mcp-pyright

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose targeting specific language server protocol operations: type checking, reference finding, code formatting, completions, definition lookup, symbol listing, and hover information. There is no overlap or ambiguity between these functions.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern with clear, descriptive names (check_types, find_references, format_code, get_completions, get_definition, get_document_symbols, get_hover). The naming convention is uniform throughout the set.
Tool Count5/5
With 7 tools, this server provides a well-scoped set covering essential Python language server operations. Each tool earns its place by addressing a distinct aspect of code analysis and editing assistance.
Completeness4/5
The toolset covers most core language server operations for Python code analysis, including diagnostics, navigation, formatting, and documentation. A minor gap exists in code modification operations like refactoring or rename, but the surface supports typical agent workflows effectively.
Average 3.1/5 across 7 of 7 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
- Last stable release on April 1, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'returns diagnostics' but doesn't specify what these diagnostics include (e.g., errors, warnings), how they're formatted, or any limitations (e.g., performance, supported Python versions). The mention of Pyright adds some context, but behavioral traits like rate limits, authentication needs, or side effects are not addressed.
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 that are front-loaded: the first sentence states the purpose, and the second adds implementation details (Pyright) and output. There's minimal waste, though it could be slightly more structured (e.g., separating purpose from behavior).
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 (type checking with multiple parameters), no annotations, and an output schema (which handles return values), the description is adequate but has gaps. It covers the basic purpose and tool used (Pyright), but lacks details on behavioral context, usage guidelines, and output specifics beyond 'diagnostics', making it minimally viable but incomplete for full 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?
Schema description coverage is 100%, so the schema already documents all four parameters (code, file_path, python_path, type_checker_mode) with descriptions. The description adds no additional meaning beyond what the schema provides, such as explaining parameter interactions or usage examples. Baseline 3 is appropriate when 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: 'Check types in a Python file or string of code' specifies the verb ('check types') and resource ('Python file or string of code'). It distinguishes from siblings like 'format_code' or 'get_completions' by focusing on type checking, but doesn't explicitly differentiate from all siblings (e.g., 'find_references' might also analyze code).
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 using Pyright for type checking, but doesn't indicate scenarios where this is preferred over other tools (e.g., 'get_completions' for code suggestions) or when not to use it. Usage is implied by the purpose but lacks explicit context or exclusions.
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 states the tool 'Returns available completions' but doesn't explain what 'completions' entail (e.g., suggestions, snippets), how they're generated, potential limitations (e.g., language support, accuracy), or response format. For a tool with no annotations, 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 concise with two sentences that directly state the purpose and return value. It's front-loaded with the core action and avoids unnecessary details. However, the second sentence slightly repeats the first ('Get code completions' vs. 'Returns available completions'), making it marginally less efficient than a perfectly streamlined version.
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 (code analysis), no annotations, and an output schema (which handles return values), the description is minimally adequate. It covers the basic purpose but lacks behavioral context (e.g., how completions work) and usage guidelines. With the output schema, completeness isn't severely lacking, but it doesn't fully compensate for the missing annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters ('code', 'position', 'python_path') with clear descriptions. The description adds no additional parameter semantics beyond implying position-based completion, which the schema's 'position' description ('Character position in the code') already covers. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 code completions at a specific position' specifies the verb ('Get'), resource ('code completions'), and scope ('at a specific position'). However, it doesn't explicitly differentiate from sibling tools like 'get_definition' or 'get_hover', which might also operate on code positions, so it lacks sibling differentiation for a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage 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 sibling tools like 'check_types' or 'format_code', nor does it specify prerequisites or exclusions. The agent must infer usage from the purpose alone, which is insufficient for effective tool selection.
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. It mentions the return includes 'document symbols including functions, classes, variables, etc.', which adds some behavioral context about output content. However, it lacks details on performance (e.g., handling large code), error conditions, or dependencies like the 'python_path' parameter's effect, leaving gaps 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 with two sentences: the first states the purpose, and the second clarifies the return content. It's front-loaded with the main action. There's no wasted text, though it could be slightly more structured by explicitly separating purpose and 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 (analyzing code for symbols), 100% schema coverage, and the presence of an output schema (implied by 'Has output schema: true'), the description is minimally adequate. It covers what the tool does and the return types, but lacks usage context, error handling, or examples, making it incomplete for optimal agent guidance.
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 fully documents parameters ('code' as Python code string, 'python_path' as optional interpreter path). The description adds no additional meaning beyond this, such as examples or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't need to heavily.
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 all symbols in a Python document' with specific examples like 'functions, classes, variables, etc.' This distinguishes it from siblings like 'get_definition' (specific symbol) or 'get_completions' (suggestions). However, it doesn't explicitly contrast with 'find_references' (which might find symbol usage), leaving some ambiguity.
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 scenarios like code analysis, navigation, or comparison with siblings such as 'get_definition' (for a single symbol) or 'find_references' (for symbol usage). This lack of context makes it harder for an agent to choose appropriately.
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. It mentions the tool returns a location, but doesn't disclose behavioral traits like what happens with invalid positions, whether it handles imports or external symbols, performance characteristics, or error conditions. The description is minimal and lacks operational context.
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 two sentences that directly state the action and result. It is front-loaded with the primary purpose and wastes no words, making it efficient for quick understanding.
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 an output schema (which handles return values), 100% schema coverage for inputs, and no annotations, the description is minimally adequate. However, for a code analysis tool with siblings, it lacks context on integration (e.g., how it relates to other tools) and behavioral details, leaving gaps in 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?
Schema description coverage is 100%, so the schema already documents all three parameters (code, position, python_path) with descriptions. The description adds no additional meaning beyond what the schema provides, such as examples or edge cases, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('definition location'), and identifies the target ('symbol at the given position'). It distinguishes from some siblings like 'format_code' or 'get_completions', but doesn't explicitly differentiate from similar tools like 'find_references' or 'get_hover'.
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 sibling tools like 'find_references' (which might find usage locations) or 'get_hover' (which might provide quick info), nor does it specify prerequisites or exclusions for usage.
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 states the tool returns type information and documentation, but doesn't disclose behavioral traits such as error handling, performance characteristics, or whether it requires specific permissions. The description is minimal and lacks depth for a tool with parameters.
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 two sentences that directly state the tool's purpose and return value. It is front-loaded and wastes no words, making it efficient and easy to parse.
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 an output schema (which covers return values) and full schema description coverage, the description is minimally adequate. However, for a tool with no annotations and sibling alternatives, it lacks context on usage and behavioral transparency, making it incomplete for optimal agent 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?
Schema description coverage is 100%, so the schema fully documents the three parameters (code, position, python_path). The description adds no additional meaning beyond what the schema provides, such as examples or usage context for the parameters. Baseline 3 is appropriate since the schema handles parameter documentation.
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 with a specific verb ('Get') and resource ('hover information'), and specifies it returns type information and documentation for symbols. However, it doesn't differentiate from sibling tools like 'get_definition' or 'get_completions' which might provide related information.
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 like 'get_definition' or 'get_completions'. It mentions the tool's function but offers no context about appropriate scenarios, prerequisites, or exclusions.
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 states the tool returns reference locations, which is useful, but doesn't cover other behavioral aspects such as performance considerations, error handling, or how it interacts with the optional 'python_path' parameter. For a tool with no annotations, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and front-loaded, consisting of two sentences that directly state the purpose and return value without any wasted words. Every sentence earns its place by adding essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, 2 required), 100% schema coverage, and the presence of an output schema (which handles return values), the description is reasonably complete. It covers the core functionality but could improve by adding more behavioral context, especially since no annotations are provided. The output schema reduces the 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?
The schema description coverage is 100%, so the schema already documents all parameters ('code', 'position', 'python_path') with descriptions. The description adds no additional parameter semantics beyond what the schema provides, such as format details or usage examples. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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: 'Find all references to a symbol at a specific position.' It specifies the verb ('find'), resource ('references'), and context ('symbol at a specific position'). However, it doesn't explicitly differentiate from sibling tools like 'get_definition' or 'get_hover', which might also involve symbol analysis, so it doesn't reach the highest score.
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 sibling tools like 'get_definition' or 'check_types', nor does it specify prerequisites or exclusions. The usage is implied by the purpose but lacks explicit context for selection.
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. It mentions the formatter (pyright) but doesn't disclose behavioral traits like whether it modifies code in-place, returns formatted output, handles errors, or has rate limits. The description lacks details on what the tool actually does beyond the basic action, leaving 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded: the first sentence 'Format Python code.' directly states the purpose, and the second adds context without waste. Every sentence earns its place by providing essential information efficiently.
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 moderate complexity (formatting code), 100% schema coverage, and the presence of an output schema (which means return values are documented elsewhere), the description is reasonably complete. It covers the what and how (using pyright) but lacks behavioral details. For a formatting tool with good schema support, this is adequate though not exhaustive.
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 ('code' and 'python_path') with descriptions. The description adds no additional meaning beyond what the schema provides, such as explaining parameter interactions or usage examples. Baseline 3 is appropriate as the schema handles 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: 'Format Python code' specifies the verb (format) and resource (Python code). It distinguishes from siblings like 'check_types' or 'get_completions' by focusing on formatting rather than analysis or suggestions. However, it doesn't explicitly contrast with potential formatting alternatives beyond mentioning pyright.
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 by stating 'Formats the given Python code using pyright's formatter,' suggesting this is for code formatting specifically with pyright. However, it doesn't provide explicit guidance on when to use this tool versus alternatives (e.g., other formatters or manual formatting), nor does it mention prerequisites or exclusions.
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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