aluris-caselibrary-mcp

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Each tool has a distinct purpose: precise filtering, rule finding, citation generation, detail retrieval, statistics, authoritative search, semantic search, and sync. There is minor potential confusion between filter_cases and search_authoritative_cases, but descriptions clarify the difference.
Naming Consistency4/5
Most tools follow a verb_noun pattern in snake_case (e.g., filter_cases, get_case_detail). 'library_stats' and 'sync_now' deviate slightly, but overall naming is predictable and readable.
Tool Count5/5
Eight tools cover the essential operations for a case library: search, filter, detail, rules, citations, statistics, and sync. The count is well-scoped without being excessive or insufficient.
Completeness4/5
The tool set covers core use cases like searching, filtering, and retrieving case details and rules. It lacks explicit tools for adding or deleting cases, but that aligns with a read-only library with external sync. Minor gap in batch operations.
Average 3.6/5 across 8 of 8 tools scored. Lowest: 2.7/5.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
- 22 commits in the last 12 weeks
- Last stable release on June 8, 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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description does not disclose behavioral traits such as authentication, rate limits, or output format. It only states what the tool does without further context.
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 one concise sentence with examples, making it easy to read. However, it could be slightly more structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 6 parameters and no output schema, the description is incomplete. It does not explain optional parameters like cat, source, or year range, which are critical for effective use.
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?
Schema description coverage is only 33% (only 'issue' described). The description adds no parameter-level details beyond the schema, failing to compensate for the low coverage.
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 finds authoritative cases and adjudication rules by legal issue, with examples. It is specific about the resource and action, but could be more distinct from siblings like search_authoritative_cases.
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?
No explicit guidance on when to use this tool versus alternatives. Siblings exist but no differentiation is provided.
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?
With no annotations, the description carries full burden. It mentions exact filtering behavior but does not disclose whether the tool is read-only, how results are returned, pagination, or error handling. This is insufficient for a 7-parameter 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 a single, concise sentence that front-loads the key purpose. It contains no redundant information. However, additional structure (e.g., listing dimensions) could improve clarity without adding length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 7 parameters, no output schema, and no annotations, the description is too brief. It fails to explain return values, parameter interactions, or typical use cases, leaving significant gaps for an agent to use 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 description lists dimensions (court, year, cause, source) that map to some parameters, adding context beyond the schema where only 'limit' has a description. However, it does not explain parameter formats, allowed values, or the 'cat' parameter. Given low schema coverage (14%), the description partially compensates but is not comprehensive.
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 filters cases precisely by conditions and explicitly says it does not perform semantic search, which distinguishes it from siblings like search_authoritative_cases and search_similar_cases. The verb 'filter' and resource 'cases' are specific.
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 for exact filtering (no semantic search) but does not explicitly state when to use this tool versus alternatives or provide exclusion criteria. The guidance is implicit from the '不做语义搜索' note, but lacks direct 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. The description notes that results include authority type and citation value, and implies semantic search (not keyword). However, it lacks details on authentication, rate limits, or data scope.
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 clear sentence in Chinese, front-loading the purpose. It is concise but could benefit from a more structured format to include parameter details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite 7 parameters and no output schema, the description focuses only on the query functionality. It does not explain how filtering parameters (cat, source, year, court) interact or describe output format. Underdescribed 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 coverage is 100%, so baseline is 3. The description adds minimal meaning beyond the schema, only contextualizing the 'query' parameter with an example. No additional semantics for filtering 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 performs semantic retrieval of similar cases using natural language input, with an example. It distinguishes itself from sibling tools like filter_cases (structured filtering) and search_authoritative_cases (specific type search).
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 for natural language queries to find similar cases, but does not explicitly state when to use this tool versus alternatives like filter_cases or search_authoritative_cases. No exclusions or prerequisites are mentioned.
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 states generation of a citation abstract but does not disclose side effects, permissions, rate limits, or whether it is read-only. The description is adequate but lacks depth on behavioral traits.
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 sentence that efficiently conveys the tool's purpose and context. It is front-loaded and free of fluff, though slightly lengthy in Chinese characters.
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 tool with one simple parameter and no output schema, the description provides enough context: input from search results, output for reports. It covers the main use case and the sibling tools list gives broader context. Minor lack of output format details.
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 a clear description of 'local_id'. The description adds context that the number comes from search results and output is a citation abstract, but does not add meaning beyond the schema's parameter description.
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 verb (generate), resource (case citation brief), and context (from search result case number). It distinguishes from sibling tools like get_case_detail and search_similar_cases by focusing on generating a citation abstract for reports and opinions.
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 a case number is available from search results and a brief citation is needed, but it lacks explicit 'when to use' vs 'when not to use' guidance or mention of alternatives. The context from sibling tools helps but is not 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?
No annotations provided, so description carries the burden. It indicates a read operation and lists returned content, but lacks details on side effects, permissions, or rate limits. Adequate for a simple get but not extra informative.
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?
Single sentence that is front-loaded with the action and resource. Efficient with no fluff, but could include brief usage note.
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 (1 param, no output schema), the description suffices for correct invocation. However, it does not clarify the output format, though that might be obvious.
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 description adds little beyond the schema's own description of local_id as case number from search results. 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?
Description clearly specifies the action (获取完整信息) and resource (案例编号), and lists specific content (基本案情、裁判理由、全文等). It distinguishes from sibling tools that focus on searching or filtering cases.
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?
Implies usage after search results are obtained, but does not explicitly state when to use this tool over siblings or mention any 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?
Discloses that sync may take minutes and returns the count of new cases, and the dry_run parameter hints at idempotency. However, with no annotations, it does not fully describe side effects like whether data is modified or concurrent usage implications.
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 concise sentences, front-loaded with the core purpose, and each sentence provides necessary information without waste.
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?
Adequately explains purpose, inputs, latency, and return value for a simple tool with no output schema. Could elaborate on the return format and the meaning of source options.
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 covers both parameters with descriptions; the tool description adds no additional meaning beyond what the schema already provides.
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 the tool triggers an incremental sync from rmfyalk, retrieves latest cases, and returns the count. The verb and resource are specific, and it differentiates from sibling tools that are for filtering, searching, or getting details.
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?
Implicitly indicates manual triggering but provides no explicit guidance on when to use this tool versus alternatives, nor any exclusion 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?
No annotations are provided, so the description carries full burden. It discloses the outcome (statistics) but does not mention performance, data freshness, or authorization requirements. The tool is simple and read-only, so a moderate score is appropriate.
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 sentence that is front-loaded with the main action and lists key statistics clearly. It is concise with no redundant information.
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 has no parameters, no output schema, and no annotations, the description provides sufficient context for a simple statistics reporting tool. It could be more detailed about output format or error cases, but it is adequately complete for its simplicity.
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?
There are zero parameters, and schema coverage is 100%. The description adds value by explaining what the output contains (total count, distributions, year range), which is beyond the schema's empty definition.
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: viewing case library statistics including total count, category distribution, data source distribution, and year range. It distinguishes itself from sibling tools like filter_cases or get_case_detail, which are for specific queries or details.
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 for obtaining aggregate statistics, but does not explicitly state when to use this tool over alternatives like filter_cases or search functions. No exclusion criteria or contextual cues 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 carries full burden. It discloses that the tool prioritizes authoritative sources and annotates results with authority type and citation value. However, it does not detail other behavioral aspects like rate limits, authentication, or handling of edge cases.
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 main action and includes key details about prioritization and annotations. Every phrase serves a purpose with no 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 7 parameters and no output schema, the description gives a reasonable overview but lacks details on expected output format, pagination, or limitations. It hints at output annotations but is not fully comprehensive for a complex search 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 description coverage is 100%, so the baseline is 3. The description does not add extra meaning beyond the schema; it only provides a high-level purpose. No parameter-specific elaboration is given.
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 it retrieves authoritative cases and judicial rules, prioritizing specific high-level sources like the Supreme Court and annotates authority type and citation value. This distinguishes it from sibling tools like 'filter_cases' or 'search_similar_cases', which lack this focus.
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 the tool is for authoritative case and rule search from specific sources. While it provides clear context for when to use it, it does not explicitly mention when not to use it or suggest alternatives (e.g., for general search).
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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