the entry with a timestamp and returns an ID, which covers key behavioral aspects. It does not discuss permissions, reversibility, or side effects, but for a logging tool this 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences that front-load the primary action and add only essential behavioral information. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (4 params, no output schema, no annotations), and the description covers purpose, behavior, and return value. It lacks alternative guidance, but given the low complexity, it is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema already documents all four parameters. The description adds no additional parameter-level detail, earning the baseline score of 3 per the rubric.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function with a specific verb and resource: 'Log a topic or doubt a student studied or asked about.' It also specifies the outcome (stores with timestamp, returns id), distinguishing it from write tools like log_quiz_result or record_study_session.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool: when a student studied or asked about a topic or doubt. It provides clear context but does not explicitly reference alternatives or exclusion criteria, which prevents a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing side effects. It explicitly mentions logging the interaction and updating mastery conditionally, which alerts the agent to write operations. It does not detail permission requirements, reversibility, or error handling, but it does convey the primary behavioral traits beyond the schema. No contradiction with annotations exists.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that uses a colon to introduce a list of distinct actions. Every clause adds meaningful detail, with no unnecessary words or repetition. It is concise despite covering multiple aspects of the tool's behavior.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has moderate complexity with conditional side effects, and there is no output schema to define the return value. The description only mentions 'a structured spoken response' without specifying its format or contents, leaving a gap for the agent. It also does not mention potential failure scenarios (e.g., invalid sessionId) or how it relates to session lifecycle tools. This makes it not fully complete, but the core behavior is still understandable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema descriptions cover all 4 parameters (100% coverage), giving a baseline of 3. The tool description adds extra meaning by explaining that the transcript is used for keyword-based intent detection and that audioDurationSeconds contributes to 'voice-specific fields' in logging. This enriches the semantic understanding of the parameters, justifying a slightly higher score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's main function with a specific verb ('processes') and resource ('spoken input from a voice session'), then enumerates the sub-actions (intent detection, logging, mastery update, response generation). This differentiates it from sibling tools like start_voice_session and end_voice_session, which manage session lifecycle rather than handle input.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies that this tool is used during a voice session when a student provides spoken input. It does not explicitly name alternatives or exclusions, but the context is clear enough—it is the core processor for voice utterances. The lack of explicit alternative references lowers it from 5, but it is still well-grounded.
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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