bunpro-mcp

by Muslinmin

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose: adding items, getting specific items, obtaining practice pools, getting review queues, importing/exporting, and submitting grades. No two tools overlap in functionality.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (e.g., add_item, get_item, get_practice_pool). The naming is predictable and uniform.
Tool Count5/5
With 6 tools, the set is well-scoped for a Japanese SRS system. It covers the essential operations without being overly large or sparse.
Completeness4/5
The tools cover core workflows: adding, retrieving, reviewing, practicing, grading, and import/export. Missing explicit update/delete tools is a minor gap, but the import/export can handle bulk changes, and the overall surface is satisfactory.
Average 4.1/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 9 commits in the last 12 weeks
- Last stable release on July 15, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It describes return values (grammar/vocab, freshness, mistake notes) but does not disclose mutability, auth requirements, or other side effects. Adequate but not comprehensive.
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?
Concise, front-loaded with main action, and no unnecessary words. Every sentence adds value.
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 complexity and presence of output schema, description covers purpose, usage, and output format. Lacks parameter details, but overall sufficient for an agent to understand when and why to use it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning no descriptions in schema. The description does not explain the 'kind' or 'limit' parameters, leaving the agent without guidance on how to use them 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 retrieves Japanese items needing review, ordered by priority. It specifies the items are grammar and vocabulary with freshness and mistake notes, distinguishing it from sibling tools like get_practice_pool.
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?
Explicitly advises calling at the start of a review session, providing clear context. Does not explicitly distinguish from siblings, but the usage is well-defined.
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?
The description indicates a read operation without any destructive effects, which is sufficient given no annotations. However, it could be more explicit about being non-destructive and about prerequisites like needing a valid item_id.
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 action and return content. No superfluous 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?
Given the presence of an output schema, the description adequately covers what is returned (comprehensive info including notes). It lacks details on error behavior or edge cases, but is sufficient for a simple retrieval tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter item_id is not described in the schema (0% coverage), and the description does not elaborate on how to obtain or format the ID. The agent is left to infer that item_id is a unique identifier, but no additional context is provided.
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 retrieves everything known about a single Japanese grammar point or word, including the learner's notes. It distinguishes from siblings like add_item (create) and get_practice_pool (list).
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 full details on a specific item are needed, but it does not explicitly state when to use it versus alternatives or provide when-not guidance. The sibling tools have different purposes, so it is reasonably clear.
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?
Describes key behaviors: returns mastered items with meaning/reading, no pre-filtering by theme. No annotations exist, so description carries full burden. Could mention ordering or size constraints, but limit parameter addresses size. Good overall.
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?
Information-dense with essential workflow details. Slightly verbose in places (e.g., repeating contrast with review queue), but front-loaded with main purpose. Efficient overall.
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?
Covers pre-use, usage, and post-use steps, and references output schema by mentioning meaning/reading. Missing parameter explanations, but otherwise complete for a tool with output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage 0% and description does not explain the 'kind' parameter (enum of grammar/vocab/both) or 'limit'. Only workflow implies both word types, but no clarification. Fails to add meaning beyond 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?
Clearly states it returns mastered items for active practice, contrasting with review queue. Specific verb 'Get', resource 'pool of mastered words/grammar', and purpose 'active-use practice'. Distinguishes from sibling get_review_queue.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says when to use ('when learner wants to be tested on known words, not review forgotten ones') and provides a full 6-step workflow including after-call actions like item selection and submit_grades. Also notes pool is not pre-filtered.
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 discloses the safety-oriented dry-run step but omits details on data merging, overwrite behavior, or error handling. The existence of an output schema is noted but not elaborated.
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, concise and front-loaded. The first sentence states the purpose; the second provides critical usage instruction without redundancy.
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?
With two parameters and an output schema available, the description plus schema provide adequate information for correct invocation. The workflow is clear, though error conditions or report format are not detailed.
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 0%, so the description must compensate. It adds context for parameter dry_run by stating when to set it to true and that a report is shown, but it does not explain csv_path beyond its name. Partial value added.
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: 'Import a Bunpro CSV export into the vault.' The verb 'import' and resource 'Bunpro CSV export' are specific, and the tool is distinguished from sibling tools like add_item or get_item by its import nature.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'ALWAYS run with dry_run=true first and show the learner the report before running for real.' This instructs the agent on the safe workflow and leverages the dry_run parameter.
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. It explains that the AI should fill in reading, meaning, JLPT from its own knowledge, and put context into note. It does not mention return values or side effects beyond adding, but the expected behavior is well communicated.
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 a single paragraph that is front-loaded with purpose and provides detailed guidance without extraneous words. Each sentence adds value, though the structure could be improved with bullet points for clarity.
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 complexity (8 parameters, 2 required) and no annotations, the description is fairly complete. It covers usage scenarios, parameter guidance, and edge cases (e.g., setting progress for known items). Missing details on output schema are acceptable as output schema exists separately.
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 description coverage is 0%, so the description must explain parameter semantics. It adds meaning for reading, meaning, level, note, progress, and kind, covering most parameters except tags. This provides significant value beyond the bare 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 clearly states 'Add a Japanese grammar point or word', specifying the verb 'Add' and the resource 'Japanese grammar point or word'. It distinguishes itself from sibling tools like get_item by focusing on creation. The purpose is unambiguous and specific.
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: 'the learner has just encountered' a word. It gives guidelines on handling known vs new items and how to set progress. However, it does not explicitly state when not to use the tool or mention alternatives among siblings, which prevents a higher score.
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?
No annotations provided, so description carries full burden. Discloses that tool should be called once at end of session, not incrementally. Explains grade meanings and error_note requirement. Lacks details on idempotency or side effects, but appropriate for a simple write 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?
Two succinct sentences with front-loaded purpose. Every sentence adds value: purpose, timing/scope, grade definitions, error_note instruction. 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?
Given one parameter with nested structure and existence of output schema (not shown), description covers timing, grade meaning, and error_note usage. The output schema likely covers return values. Could mention idempotency or duplicate handling, but adequate for a submission tool.
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 description coverage is 0%, so description must compensate. Explains that grades array covers every item observed, defines grade values (1-4), and specifies when to include error_note. Does not describe item_id format, but context is clear.
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?
Description clearly states verb 'Record' and resource 'learner performance on items during a review'. Differentiates from sibling tools (add_item, get_item, etc.) which serve different purposes.
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?
Explicitly says 'Call this at the END of a review session, once, with every item you observed', providing clear timing and scope. Does not mention alternatives but no similar siblings exist.
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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