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by anaseqal

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Server Quality Checklist

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Latest release: v0.2.5
Disambiguation5/5
Each tool has a clear and distinct purpose. run_python and run_python_stream are differentiated by streaming vs. non-streaming execution. run_with_retry adds retry logic. Learning tools (add_learning, record_semantic_failure) handle different failure types. configure, get_learnings, get_system_context, and pip_install are all unique and unambiguous.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case, e.g., add_learning, run_python, get_system_context. There are no deviations or mixed conventions.
Tool Count5/5
With 9 tools, the set is well-scoped for a code execution MCP server. It covers execution, learning, configuration, context retrieval, and package management without redundancy or gaps.
Completeness4/5
The tool surface covers core workflows: code execution (with variants), error/semantic learning, configuration, system context, and package installation. Minor gaps exist (e.g., no tool to delete or update learnings), but overall it's nearly complete for the stated purpose.
Average 4.2/5 across 9 of 9 tools scored. Lowest: 3.5/5.
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.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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}
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses it's a read-only operation ('View') and mentions optional filtering. However, it lacks detail on potential side effects or idempotency.
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 very concise, with three lines covering purpose, parameters, and returns. No fluff, main point first.
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 an output schema exists and the tool is simple, the description covers the core functionality. Missing details like pagination are minor for this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage; the description adds meaning by explaining the 'search' parameter as an optional filter, overcoming the schema gap.
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 it views recorded learnings, using the verb 'View' and specifying the resource. It implicitly differentiates from sibling 'add_learning'.
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 like 'add_learning'. The description only states what it does, not when it's appropriate.
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 'Installation result' but lacks details on side effects (e.g., environment changes, permissions, failure modes). Minimal behavioral disclosure.
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 (3 sentences) with a clear structure: purpose, usage guidance, and parameter/return sections. No extraneous content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple 1-parameter tool with an output schema, the description covers core purpose and usage. However, it omits error handling, return format, and potential prerequisites, leaving gaps for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description adds basic meaning to 'package_name' as 'The pip package name to install', but does not elaborate on format or version specifics. Adequate but not rich.
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 action ('Pre-install a Python package') and the resource (Python package). It distinguishes from sibling tools like 'run_python' by specifying installation.
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?
Provides explicit guidance on when to use ('if you know you'll need them, or if auto-install missed something'), but does not mention when not to use or name alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits. It states that code execution occurs, returns stdout/stderr/status/images, and mentions timeout and auto-install. However, it does not disclose potential security implications, execution environment restrictions, or that any code run could be dangerous. This omission is notable for a tool that runs arbitrary code.
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 well-structured with a summary, bullet list of capabilities, clear Args section, Returns line, and an example. It is front-loaded with the primary purpose. While somewhat lengthy due to the example and extensive list, every part adds value. It could be slightly more concise but remains effective.
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 4 parameters, no output schema, and no annotations, the description covers purpose, parameters, return format, and provides an example. However, it lacks usage guidelines, security warnings, and context about when to prefer siblings. It is adequate but not fully complete for a tool of this complexity.
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 input schema has 0% description coverage, but the tool description explains each parameter in the Args section: 'code: Python code to execute. Use print() for output.', 'description: Brief task description (for logging)', 'timeout: Max execution time in seconds', 'auto_install: Auto-install missing packages'. This adds meaning beyond the schema's titles and types.
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: 'Execute Python code to accomplish ANY task.' It lists numerous capabilities (HTTP requests, parsing, data processing, etc.), making the scope unmistakable. The tool name 'run_python' is self-explanatory, and the description reinforces its universality.
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 positions the tool as 'universal' and lists many use cases, but it does not provide guidance on when not to use it or suggest alternative tools. Siblings like 'pip_install' and 'run_python_stream' exist, but no distinctions are made. The description lacks explicit context for appropriate vs. inappropriate usage.
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 must cover behavior. It notes that learning will be suggested for similar errors, but does not disclose details like storage persistence, duplicate handling, or side effects.
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?
Well-structured with clear sections, example, and return info. Could be slightly more concise but remains readable and informative.
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?
Fairly complete for a 4-parameter tool with no output schema; covers what each parameter does and typical usage. Could mention behavior for duplicate patterns or storage limits.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description compensates by explaining each parameter (error_pattern, solution, context, tags) with brief descriptions and a full example, adding meaning beyond parameter names.
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 'Record a learning from a code execution for future reference' and provides a concrete example. It distinguishes from sibling tools like get_learnings (retrieval) effectively.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'When you figure out how to fix an error, record it here' and mentions future suggestion. Does not explicitly list when not to use, but sibling tools cover alternative actions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully describes the streaming behavior, the role of print statements, and the output format (streaming, summary, images). It provides a concrete example demonstrating real-time output. No critical behavioral traits are missing.
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 well-structured with headers, bullet points, and an example. It is somewhat verbose but every part adds value. The purpose is front-loaded, and the organization aids readability.
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 no output schema, the description explains the return format clearly. It covers usage patterns, parameter semantics, and provides an example. For a 4-parameter tool with no annotations, it is fairly complete, though a brief note on error behavior would enhance it.
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 0%, so the description must compensate. It explains that 'code' should use print(), 'description' is for logging, 'timeout' is max execution time, and 'auto_install' handles missing packages. However, it does not provide detailed parameter constraints or formats beyond schema types, so value added is moderate.
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 executes Python code with real-time streaming output, distinguishing it from siblings like 'run_python' and 'pip_install'. It uses specific verbs and resources, making the purpose unambiguous.
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 multiple use cases (e.g., web scraping, data processing loops) and explains when streaming is beneficial. However, it does not explicitly state when not to use the tool (e.g., for short tasks or non-streaming needs), but the context is clear enough for an AI agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description details actions and available settings, disclosing behavioral traits such as the effect of 'set' action. It does not mention persistence or side effects, but this is acceptable for a configuration tool. No annotation contradiction.
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 well-organized into Args, Available settings, Examples, and Returns. It is concise yet complete, with no wasted sentences.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given three simple parameters, zero schema coverage, no annotations, and an output schema, the description thoroughly covers purpose, parameters, settings, and examples. It adds necessary context for correct tool invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining each parameter's meaning—action as 'view' or 'set', key as config key, value as new value—and listing all configurable settings. This goes well beyond the raw schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'View or update Code Mode configuration', providing a specific verb and resource. This distinguishes it from sibling tools like run_python or pip_install, which focus on execution or installation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly indicates when to use 'view' vs 'set' actions. It does not explicitly state when not to use or mention alternatives, but the context of sibling tools makes the usage clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It explains that the tool records semantic failures and returns a confirmation message, which is adequate for a simple recording tool. No contradictions.
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, with a clear purpose statement, usage conditions, labeled parameters, and a concrete example. No extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers purpose, usage guidelines, all parameters with examples, and return value. Given the tool's low complexity and presence of an output schema, it is fully complete 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.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has no descriptions (0% coverage), but the tool description provides detailed explanations for all five parameters, including examples and defaults, fully compensating for the schema gap.
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: recording semantic failures when code runs without errors but fails to achieve the goal. It includes examples and distinguishes from error-based learning, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly lists three scenarios for using the tool, providing clear context. It does not explicitly mention when not to use it or compare to siblings, but the guidance is sufficient for differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It details what information is returned (OS, Python version, paths, libraries, execution mode, past learnings, tips), making behavior fully transparent.
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?
Description is concise: one sentence followed by a bulleted list of what it provides. Every sentence adds value, and structure is front-loaded with the main purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and an output schema, the description fully covers the tool's context. It explains what the tool returns and its recommended usage, leaving no 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?
Input schema has 0 parameters, so schema coverage is 100%. According to guidelines, 0 params warrants a baseline of 4. Description adds no parameter info, which is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get comprehensive system context before writing code.' It uses specific verb+resource and distinguishes itself from siblings like run_python and pip_install.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'CALL THIS FIRST,' indicating when to use it. While no alternatives or exclusions are given, the context makes it clear that this is the initial step. Siblings are distinct tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but description fully details the retry mechanism: analyzing errors, searching learnings, providing diagnostics and suggestions. Discloses retry count, timeout, and post-failure analysis. No contradictions.
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?
Well-structured with summary, bullet points, usage note, parameter list, and return description. Minor redundancy ('error and semantic learnings' appears twice) but overall concise for the amount of information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers all aspects: purpose, behavior on failure, parameter explanations, usage guidance, return value. Output schema exists and description appropriately mentions return type. References sibling tools for additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage, but description adds meaningful explanations for all four parameters: code, description (helps find semantic learnings), max_retries (max retry attempts), timeout (execution timeout). Adds value beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Execute Python code with intelligent retry and error analysis.' It specifies the verb and resource, and distinguishes from siblings like run_python (no retry) and run_python_stream (streaming).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Use this for more robust execution when errors are expected or when learning from previous similar tasks.' Also advises using record_semantic_failure for non-error failures, providing clear usage context. Could be slightly more explicit about when not to use.
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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