MCP Hub

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation2/5
Multiple tools have overlapping or unclear boundaries. For example, ShallowCodeResearch_agent_research_request appears to combine research, code generation, and execution, which overlaps with ShallowCodeResearch_agent_code_generator and ShallowCodeResearch_code_runner_wrapper. The distinction between ShallowCodeResearch_agent_llm_processor and ShallowCodeResearch_agent_question_enhancer is also vague, as both involve LLM-based text processing. This ambiguity could lead to agent misselection.
Naming Consistency4/5
The naming follows a mostly consistent pattern with a prefix 'ShallowCodeResearch_' and snake_case throughout. However, there are minor deviations: ShallowCodeResearch_agent_research_request uses 'research_request' while others use more specific terms like 'code_generator', and ShallowCodeResearch_get_sandbox_pool_status_sync includes 'sync' as a suffix, which is not present in other getter tools. Overall, the naming is readable and largely predictable.
Tool Count4/5
With 11 tools, the count is reasonable for a server focused on code research and system monitoring. It covers a range of functions from code generation and execution to health checks, which aligns with the apparent scope of an MCP Hub. While not perfectly scoped (some tools could be consolidated), it does not feel overly heavy or thin for the domain.
Completeness3/5
The tool surface has notable gaps in coverage. For a code research domain, there are tools for generation, execution, and monitoring, but missing operations like code editing, debugging, or version control integration. The set includes getters for status and metrics but lacks corresponding setters or configuration tools. Agents may encounter dead ends when trying to perform comprehensive coding workflows beyond basic generation and execution.
Average 2.9/5 across 11 of 11 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 is failing
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?
No annotations are provided, so the description carries full burden. It mentions it's a wrapper and returns a tuple, but doesn't disclose behavioral traits like whether it's read-only or mutating, authentication needs, rate limits, error handling, or what the 'generation result' entails. For a tool with no annotations, this leaves significant gaps in understanding its behavior.
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 appropriately concise with two sentences that directly state the tool's function and return value. It's front-loaded with the main purpose. However, the second sentence about the return tuple could be slightly clearer about what 'generation result' means, but overall it's efficient with minimal waste.
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?
Given no annotations, no output schema, and a tool that generates code (which implies potential complexity), the description is incomplete. It doesn't explain the return format beyond 'tuple', what errors might occur, or how the wrapper interacts with CodeGeneratorAgent. For a code generation tool with no structured output documentation, more context is needed.
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 both parameters (user_request and grounded_context). The description adds no additional meaning about parameters beyond what the schema provides. With high schema coverage, the baseline is 3 even without param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states it's a wrapper for CodeGeneratorAgent to generate Python code, which provides a basic purpose (verb+resource). However, it doesn't differentiate from sibling tools like ShallowCodeResearch_code_runner_wrapper or explain what makes this specific to code generation versus other processing tools. The purpose is clear but lacks sibling differentiation.
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 guidance is provided on when to use this tool versus alternatives. The description doesn't mention when this wrapper should be chosen over other code-related tools (like code_runner_wrapper) or when to use it versus general processing tools (like llm_processor). There's no context about prerequisites or typical use cases.
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 provided, the description carries full burden for behavioral disclosure. It mentions 'Returns: LLM processing result with output and metadata' which gives some output information, but doesn't describe important behavioral aspects like rate limits, authentication requirements, error conditions, processing time, or what happens with invalid inputs. For a tool that processes text with an LLM, this leaves significant gaps in understanding how it behaves.
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 appropriately concise at two sentences. The first sentence states the core function, and the second describes the return value. There's no wasted text or unnecessary elaboration. However, it could be slightly more front-loaded by integrating the return information into the main purpose statement.
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 3 parameters, no annotations, and no output schema, the description is insufficiently complete. While it mentions the return includes 'output and metadata', it doesn't specify what format this takes or what the metadata contains. Given the complexity of LLM processing and the lack of structured output documentation, the description should provide more context about expected behavior and results.
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 the schema already documents all three parameters thoroughly. The description adds no additional parameter information beyond what's in the schema. It doesn't explain parameter interactions, provide examples, or add context about how parameters affect processing. This meets the baseline of 3 when schema coverage is complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'process text with LLM' which provides a basic purpose, but it's vague about what 'process' entails. It distinguishes from some siblings like 'citation_formatter' or 'web_search' by mentioning LLM processing, but doesn't clearly differentiate from 'question_enhancer' or 'code_generator' which might also use LLMs. The description lacks specificity about the nature of the processing beyond the wrapper function.
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 guidance is provided about when to use this tool versus alternatives. The description doesn't mention when this wrapper should be chosen over direct LLM calls or other processing tools in the sibling list. There's no context about appropriate use cases, prerequisites, or limitations that would help an agent decide between this and similar tools.
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 provided, so the description carries the full burden of behavioral disclosure. It mentions that the tool 'Returns: Enhanced question result with sub-questions,' which gives some output information, but lacks details on how the enhancement works (e.g., is it AI-based, does it modify the input, are there rate limits or authentication needs?). This is a significant gap for a tool with no annotations, as it doesn't fully describe behavioral traits beyond the basic return statement.
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 appropriately sized with two sentences: one stating the wrapper purpose and another specifying the return value. It's front-loaded with the main function and avoids unnecessary details. However, it could be slightly more structured by explicitly separating purpose from output, but overall it's efficient with minimal waste.
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?
Given the tool's complexity (a wrapper agent for enhancement), lack of annotations, and no output schema, the description is incomplete. It mentions the return includes 'Enhanced question result with sub-questions,' but doesn't explain the format or content of these results. For a tool that processes user requests, more context on behavior, error handling, or examples would be needed to be fully helpful, especially with no structured fields to rely on.
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 adds no meaning beyond what the input schema provides. The schema has 1 parameter with 100% coverage (a 'user_request' string described as 'The original user request to enhance'), and the description doesn't elaborate on this parameter's usage, format, or constraints. With high schema coverage, the baseline is 3, as the description doesn't compensate but also doesn't detract from the schema's information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Wrapper for QuestionEnhancerAgent to provide question enhancement' which clarifies the tool's function as a wrapper that enhances questions. However, it's somewhat vague about what 'enhancement' entails and doesn't distinguish this tool from its siblings like 'ShallowCodeResearch_agent_research_request' or 'ShallowCodeResearch_agent_llm_processor' which might also process user requests. The description provides a basic purpose but lacks specificity about the enhancement mechanism.
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 guidance on when to use this tool versus alternatives. It doesn't mention any specific contexts, prerequisites, or exclusions, nor does it reference sibling tools. For example, it doesn't clarify if this should be used for initial query refinement versus other processing steps, leaving the agent with no usage instructions beyond the generic purpose.
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 provided, the description carries the full burden of behavioral disclosure. It describes the tool's actions (research, generate code, execute, return summary) but lacks details on permissions, rate limits, error handling, or what 'clean summary' entails. For a tool that involves code generation and execution, this is a significant gap in transparency, though it doesn't contradict any annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, which is appropriately sized, but it's somewhat repetitive (e.g., 'This is an mcp server function that responds to research coding requests from users' restates the purpose). It could be more front-loaded by eliminating redundancy, but it's not overly verbose or poorly 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?
Given the tool's complexity (involving research, code generation, and execution) and the lack of annotations and output schema, the description is incomplete. It doesn't explain the return format beyond 'a tuple containing the JSON result from the orchestrator and a clean summary,' leaving the agent uncertain about results. For such a multifaceted tool, more detail is needed to be fully helpful.
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 has 100% description coverage for its single parameter ('user_request'), so the schema already documents it fully. The description adds no additional meaning or context about the parameter beyond what's in the schema, such as examples or constraints. This meets the baseline for high schema coverage but doesn't enhance understanding.
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: 'researches a coding request from the user, generates code, executes it, and returns a clean summary of the results.' It specifies the verb (research, generate, execute) and resource (coding request), but doesn't explicitly differentiate from sibling tools like 'ShallowCodeResearch_agent_code_generator' or 'ShallowCodeResearch_code_runner_wrapper', which might handle similar aspects. This makes it clear but not fully sibling-distinctive.
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 mentions it's for 'researching a coding request,' but doesn't specify contexts, prerequisites, or exclusions compared to siblings like 'ShallowCodeResearch_agent_web_search' or 'ShallowCodeResearch_agent_llm_processor.' This lack of explicit usage instructions leaves the agent with minimal direction.
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 provided, the description carries the full burden of behavioral disclosure. It mentions the tool is a 'wrapper for CitationFormatterAgent' and returns formatted citations, but doesn't describe what 'wrapper' means operationally, whether it makes external API calls, has rate limits, requires specific permissions, or what happens with malformed input. For a tool with zero annotation coverage, this leaves significant behavioral gaps.
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 appropriately concise with two sentences that directly state the tool's function and output format. It's front-loaded with the core purpose and avoids unnecessary elaboration. However, it could be slightly more structured by explicitly separating purpose from output details.
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 has no annotations, no output schema, and a simple single parameter with good schema coverage, the description provides basic completeness about what the tool does and its output format. However, for a tool that presumably processes text and returns formatted citations, more detail about behavioral characteristics (like error handling or processing constraints) would improve 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 has 100% description coverage, with the single parameter 'text_block' clearly documented as 'The text containing URLs to cite'. The description adds that it formats citations and returns APA-style references, which provides context about what the parameter should contain, but doesn't add significant semantic value beyond what the schema already provides. Baseline 3 is appropriate when schema coverage is high.
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 as 'format citations' and specifies it returns 'APA-style references', which provides specific verb+resource information. However, it doesn't explicitly differentiate from sibling tools like 'ShallowCodeResearch_agent_question_enhancer' or 'ShallowCodeResearch_agent_research_request' that might also handle citation-related tasks.
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 doesn't mention when to choose this citation formatter over other tools in the server or what specific scenarios it's designed for. The only implied usage is formatting citations, but no explicit context or exclusions are 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 provided, the description carries the full burden of behavioral disclosure. It mentions that the tool returns 'web search results with summaries and URLs', which gives some output context, but lacks details on rate limits, authentication needs, error handling, or whether it's read-only or mutative. For a web search tool, this is a significant gap in transparency.
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 concise and front-loaded, consisting of two clear sentences that state the tool's function and return value. There's no unnecessary information, making it efficient, though it could be slightly more structured by explicitly separating purpose from output details.
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 moderate complexity (web search with one parameter) and lack of annotations and output schema, the description is minimally adequate. It covers the basic purpose and return format but misses behavioral details like pagination, result limits, or error cases. Without an output schema, more detail on return values would be helpful, but it's not entirely 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?
The input schema has 100% description coverage, with the 'query' parameter fully documented. The description doesn't add any additional meaning beyond what the schema provides, such as query formatting tips or examples. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
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: it's a wrapper for WebSearchAgent that performs web searches and returns results with summaries and URLs. It specifies the verb ('perform web searches') and resource ('web search results'), though it doesn't explicitly differentiate from siblings like 'ShallowCodeResearch_agent_question_enhancer' or 'ShallowCodeResearch_agent_research_request' which might also involve search-related functionality.
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 doesn't mention any specific contexts, prerequisites, or exclusions, nor does it reference sibling tools that might handle similar tasks, leaving the agent to infer usage based on the tool name alone.
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 provided, so the description carries the full burden of behavioral disclosure. While 'Get' implies a read-only operation, the description doesn't specify what kind of cache is involved, what statistics are returned, whether this affects system performance, or what format the output takes. For a monitoring tool with zero annotation coverage, this leaves significant behavioral questions unanswered.
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 extremely concise at just 5 words ('Get cache status and statistics'). Every word earns its place by specifying the action, resource, and scope. There's no wasted language or unnecessary elaboration for such a simple tool.
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?
Given that this is a monitoring tool with no annotations and no output schema, the description should provide more context about what 'cache status and statistics' actually means. Without knowing what cache system this refers to, what metrics are included, or what format the output takes, the description leaves too many questions unanswered for effective tool selection and use.
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 tool has 0 parameters with 100% schema description coverage, so the schema fully documents the parameter situation. The description appropriately doesn't discuss parameters since none exist. The baseline for 0 parameters with complete schema coverage is 4, as there's no parameter information to add beyond what's already clear from the structured data.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get cache status and statistics' clearly states the verb ('Get') and resource ('cache status and statistics'), making the purpose understandable. However, it doesn't differentiate this tool from its sibling tools like 'get_health_status' or 'get_performance_metrics' - all appear to be monitoring/status tools, so the distinction isn't clear from the description alone.
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. With multiple sibling tools that appear to serve monitoring functions (get_health_status, get_performance_metrics, get_sandbox_pool_status_sync), there's no indication of what specific cache information this provides or when it should be preferred over other status-checking tools.
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 provided, so the description carries full burden. It mentions 'synchronous wrapper' which implies blocking behavior and possibly that there's an asynchronous version, but doesn't disclose what 'sandbox pool' means, what status information is returned, whether this requires permissions, or any rate limits. For a status-checking tool with zero annotation coverage, this leaves significant behavioral 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 a single, efficient sentence with zero waste. It's appropriately sized for a no-parameter tool and front-loads the essential information (synchronous wrapper for sandbox pool status). Every word earns its place.
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?
Given the tool's apparent purpose (checking system status) with no annotations and no output schema, the description is incomplete. It doesn't explain what 'sandbox pool' is, what status information is returned, or why this check matters. For a status tool that likely returns important system information, this leaves too many unanswered questions about what the agent can expect.
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?
With 0 parameters and 100% schema description coverage, the baseline is 4. The description doesn't need to explain parameters since none exist, and it correctly doesn't attempt to describe non-existent parameters. No additional parameter semantics are needed or provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states it's a 'synchronous wrapper for sandbox pool status', which provides a basic purpose (checking status) but lacks specificity about what 'sandbox pool' refers to or what status information is returned. It distinguishes from most siblings by focusing on status rather than agent tasks or other system checks, but doesn't clearly differentiate from similar status-checking tools like get_cache_status or get_health_status.
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 guidance is provided about when to use this tool versus alternatives. The description doesn't mention when this status check is needed, what triggers its use, or how it differs from other status-checking siblings like get_cache_status or get_health_status. The agent receives no usage context.
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 adds useful context: it ensures sandbox spawning and readiness, uses async execution with warm pool, and provides user-friendly error messages. However, it lacks details on permissions, rate limits, or what happens in edge cases like timeouts or resource constraints.
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 front-loaded and efficient, using three sentences to cover purpose, process, and return value. Each sentence adds value, with no redundant information, though it could be slightly more structured for clarity.
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 annotations and no output schema, the description is moderately complete. It explains the execution process and return values (result or error message), but for a code execution tool, it lacks details on sandbox environment, security implications, or output format specifics, which are important for contextual understanding.
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 has 100% coverage, documenting the single parameter 'code_or_obj' as a string for code or object execution. The description doesn't add any semantic details beyond this, such as examples or constraints on the code format. Baseline 3 is appropriate since the schema does the heavy lifting.
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: it's a wrapper for CodeRunnerAgent that executes code using async execution with warm pool. It specifies the action (executes code) and resource (CodeRunnerAgent wrapper), though it doesn't explicitly differentiate from sibling tools like the code generator or LLM processor, which might have overlapping functions.
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 mentions it's a wrapper for CodeRunnerAgent but doesn't explain when this wrapper is preferred over direct execution or other code-related tools in the sibling list, such as the code generator or LLM processor.
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 describes the tool's behavior: retrieving health information, including advanced features if available, falling back to basic information otherwise, and returning a dictionary. However, it lacks details on permissions, rate limits, error conditions, or what constitutes 'advanced' versus 'basic' information, leaving behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is moderately concise but could be more front-loaded. The first sentence states the purpose, but the second sentence partially repeats it ('Retrieves detailed health information'). The third sentence adds useful fallback behavior, and the fourth specifies the return format. Some redundancy exists, but overall it's reasonably 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 complexity (health monitoring with fallback behavior), no annotations, no output schema, and 0 parameters, the description is adequate but incomplete. It covers the core function and return type but lacks details on output structure, error handling, or integration with sibling tools. For a health-check tool, more context on metrics or thresholds would be beneficial.
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 tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, focusing on the tool's function. A baseline of 4 is applied since it avoids unnecessary parameter details for a zero-parameter tool.
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: 'Get comprehensive system health status including advanced monitoring features' and 'Retrieves detailed health information about the system'. It specifies the resource (system health) and scope (comprehensive/detailed). However, it doesn't explicitly differentiate from sibling tools like get_cache_status or get_performance_metrics, which appear related but distinct.
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. While it mentions 'advanced monitoring features' and 'basic information if advanced monitoring is not available', it doesn't specify use cases, prerequisites, or contrast with sibling tools like get_performance_metrics. This leaves the agent without clear selection criteria.
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 provided, the description carries full burden. It mentions the tool 'Collects and returns' metrics and has fallback behavior ('Provides basic information if advanced metrics collection is not available'), which adds some behavioral context. However, it lacks details on permissions, rate limits, data freshness, or whether this is a read-only operation. For a metrics tool with zero annotation coverage, this is insufficient disclosure.
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 appropriately sized with three sentences: purpose, scope/fallback, and return value. It's front-loaded with the core purpose. However, the third sentence 'Returns: A dictionary containing performance metrics and statistics' is somewhat redundant with the first two sentences and could be more integrated.
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 0 parameters, no annotations, and no output schema, the description provides adequate purpose and scope but lacks behavioral details needed for full transparency. It explains what metrics are collected and mentions fallback behavior, but doesn't cover response format details, error handling, or system impact. For a metrics tool with minimal structured data, it's moderately complete but has gaps.
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 tool has 0 parameters with 100% schema description coverage (empty schema). The description doesn't need to explain parameters, so it appropriately focuses on what the tool does rather than inputs. No parameter information is missing or needed, meeting the baseline for zero-parameter tools.
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 as 'Get performance metrics and analytics for the MCP Hub system' with specific metrics listed (execution times, success rates, error counts, resource utilization). It distinguishes from siblings like get_cache_status or get_health_status by focusing on performance analytics rather than cache/health status. However, it doesn't explicitly contrast with these siblings in the description text.
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 through 'Provides basic information if advanced metrics collection is not available,' suggesting fallback behavior. However, it doesn't explicitly state when to use this tool versus alternatives like get_health_status or get_cache_status, nor does it mention prerequisites or exclusions. The guidance is implied rather than explicit.
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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