Professional Python MCP Server

by bash20cu

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: list_models_with_limits handles model information, notify_dev manages notifications, read_code and write_code handle file I/O (one for reading, one for writing), and run_terminal executes shell commands. The descriptions reinforce these distinct roles, making misselection unlikely.
Naming Consistency4/5
The tool names follow a consistent verb_noun pattern (e.g., list_models_with_limits, read_code, write_code, run_terminal), which is predictable and readable. The only minor deviation is notify_dev, which uses 'notify' as the verb instead of a more standard action like 'send_notification', but it still fits the overall pattern well.
Tool Count4/5
With 5 tools, the count is reasonable for a server focused on Python development utilities, covering model listing, notifications, file operations, and terminal execution. It feels slightly thin but well-scoped, as each tool serves a clear purpose without unnecessary bloat.
Completeness3/5
The server covers basic development tasks like file I/O, command execution, and notifications, but there are notable gaps for a 'Professional Python' scope, such as missing tools for code analysis (e.g., linting, testing), dependency management, or environment handling. The surface is functional but incomplete for advanced workflows.
Average 3.3/5 across 5 of 5 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
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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 states the action ('executes a shell command') and output ('returns stdout and stderr'), but lacks critical details such as execution environment, permissions required, potential side effects (e.g., file modifications), error handling, or security implications. This is a significant gap for a tool that interacts with the shell.
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 and front-loaded, with the core purpose stated first and an example provided for clarity. Both sentences earn their place by explaining the tool's function and illustrating usage, though it could be slightly more structured by separating guidelines from examples.
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 (executing shell commands) and the presence of an output schema (which likely covers return values), the description is moderately complete. It covers the basic action and output but lacks details on behavioral aspects like safety, environment, or error handling. With no annotations and low schema coverage, it should do more to address these gaps for a tool of this nature.
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 does not add meaning beyond what the input schema provides. The schema has 1 parameter ('command') with 0% description coverage, and the description only implies its use through the example ('npm run dev, pytest, etc.'), without explaining syntax, constraints, or valid values. Since schema_description_coverage is low (<50%), the description fails to compensate adequately, resulting in a baseline score.
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: 'Executes a shell command and returns stdout and stderr.' It specifies the verb ('executes'), resource ('shell command'), and outcome ('returns stdout and stderr'). However, it doesn't explicitly differentiate from sibling tools like 'read_code' or 'write_code', which might involve similar operations but with different scopes or purposes.
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 includes an example ('Example: npm run dev, pytest, etc.'), but this only illustrates usage rather than specifying contexts, prerequisites, or exclusions. There is no mention of when to choose this over sibling tools like 'read_code' or 'write_code' for related tasks.
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 platform limitation ('Supports Linux') which is useful context, but doesn't describe what happens when invoked (e.g., does it block execution, what happens on non-Linux systems, error behavior, or visual characteristics of the notification). 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with just two sentences that each add value. The first sentence states the core purpose, the second adds important platform context. There's zero wasted language or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 parameter, has output schema), the description covers the basic purpose and platform constraint. However, with no annotations and 0% schema coverage, it should provide more about parameter usage and behavioral expectations. The existence of an output schema means it doesn't need to describe return values.
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 has 0% description coverage, so the description must compensate. It doesn't mention the 'message' parameter at all, providing no semantic information about what the message should contain, format expectations, or length limits. However, with only one simple string parameter, the baseline is higher than for complex 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 action ('Sends a desktop notification') and specifies the target resource ('desktop notification'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'run_terminal' or 'write_code' which are completely different operations, so it doesn't need sibling differentiation here.
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 minimal usage context by mentioning platform support ('Supports Linux (notify-send)'), but offers no guidance on when to use this tool versus alternatives or any prerequisites. There's no mention of when-not-to-use scenarios or comparison with other notification methods.
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 supported file extensions, which adds useful context beyond the schema, but fails to address critical behaviors like error handling (e.g., what happens if the file doesn't exist or has an unsupported extension), permissions required, or rate limits. The description doesn't contradict annotations (none exist).
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—two sentences that directly state the tool's function and constraints without any fluff. It is front-loaded with the core purpose, and every sentence adds value (the extension list is necessary context).
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 (one parameter) and the presence of an output schema (which handles return values), the description is minimally adequate. However, it lacks details on behavioral aspects like error handling or permissions, which are important for a file-read operation. With no annotations, it should do more to be fully complete.
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 0%, so the description must compensate. It adds meaning by specifying supported file extensions, which clarifies the 'path' parameter's expected content. However, it doesn't explain the path format (e.g., absolute vs. relative), constraints, or examples. With one parameter and partial compensation, a baseline 3 is appropriate.
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 ('Reads') and resource ('content of a file'), making the purpose immediately understandable. It distinguishes from siblings like 'write_code' by specifying it's a read operation, though it doesn't explicitly contrast with other read-like tools (none exist in the sibling list).
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 supported file extensions but doesn't explain when to choose this over other tools (e.g., 'run_terminal' for executing code or 'list_models_with_limits' for other operations). No exclusions or prerequisites are stated.
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 but only states the basic action. It doesn't disclose critical behavioral traits like permissions required, whether it creates directories, error handling, or side effects, which are essential for a write operation.
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 front-loaded with the core action and resource, making it easy to parse quickly without unnecessary elaboration.
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 a write operation with no annotations, 2 parameters at 0% schema coverage, and an output schema present, the description is minimally adequate. It states the action but lacks details on behavior, parameters, and context, though the output schema reduces need for return value explanation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the schema provides no parameter details. The description adds minimal semantics by implying 'path' and 'content' parameters but doesn't explain format, constraints, or examples. This partially compensates but leaves significant gaps.
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 action ('writes or overwrites') and target resource ('content to a file'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'read_code' or 'run_terminal' beyond the obvious verb difference, missing explicit comparison.
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 prerequisites, when it's appropriate versus other file operations, or any context for choosing it over similar tools, leaving usage decisions ambiguous.
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. It discloses the tool's behavior as a read-only listing operation with sorting by token limit and explains the rationale (proxy for capacity). However, it lacks details on potential limitations like rate limits, error conditions, or data freshness, which would be valuable for a tool with no annotation coverage.
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: the first states the core action and sorting, the second explains the utility. It is front-loaded with the main purpose, making it easy to scan and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no annotations, but with an output schema), the description is mostly complete. It explains what the tool does and why, but could benefit from mentioning the output format or any behavioral constraints. The presence of an output schema reduces the need to detail return values, keeping it adequate.
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 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately adds no parameter details, focusing instead on the tool's purpose and output behavior. A baseline of 4 is applied as it efficiently handles the zero-parameter case without redundancy.
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 specific action ('Lists available Gemini models') and resource ('Gemini models'), with explicit sorting criteria ('sorted by input token limit'). It distinguishes from siblings by focusing on model listing with capacity metrics, unlike notification, code reading/writing, or terminal execution tools.
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 ('Provides a proxy for capacity since exact quota is not available via API'), indicating this tool is for assessing model capabilities when quota data is inaccessible. However, it does not explicitly state when not to use it or name specific alternatives among siblings.
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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