aliyun-sls-mcp

Server Quality Checklist

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Latest release: v0.1.3
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: list_projects and list_logstores are for discovery, query_logs and query_logs_sql are for different query methods, get_log_histogram provides time-series analysis, and get_context_logs offers contextual retrieval. The descriptions explicitly differentiate their use cases, eliminating any ambiguity.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with snake_case (e.g., list_projects, query_logs, get_log_histogram). The naming is predictable and readable throughout, making it easy for agents to understand the action and target resource without confusion.
Tool Count5/5
With 6 tools, the server is well-scoped for log analysis and management in Aliyun SLS. Each tool earns its place by covering essential operations like discovery, querying, aggregation, and contextual analysis, without being overly sparse or bloated for the domain.
Completeness4/5
The toolset provides strong coverage for core log analysis workflows, including discovery, querying, aggregation, and contextual retrieval. A minor gap exists in lacking explicit CRUD operations for managing logstores or projects (e.g., create/delete), but this is reasonable for a read-focused analysis server, and agents can still perform most tasks effectively.
Average 4/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 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.
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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 are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool lists logstores but doesn't cover critical aspects like whether it's a read-only operation, potential rate limits, authentication needs, or what the output format looks like. This leaves significant gaps for an agent to understand the tool's 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 two concise sentences with zero waste—each sentence directly contributes to understanding the tool's purpose and usage. It's front-loaded with the core action and efficiently 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?
Given the tool's low complexity (simple list operation) and high schema coverage, the description is somewhat complete but lacks output details (no output schema) and behavioral context. It covers the basics but doesn't fully compensate for the absence of annotations, leaving room for improvement in transparency.
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 both parameters ('project' and 'region') adequately. The description adds no additional parameter semantics beyond what the schema provides, which meets the baseline for high schema 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 verb ('List') and resource ('all logstores within an SLS project'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'list_projects' beyond the resource scope, which prevents a perfect score.
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 usage ('to discover available logstores before querying logs'), which implicitly suggests when to use this tool. It doesn't explicitly state when not to use it or name alternatives, but the guidance is helpful and contextually relevant.
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 explains the return format ('Returns a visual histogram showing log volume over time') and practical use case, but doesn't cover aspects like rate limits, authentication needs, error handling, or whether this is a read-only operation (though 'Get' implies it).
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 efficiently structured in three sentences: the core function, the return format, and the use case. Every sentence adds value without redundancy, making it appropriately sized and front-loaded with essential information.
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 tool with 7 parameters, no annotations, and no output schema, the description provides adequate context on purpose and usage but lacks details on behavioral aspects like permissions, rate limits, or error responses. It's complete enough for basic understanding but has gaps given the 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%, so the schema already documents all 7 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline of 3 when the schema does the heavy lifting.
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 specific verbs ('Get the time-series distribution of log counts matching a query') and resource ('log counts'), distinguishing it from siblings like get_context_logs or query_logs by focusing on histogram/visual distribution rather than raw log retrieval or SQL queries.
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 this tool ('Useful for identifying when errors spiked or when unusual activity occurred'), but it doesn't explicitly state when not to use it or name alternatives among the sibling tools, which would be needed for a score of 5.
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 effectively describes the core functionality (querying logs with time range and filters) and mentions the return format ('formatted log entries'), but lacks details about authentication requirements, rate limits, pagination behavior, error handling, or whether this is a read-only operation (though implied by 'query').
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 efficiently structured in two sentences: the first states the core functionality, the second provides usage context and syntax examples. Every element serves a purpose with zero wasted words, making it easy to parse while remaining comprehensive.
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 complex tool with 8 parameters and no output schema, the description provides adequate context about what the tool does and when to use it, but lacks details about the return format (beyond 'formatted log entries'), error conditions, authentication requirements, and behavioral constraints. With no annotations and no output schema, more completeness would be beneficial for this query 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 schema already documents all 8 parameters thoroughly. The description adds minimal value beyond the schema by mentioning 'time range and optional filter query' and providing SLS syntax examples, but doesn't explain parameter interactions (e.g., time_range vs from/to override) or add significant semantic context not already in the schema descriptions.
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 specific verb ('query'), resource ('log data from an SLS logstore'), and scope ('with a time range and optional filter query'). It distinguishes itself from siblings by specifying it returns formatted log entries for debugging/analysis, unlike get_log_histogram (statistical) or query_logs_sql (SQL-based).
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 this tool ('for debugging, error investigation, and log analysis') and mentions SLS query syntax, which helps differentiate it from SQL-based alternatives. However, it doesn't explicitly state when NOT to use it or provide direct comparisons with specific sibling tools like get_context_logs or query_logs_sql.
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 mentions the tool's analytical purpose and provides an example query, but doesn't cover important behavioral aspects like authentication requirements, rate limits, error handling, or what happens when queries fail. The description adds some value but leaves significant 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 perfectly sized with two sentences: the first states purpose and optimal use case, the second provides a concrete example. Every element earns its place, and the information is front-loaded with the core functionality stated immediately.
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 complex SQL execution tool with 6 parameters and no output schema, the description provides adequate purpose and usage context but lacks important behavioral details. Without annotations covering safety, authentication, or limits, and without an output schema, the description should do more to prepare the agent for proper tool invocation and result interpretation.
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 schema already documents all 6 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema descriptions. It mentions SQL queries generally but doesn't provide additional parameter semantics, earning the baseline score for high 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 action ('Execute a SQL query'), target resource ('against an SLS project'), and purpose ('for log analysis and aggregation'). It distinguishes from sibling tools like 'get_context_logs' or 'get_log_histogram' by specifying SQL-based analysis capabilities. The example further clarifies the scope.
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 about when to use this tool ('Best for counting, grouping, statistical analysis'), but doesn't explicitly mention when NOT to use it or name specific alternatives among the sibling tools. It implies usage for complex analytical queries versus simpler retrieval operations.
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 the full burden of behavioral disclosure. It explains the tool's purpose and workflow but lacks details on permissions, rate limits, error handling, or response format. The description doesn't contradict annotations, but it doesn't fully compensate for the absence of structured behavioral hints.
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 well-structured in two sentences. The first sentence states the purpose, and the second provides usage guidelines and context. Every sentence adds value with no wasted words, and it's front-loaded with the core functionality.
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 tool's complexity (7 parameters, no annotations, no output schema), the description is adequate but incomplete. It explains the purpose and workflow but lacks details on output format, error cases, or behavioral constraints. Without annotations or output schema, more context would be helpful for safe 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 description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by mentioning pack_id and pack_meta fields but doesn't provide additional syntax or format details. This meets the baseline for high 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: 'Retrieve log lines before and after a specific log entry using its pack_id and pack_meta.' It specifies the verb ('retrieve'), resource ('log lines'), and scope ('before and after a specific log entry'), and distinguishes it from sibling tools by mentioning query_logs as a prerequisite step.
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: 'First query logs with query_logs to find a log entry, then use its __tag__:__pack_id__ and __tag__:__pack_meta__ fields to get surrounding context.' It names the alternative tool (query_logs) and specifies when to use this tool (after finding a log entry) versus when not to (for initial log 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 full burden and does well by explaining the default behavior (uses env variables when regions omitted) and the tool's purpose in the workflow. However, it doesn't mention potential rate limits, authentication needs, or pagination 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?
Three tightly focused sentences with zero waste: first states core functionality, second explains parameter usage, third provides usage context. Every sentence earns its place by adding distinct 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?
For a single-parameter read operation with no annotations and no output schema, the description provides good context about the tool's role in the workflow and default behavior. However, without output schema, it doesn't describe what the returned project list looks like.
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 schema already fully documents the single parameter. The description adds minimal value beyond the schema by mentioning the parameter can be omitted, which is already implied by the schema's optional nature.
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 ('List') and resource ('SLS projects'), specifies the scope ('in one or more Alibaba Cloud regions'), and distinguishes from siblings by mentioning its discovery purpose before querying logs (unlike sibling tools that directly query logs).
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?
It explicitly states when to use this tool ('to discover available projects before querying logs'), provides an alternative scenario (if regions omitted, uses env variables), and distinguishes from sibling tools by positioning it as a prerequisite step for log querying operations.
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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