ast-editor
Server Quality Checklist
- Disambiguation5/5
Each tool has a clearly distinct purpose with explicit 'Use this when' and 'Don't use this when' guidance that eliminates ambiguity. For example, add_comment_before vs replace_leading_comment vs replace_docstring are carefully distinguished, and tools for different data structures (dict vs array) are separated. The descriptions create clear boundaries between overlapping operations.
Naming Consistency5/5All tools follow a consistent verb_noun or verb_preposition_noun pattern throughout (add_comment_before, add_field, add_import, replace_function_body, etc.). The naming convention is perfectly uniform with snake_case used consistently across all 32 tools, making the tool set highly predictable and readable.
Tool Count3/5With 32 tools, the count feels heavy for an AST editor, though each tool appears specialized. While many tools earn their place through distinct operations, the high number could overwhelm agents and suggests potential over-specialization. A typical well-scoped server might have 15-25 tools; 32 is borderline excessive but not extreme.
Completeness5/5The tool set provides comprehensive coverage for AST editing across multiple languages and file types. It includes full CRUD operations for symbols, imports, comments, parameters, and configuration data, plus read-only analysis tools. There are no obvious gaps—every editing need appears addressed with appropriate granularity, and the domain is fully covered without dead ends.
Average 4.5/5 across 32 of 32 tools scored.
See the tool scores section below for per-tool breakdowns.
This repository includes a README.md file.
This repository includes a LICENSE file.
Latest release: v1.2.0
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- This server provides 32 tools. View schema
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Tool Scores
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It explains the tool inserts comments before symbols and includes an example, but doesn't disclose important behavioral traits like whether this modifies files in-place, what happens if the target doesn't exist, error conditions, or what the output contains. The example helps but leaves gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Perfectly structured with purpose statement, usage guidelines, and example. Every sentence earns its place. The information is front-loaded with clear purpose, followed by specific guidance, then illustrative example.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
While there's an output schema (which helps), this is a file-modifying tool with 3 parameters and 0% schema description coverage. The description explains the core operation and provides good usage guidance, but lacks important context about how the tool behaves (error handling, file modification approach) and parameter meanings. The example helps but doesn't fully compensate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It mentions 'target' and 'comment' parameters in the example but doesn't explain what 'file_path', 'target', or 'comment' represent semantically. The example shows target='LRUCache.get' but doesn't explain target syntax. No information about file_path parameter at all.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verb ('Insert comment line(s)') and resource ('before a named symbol'). It distinguishes from siblings by specifying what it does differently from related tools like replace_docstring and replace_leading_comment.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly provides 'Use this when' and 'Don't use this when' sections with clear alternatives named (replace_docstring, replace_leading_comment). This gives perfect guidance on when to choose this tool versus other options.
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 full burden. It describes the tool's behavior by specifying what is replaced (body) and what is preserved (signature, decorators), but lacks details on error handling, permissions, or side effects. The example adds practical context but doesn't fully compensate for the missing 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by usage guidelines and a concrete example. Every sentence adds value without redundancy, and the structure is logical and efficient for an AI agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (function modification), lack of annotations, and 0% schema coverage, the description does well with purpose, guidelines, and an example. However, it could better address parameter details and behavioral aspects. The presence of an output schema reduces the need to explain return values, but overall completeness is good with minor gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It provides an example that illustrates the use of `target` and `content` parameters, adding meaning beyond the bare schema. However, it doesn't explain `file_path` or provide detailed semantics for all three parameters, leaving some gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Replace only the body of a function') and resource ('a function'), distinguishing it from sibling tools like `replace_signature` or `replace_function`. It explicitly defines what is preserved (signature and decorators) versus what is changed (implementation).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use ('changing the implementation while keeping the interface stable') and when not to use ('changing parameters or return type'), with clear alternatives named (`replace_signature` or `replace_function`). This directly addresses sibling tool differentiation.
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 full burden. It states the tool modifies files (implied mutation) and provides an example, but doesn't disclose important behavioral traits like error handling, file permissions needed, whether changes are reversible, or format-specific constraints. The example adds some context but leaves gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Perfectly structured with purpose statement, usage guidelines, and example in three compact sections. Every sentence earns its place with zero wasted words. The information is front-loaded with the core purpose immediately clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given 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), no annotations, and 3 parameters with 0% schema coverage, the description does well with purpose and guidelines. However, it lacks complete parameter documentation and behavioral details needed for full understanding of a file mutation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does 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 documentation. The description includes an example showing 'target' and 'content' usage, which adds some semantic meaning beyond the bare schema. However, it doesn't explain 'file_path' or provide complete parameter documentation, leaving significant gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('replace the value') and resource ('existing key in a JSON, YAML, or TOML file'), distinguishing it from siblings like 'add_key' and array modification tools. It provides a concrete verb+resource combination with clear scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly provides 'Use this when' and 'Don't use this when' sections, naming specific alternative tools ('add_key', 'append_to_array', 'remove_from_array'). This gives clear contextual boundaries and direct guidance on when to choose this tool over siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior ('If removing the only remaining name, removes the entire import statement') and constraints ('Python-only'), which are not covered by the input schema. However, it doesn't mention potential side effects like file modification or error handling, leaving some behavioral aspects unclear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with the core purpose, followed by usage guidelines and an example. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (a mutation operation with 3 parameters), no annotations, and an output schema (which reduces the need to describe return values), the description is mostly complete. It covers purpose, usage, and key behavior, but lacks details on parameter 'file_path' and potential error cases, leaving minor gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description must compensate. It provides an example that clarifies the semantics of 'module' and 'name' parameters, but doesn't explain 'file_path' at all. With 3 parameters and only partial coverage in the description, it doesn't fully compensate for the schema gap, leaving one parameter undocumented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Remove a name from a Python `from <module> import a, b, c` statement') and resource (import statement), with explicit sibling differentiation ('If removing the only remaining name, removes the entire import statement' and distinguishing from 'remove_import'). It precisely defines the tool's scope and distinguishes it from alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidelines with 'Use this when: You want to remove a single name from a multi-name import' and 'Don't use this when: You want to remove the entire import line -> use `remove_import`', including clear alternatives and exclusions. This directly addresses when to use this tool versus its sibling.
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 describes the action (adding a field) and placement (top of class body, fields-before-methods convention), but lacks details on permissions, error handling, or what happens if the class doesn't exist. It adds some behavioral context but not comprehensively.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Front-loaded with the core purpose, followed by usage guidelines and a concrete example. Every sentence serves a distinct purpose with zero waste, 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.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema (which handles return values), the description focuses on purpose and usage effectively. It covers key aspects like sibling differentiation and parameter illustration, but as a mutation tool with no annotations, it could benefit from more behavioral transparency (e.g., error cases).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must compensate. The example illustrates usage of 'class_target' and 'content' parameters, clarifying their roles. However, 'file_path' is not explained in the description, leaving one parameter undocumented. The example adds significant value but doesn't fully cover all parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Add a field/attribute/member') and the location ('at the top of a class body'), distinguishing it from sibling tools like 'add_method' by specifying it's for fields, not methods. The example reinforces this distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly provides when to use ('when: You're adding a class attribute...') and when not to use ('Don't use this when: You're adding a method -> use `add_method`'), naming the alternative tool. This directly addresses sibling tool differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it's Python-specific, skips duplicates, and operates on existing import statements. However, it doesn't mention potential side effects like file modification confirmation or error handling for invalid inputs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Perfectly front-loaded with the core purpose first, followed by usage guidelines and a concrete example. Every sentence earns its place with zero wasted words. The structure moves naturally from what it does to when to use it to how it works.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations, 0% schema coverage, but with an output schema present, the description does well by covering purpose, usage, and key behaviors. The output schema likely handles return values, so the description appropriately focuses on when and how to use the tool rather than return details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It provides an example showing 'module' and 'name' parameters in action, but doesn't explain 'file_path' at all. The description adds some meaning for 2 of 3 parameters, but leaves one completely undocumented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Add a name'), the resource ('existing Python `from <module> import a, b` statement'), and scope ('Python-only. Skips duplicates'). It explicitly distinguishes from sibling 'add_import' by specifying this is for adding to an existing import line, not creating a new one.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit 'Use this when' and 'Don't use this when' guidance with clear alternatives named ('add_import'). It gives concrete context about when to choose this tool versus its sibling, including a specific example scenario.
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 full burden. It describes the action (adding a method to a class) and placement ('at the end'), but lacks details about permissions, error handling, or what happens if the class doesn't exist. The example helps but doesn't fully compensate for missing behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with purpose statement, usage guidelines, and example in just a few sentences. Every sentence adds value without redundancy. The information is front-loaded with the core purpose immediately stated.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 3 parameters with 0% schema coverage and no annotations, the description does well with usage guidelines and example, but lacks full parameter documentation and behavioral details. The presence of an output schema helps, but more context about the mutation's effects would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must compensate. The example illustrates usage of class_target and content parameters, providing semantic meaning beyond the schema's titles. However, file_path parameter remains unexplained in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Add a new method') and resource ('at the end of a class body'), distinguishing it from siblings like add_field and add_top_level. It precisely defines what the tool does without being vague or tautological.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly provides 'Use this when' and 'Don't use this when' guidance with named alternatives (add_field, add_top_level). This gives clear context for when to select this tool versus other available options.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It explains what the tool does (appends to arrays in various formats) and provides examples, but it lacks details on potential side effects (e.g., file modification behavior, error handling, or permissions required). The description adds value by clarifying format-specific targeting but doesn't fully cover behavioral traits like mutation implications or response format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded, starting with a clear purpose statement. Each sentence adds value: format support, targeting rules, usage guidelines, and examples. There is no redundant or wasted text, and the information is organized efficiently for quick comprehension by an AI agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (mutation tool with 3 parameters, 0% schema coverage, no annotations, but with an output schema), the description is mostly complete. It covers purpose, usage, parameter semantics with examples, and distinguishes from siblings. The output schema likely handles return values, so the description doesn't need to explain those. However, it lacks details on behavioral aspects like error conditions or file system effects, which could be important for a mutation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for undocumented parameters. It adds significant meaning beyond the schema by explaining the semantics of 'target' (dotted path for JSON/YAML/TOML, variable name for Python) and 'value' (literal value to append), with examples for TOML and Python. However, it doesn't explicitly cover 'file_path', leaving one parameter partially unexplained, though context implies it's the file to modify.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Append a literal value to an array/list.' It specifies the exact action (append), the target (array/list), and distinguishes it from sibling tools by explicitly contrasting with 'add_key' for key-value pairs. The description also enumerates supported formats (JSON, YAML, TOML, Python), making the scope specific and well-defined.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use and when not to use this tool: 'Use this when: You want to add an item to a list (dependencies, keywords, include paths, fixtures, etc.). Don't use this when: You're adding a key-value pair -> use `add_key`.' It names a specific alternative tool (`add_key`) and gives context for appropriate use cases, which is comprehensive for guiding 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?
No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: it preserves existing statements (non-destructive to existing code), inserts at the bottom (specific location), and is for adding one or two lines (scope limitation). However, it doesn't mention potential side effects like syntax validation or error handling, which could be relevant for a code modification tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by usage guidelines and a concrete example. Every sentence earns its place: the first defines the action, the second and third provide explicit when/when-not rules, and the example illustrates usage. No wasted words, and the structure is logical and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (code insertion with specific constraints), no annotations, and an output schema (which handles return values), the description is mostly complete. It covers purpose, usage, and behavioral context well. However, it lacks details on parameter formats and potential error conditions, which could be important for reliable tool invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does 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 some semantics: it implies 'target' is a function identifier (e.g., 'LRUCache.get') and 'content' is code to insert, as shown in the example. However, it doesn't explain 'file_path' or provide format details for any parameters, leaving gaps in documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Insert content at the bottom of a function body') and resource ('function body'), distinguishing it from siblings like 'prepend_to_body' (top insertion) and 'replace_function_body' (full replacement). The verb 'insert' with the qualifier 'at the bottom' precisely defines the operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit guidance is provided with 'Use this when:' for adding lines at the END (e.g., cleanup, telemetry) and 'Don't use this when:' with named alternatives for replacing the whole body ('replace_function_body') or adding to the top ('prepend_to_body'). This clearly defines when to use this tool versus siblings.
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 full burden. It clearly indicates this is a destructive operation ('remove entirely'), but doesn't mention permissions needed, whether deletions are reversible, or what happens to dependent code. The examples help clarify scope but behavioral details are incomplete.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with clear sections: purpose statement, usage guidelines with bullet points, and concrete examples. Every sentence adds value with no wasted words. The information is front-loaded with the core purpose first.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a destructive operation with no annotations, the description does well by providing usage guidelines, examples, and sibling tool distinctions. Since an output schema exists, it doesn't need to explain return values. The main gap is lack of behavioral warnings about irreversible changes.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must compensate. It provides examples showing how 'target' parameter works (e.g., 'LRUCache.old_method', 'DeprecatedClass'), which adds valuable semantic context beyond the bare schema. However, it doesn't explain the 'file_path' parameter at all.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Delete an entire function or class definition, including its decorators') and the resource ('function, method, or class'), distinguishing it from siblings like delete_key and remove_import. It provides concrete examples of what gets deleted (methods, whole classes).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly provides 'Use this when' and 'Don't use this when' sections with clear alternatives named (delete_key, remove_import, replace_function_body). This gives precise guidance on when to choose this tool versus other available options.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: it's 'Read-only' (indicating non-destructive behavior), specifies language support, and clarifies the scope (signature only, not the body). However, it lacks details on error handling, rate limits, or authentication needs, which would be useful for a tool interacting with files.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded, with the core purpose stated first, followed by behavioral notes, usage guidelines, and an example. Every sentence adds value without redundancy, and the bullet-like formatting for guidelines enhances readability, 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.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no annotations, but with an output schema), the description is largely complete. It covers purpose, behavior, and usage well. However, it lacks details on parameter semantics and error cases, which are partially mitigated by the output schema handling return values but still leave gaps for effective tool invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does 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: it implies 'target' identifies a function (e.g., 'LRUCache.get') but doesn't explain 'file_path' or the exact format of 'target'. While it offers an example, this only partially compensates for the low coverage, leaving some ambiguity in parameter usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Return the signature') and resources ('function'), distinguishing it from siblings like 'read_symbol' or 'replace_signature' by focusing solely on signature extraction. It explicitly mentions the output format ('plain text') and supported languages ('Python, JS, TS, C, and C++'), making it highly specific and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance with 'Use this when' and 'Don't use this when' sections, naming specific scenarios (documentation, refactoring, interface verification) and alternatives ('read the file directly'). This clearly defines when to use this tool versus other options, including sibling tools that handle full function bodies or modifications.
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 describes the tool's behavior as inserting content after a symbol, which implies a mutation (likely write operation), but doesn't disclose permissions, error handling, or side effects. The example clarifies the target and content parameters, adding some context, but lacks details on file modification safety or response format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with the core purpose, followed by usage guidelines and an example. Every sentence adds value without redundancy, making it efficient and easy to parse for an agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (mutation with 3 parameters), no annotations, and an output schema (which handles return values), the description is fairly complete. It covers purpose, usage, and parameter semantics but could improve by addressing behavioral aspects like permissions or error cases, though the output schema mitigates some gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains the 'target' parameter as a named symbol (e.g., 'LRUCache') and 'content' as the text to insert (e.g., 'RELATED_CONSTANT = 42'), adding meaning beyond the bare schema. However, it doesn't cover 'file_path' or provide format details, leaving some gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Insert content as a sibling') and the target ('immediately after a named symbol'), specifying the types of symbols (function, class, method, or top-level assignment). It distinguishes from sibling tools like 'add_top_level' by emphasizing precise placement after a specific symbol rather than appending to the file end.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly provides usage guidelines with 'Use this when:' and 'Don't use this when:' sections, including a named alternative ('add_top_level'). It gives concrete examples of when to use (e.g., placing a helper after a function) and when not to use (appending to file end), helping the agent choose correctly among siblings.
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 full burden. It describes the core behavior (inserting before a named symbol) and gives an example, but lacks details on error handling (e.g., if target doesn't exist), file modification effects, or permissions needed. It adds some context but not comprehensive 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by usage guidelines and an example. Every sentence adds value: the first defines the tool, the next two provide when/when-not guidance, and the example illustrates parameters. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 3 parameters with 0% schema coverage and no annotations, the description does well by explaining purpose, usage, and providing an example. An output schema exists, so return values needn't be described. However, it could better address error cases or file system implications for a mutation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains 'target' as a named symbol (function, class, method, or top-level assignment) and 'content' as what to insert, with an example showing usage. However, it doesn't clarify 'file_path' semantics or parameter constraints beyond the example, leaving some gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Insert content as a sibling immediately before a named symbol') and specifies the resource types (function, class, method, or top-level assignment). It distinguishes from sibling 'add_top_level' by emphasizing precise placement relative to another symbol rather than appending to the end of the file.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly provides 'Use this when' guidance with concrete examples (e.g., inserting a helper function just before its caller) and 'Don't use this when' with a named alternative ('add_top_level'). This gives clear context for when to choose this tool over others.
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 carries full burden. It discloses key behavioral traits: it preserves existing statements (non-destructive), operates on function bodies, and handles specific use cases. However, it doesn't mention error handling, permissions, or rate limits, which could be relevant for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Front-loaded with the core purpose, followed by usage guidelines and a concrete example. Every sentence earns its place with no wasted words, and the structure is logical and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a mutation tool with no annotations, 3 parameters, 0% schema coverage, but an output schema exists, the description is mostly complete. It covers purpose, usage, and provides an example, but could better explain parameters or error cases. The output schema reduces the need to describe return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It provides an example that clarifies 'target' (function name like 'LRUCache.get') and 'content' (code string to insert), adding meaning beyond the bare schema. However, it doesn't explain 'file_path' or provide full parameter details, leaving some gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Insert content at the top of a function body') and resource ('function body'), and distinguishes it from sibling tools like 'append_to_body' and 'replace_function_body'. The verb 'prepend' is precise and matches the tool name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly provides 'Use this when' scenarios (adding lines at the start for logging, validation, guards, debug prints) and 'Don't use this when' alternatives (replacing whole body or adding to bottom), naming specific sibling tools ('replace_function_body', 'append_to_body') for comparison.
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 states the tool modifies code ('Remove a parameter'), implying mutation, but doesn't mention permissions, error handling, or what happens if the parameter doesn't exist. The example helps, but lacks details on side effects or response format, leaving gaps in behavioral understanding.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with the core purpose, followed by usage guidelines and an example. Each sentence adds value without redundancy, making it efficient and easy to parse, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given 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), no annotations, and moderate complexity with 3 parameters, the description is mostly complete. It covers purpose, usage, and provides an example, but lacks details on behavioral aspects like error conditions or side effects, which are important for a mutation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains the 'parameter_name' parameter in the example, but doesn't clarify 'file_path' or 'target' beyond their titles. The example adds some context for 'target', but overall, the description provides partial semantic value without fully documenting all three parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Remove a parameter by name') and the resource ('from a function signature'), while explicitly distinguishing it from sibling tools like 'replace_signature'. It provides a precise scope ('Leaves the body untouched'), making the purpose unambiguous and well-differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly provides usage guidance with 'Use this when:' and 'Don't use this when:' sections, naming a specific alternative tool ('replace_signature') and clarifying the context for each. This gives clear direction on when to choose this tool over siblings, enhancing decision-making.
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 full burden. It describes the core behavior (replacing/inserting docstrings) and mentions the Python-only constraint, but doesn't disclose potential side effects, error conditions, or what happens if the target doesn't exist. It provides basic behavioral context but lacks depth for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with purpose statement, usage guidelines, and example in just four sentences. Every sentence adds value: the first states what it does, the second clarifies parameter format, the third/fourth provide clear usage boundaries. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given this is a mutation tool with no annotations, 3 parameters, and 0% schema coverage, the description does well with purpose, guidelines, and some parameter clarification. The existence of an output schema means return values don't need explanation. However, as a mutation tool, it could better address potential risks or error cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must compensate. It explains 'target' with an example ('LRUCache.get') and clarifies that 'new_docstring' should be 'a valid Python string literal including its surrounding triple quotes' with an example. However, it doesn't mention 'file_path' at all, leaving one parameter undocumented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Replace or insert'), resource ('Python docstring on a function or class'), and scope ('Python-only'). It distinguishes from sibling tools like 'replace_leading_comment' by specifying it's for docstrings, not comments.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly provides 'Use this when' and 'Don't use this when' guidance, naming the alternative tool ('replace_leading_comment') and specifying language constraints ('non-Python file -> no equivalent tool'). This gives clear context for when to choose this tool over others.
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 full burden. It clearly indicates this is a mutation operation ('replace'), but doesn't disclose important behavioral aspects like error handling, what happens if the target doesn't exist, whether changes are reversible, or file system implications. The example helps but doesn't cover behavioral traits comprehensively.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured: first sentence states the purpose, next section provides usage guidelines with clear when/when-not rules, and ends with a concrete example. Every sentence adds value with zero redundancy or filler content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given this is a mutation tool with no annotations but with an output schema (which handles return values), the description does well on purpose and guidelines. However, for a tool that modifies source code files, it lacks information about permissions needed, validation behavior, or error conditions. The example helps but doesn't fully compensate for missing behavioral context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must compensate. It provides an example that illustrates all three parameters in use, showing that 'target' identifies the function and 'content' contains the new definition. However, it doesn't explicitly explain each parameter's role or format beyond the example, leaving some interpretation required.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('replace an entire function definition') and specifies what gets replaced ('signature, body, and decorators'). It distinguishes this tool from its siblings by explicitly contrasting with 'replace_function_body' and 'replace_signature', 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 Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool ('rewriting a function top-to-bottom') and when not to use it, with clear alternatives named ('replace_function_body' for body-only changes, 'replace_signature' for signature-only changes). This gives the agent precise decision criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the tool's core behavior (preserving body and decorators while replacing signature) and includes an example, which adds useful context. However, it lacks details on error handling, permissions, or side effects (e.g., file modification impacts), leaving some behavioral aspects unclear for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by usage guidelines and an example. Each sentence earns its place by providing essential information without redundancy, and the structure is logical and efficient for quick comprehension by an agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a mutation operation with 3 parameters and no annotations) and the presence of an output schema (which reduces the need to describe return values), the description is largely complete. It covers purpose, usage, and provides an example, but could benefit from more behavioral details (e.g., error cases) to fully compensate for the lack of annotations and low schema coverage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It provides an example that clarifies the semantics of 'target' (e.g., 'LRUCache.get') and 'new_signature' (e.g., 'def get(self, key, default=None):'), adding meaningful context beyond the schema's bare titles. However, it does not explain 'file_path' or provide format details, leaving some parameter understanding incomplete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Replace only the signature of a function') and resource ('a function'), distinguishing it from siblings like 'replace_function' by emphasizing preservation of body and decorators. It explicitly contrasts with 'add_parameter' and 'remove_parameter' for parameter changes, making the purpose distinct and well-defined.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use ('changing parameters, return type, or function name without modifying the implementation') and when not to use ('Don't use this when: You also want to change the body -> use `replace_function`' and 'You're adding/removing one parameter -> use `add_parameter`/`remove_parameter`'). It names specific alternative tools, offering clear decision criteria for the 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?
With no annotations provided, the description carries full burden. It clearly indicates this is a write operation ('Append'), specifies the location ('end of the file'), and describes what gets modified. However, it doesn't mention potential side effects like file creation if non-existent, error handling, or formatting behavior, leaving some behavioral aspects uncovered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with a clear purpose statement, usage guidelines, and concrete examples. Every sentence adds value: the first defines the tool, the next two provide usage rules, and the examples illustrate parameter usage without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given this is a write operation with no annotations and 0% schema description coverage, the description does well by explaining purpose, usage context, and parameter semantics through examples. However, since there's an output schema (which handles return values), the description appropriately focuses on behavioral context rather than output details, making it reasonably complete for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It provides meaningful context for the 'content' parameter through examples showing valid content types (function, class, constant definitions). However, it doesn't explain the 'file_path' parameter's format or constraints, leaving one of the two parameters only documented in the schema's basic type information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Append arbitrary top-level content to the end of the file') and resource ('file'), with explicit examples of what constitutes top-level content (function, class, constant, type alias). It distinguishes this tool from siblings by contrasting with insert_before/insert_after for relative placement and add_method/add_field for class bodies.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit 'Use this when' and 'Don't use this when' guidance, naming specific alternative tools (insert_before/insert_after, add_method/add_field, prepend_to_body/append_to_body) for different scenarios. This gives clear context for when to choose this tool versus its siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it's 'Read-only' (safety profile), explains what gets transformed (method bodies replaced by '...'), and provides concrete examples of output format. It doesn't mention rate limits, authentication needs, or error behaviors, but covers the core operational behavior adequately.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with purpose statement, usage guidelines, and examples in distinct sections. Every sentence earns its place: the first explains what the tool does, the second provides usage context, and the third gives concrete examples. No wasted words, and information is front-loaded appropriately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, transformation behavior) and the presence of an output schema (which handles return values), the description is mostly complete. It covers purpose, usage guidelines, and behavioral context well. The main gap is incomplete parameter documentation (file_path unexplained), but otherwise it provides sufficient context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It provides meaningful context for the 'target' parameter through examples (LRUCache for class, process for function), clarifying what values are expected and how they affect output. However, it doesn't explain the 'file_path' parameter at all, leaving one of two parameters undocumented beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Return a stub view', 'returns just its signature') and resources ('class', 'function target'). It explicitly distinguishes from siblings by naming alternatives (read_symbol, get_signature) and explaining what this tool provides that they don't (public API view without implementation details).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit 'Use this when' and 'Don't use this when' sections with clear alternatives named (read_symbol for full implementation, get_signature for single method). It gives specific scenarios (understanding public API, token efficiency) and contrasts with sibling tools, offering complete guidance on when to choose this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It clearly describes the matching behavior ('by stripped text equality'), provides an example, and implies file modification. However, it doesn't mention error handling, permissions needed, or what happens if the import isn't found. It adds meaningful context beyond basic parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with purpose statement, matching criteria, usage guidelines, and example - all in four concise sentences. Every sentence adds value with no wasted words, and key information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 2 parameters with 0% schema coverage, no annotations, but an output schema exists, the description does well on purpose and guidelines but has gaps. It explains one parameter well but not the other, and while the output schema handles return values, behavioral aspects like error cases aren't covered. Good for basic use but incomplete for edge cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must compensate. It explains 'import_text' parameter meaning ('pass the exact import line you want to remove') and provides an example. However, it doesn't explain 'file_path' parameter at all. The description adds significant value for one parameter but leaves the other undocumented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Remove a matching import statement') and resource ('from a source file'), with precise matching criteria ('by stripped text equality'). It explicitly distinguishes from sibling 'remove_import_name' for multi-name imports, providing clear differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance with 'Use this when: You want to remove an unused import' and 'Don't use this when: You want to remove one name from a multi-name import -> use `remove_import_name`.' It names the alternative tool and specifies the exact scenario when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by explaining the exact removal behavior ('Stops at the first blank line or non-comment line') and providing a concrete example. However, it doesn't mention error conditions, permissions needed, or what happens if no comment exists.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Perfectly structured with purpose statement, behavioral detail, usage guidelines, and example - all in 4 sentences with zero waste. Each sentence adds distinct value and the information is front-loaded appropriately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given this is a mutation tool with no annotations, the description does well covering purpose, usage, and behavior. The existence of an output schema means return values don't need explanation. However, for a destructive operation, more safety context (like confirmation or error handling) would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description compensates by providing an example showing 'target' parameter usage ('LRUCache.get'), which helps clarify the format. However, it doesn't explain the 'file_path' parameter or provide additional semantic context beyond what the example implies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Remove the contiguous block of comment lines') and target ('immediately above a named symbol'), distinguishing it from siblings like 'replace_leading_comment'. It precisely defines what the tool does with concrete operational details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly provides both 'Use this when' (delete outdated/wrong comment) and 'Don't use this when' (update comment text) scenarios, naming the specific alternative tool 'replace_leading_comment'. This gives clear decision criteria for when to choose this tool versus its sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by explaining key behavioral traits: it handles both replacement of existing comments and insertion when none exists, clarifies what constitutes a 'contiguous leading comment block,' and implies file modification behavior. However, it doesn't mention error conditions, permissions, or whether changes are reversible.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Perfectly structured with purpose statement, usage guidelines, and concrete example in three distinct parts. Every sentence adds value with zero redundancy. The information is front-loaded with the core functionality stated first.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with 3 parameters, 0% schema coverage, and no annotations, the description does well by explaining purpose, usage, and providing an example. However, it doesn't explain what the output schema contains (though one exists), and behavioral aspects like error handling are omitted. The presence of an output schema reduces but doesn't eliminate the need for some return value context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It provides meaningful context for 'target' (named symbol like 'LRUCache.get') and 'new_comment' (the comment text to use), and the example illustrates both. However, 'file_path' parameter semantics are not explained beyond what the title suggests.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Replace the contiguous leading comment block') and resource ('above a named symbol'), and explicitly distinguishes it from sibling 'add_comment_before' by noting when not to use it. It provides a concrete example that reinforces the purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states 'Use this when: You want to update the comment text above a function/class' and 'Don't use this when: You only want to add a comment where none exists -> use `add_comment_before`.' This provides clear when-to-use guidance and names the specific alternative tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing key behaviors: skips exact duplicates, places new imports after existing ones or at top if none exist. It doesn't mention error handling, permissions, or file system effects, but covers the core operational logic adequately.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Perfectly structured: purpose statement, behavioral details, usage guidelines with when/when-not, and concrete examples. Every sentence adds value with zero waste. The information is front-loaded with the core functionality first.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given this is a mutation tool with no annotations but with an output schema (which handles return values), the description provides excellent context: clear purpose, behavioral transparency, usage guidelines, and parameter examples. It addresses what the agent needs to know to use this tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description compensates well with examples showing what 'import_text' should contain for different languages (Python, JS/TS, C/C++). It clarifies that 'import_text' is the full import statement text, not just a module name. The 'file_path' parameter meaning is implied but not explicitly explained.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Add') and resource ('import statement to a source file'), and distinguishes it from sibling 'add_import_name' by specifying it's for adding entire import statements rather than single names to existing imports. The purpose is specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly provides 'Use this when' and 'Don't use this when' guidance, naming the alternative tool 'add_import_name' for adding single names to existing multi-name imports. This gives clear context for when to choose this tool over its sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well: it discloses the tool modifies code (implied mutation), specifies it only affects signatures not bodies, and mentions default position behavior. It doesn't cover error conditions or permissions, but provides solid behavioral context for a code modification tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Perfectly structured: purpose statement first, behavioral constraint second, usage guidelines third, example fourth. Every sentence adds value with zero redundancy. The example efficiently illustrates parameter usage without being verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a code modification tool with 4 parameters, 0% schema coverage, no annotations, but with output schema present, the description is complete: covers purpose, usage, behavior, and parameter semantics. The output schema handles return values, so description appropriately focuses on tool selection and input understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so description must compensate. It provides meaning for 'target' (function name like 'LRUCache.get'), 'parameter' (string like 'default=None'), and 'position' (values 'end' or 'start' with default). Only 'file_path' lacks semantic explanation, but 3/4 parameters are well-clarified beyond schema titles.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Add a parameter'), target ('to a function signature'), and scope ('at position end or start, leaves body untouched'). It distinguishes from siblings like 'replace_signature' and 'replace_function' by specifying its limited scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly provides 'Use this when' guidance (adding 1-2 parameters without retyping signature) and 'Don't use this when' alternatives (use 'replace_signature' for entire signature, 'replace_function' for body changes). This clearly defines when to choose this tool over siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing format-specific behaviors: works for JSON/YAML/TOML/Python, explains path syntax differences between formats, and mentions JSON-specific cleanup ('removes the adjacent comma to keep the file valid'). However, it doesn't mention error handling, permissions, or what happens if the key doesn't exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Perfectly structured with zero waste: first sentence states purpose, second explains format support, third provides JSON-specific behavior, then format-specific syntax rules, followed by usage guidelines with alternatives, and concrete examples. Every sentence adds essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 2-parameter mutation tool with no annotations but an output schema, the description provides excellent context about what the tool does, when to use it, and parameter semantics. It's nearly complete, though it could mention what the output contains (but the output schema handles that). The main gap is lack of error/edge-case behavior disclosure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate fully. It provides rich semantic context for the 'target' parameter with format-specific syntax examples (JSON: 'dependencies.mcp', Python: 'CONFIG."timeout"'), explaining what the parameter represents and how it varies by file type. The 'file_path' parameter's purpose is implied through context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Delete a key-value pair') and resource ('from a dict-like container'), distinguishing it from siblings like 'remove_from_array' which handles list/array operations. It specifies the exact scope of deletion operations across multiple file formats.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states 'Use this when: You want to remove an entire dict entry' and 'Don't use this when: You want to remove an item from a list/array -> use `remove_from_array`', providing clear when-to-use and when-not-to-use guidance with named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: it's 'Read-only' (safety profile), 'syntactic only (no scope awareness)' (analysis limitations), and that 'results may include unrelated identifiers that happen to share the same name' (accuracy constraints). The example further clarifies expected output format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with clear sections: purpose statement, behavioral constraints, usage guidelines, and an example. Every sentence earns its place by adding distinct value - no repetition or filler. The information is front-loaded with the core functionality stated first.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, read-only analysis), no annotations, but with an output schema present, the description provides excellent contextual completeness. It covers purpose, behavioral limitations, usage context, and includes an example. The output schema will handle return value documentation, so the description appropriately focuses on operational context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must compensate for the lack of parameter documentation. While it doesn't explicitly name the parameters, it provides semantic context: 'target' is the identifier name being searched for, and the example shows target='LRUCache'. The file_path parameter is implied by 'in a source file' context. This adds meaningful value beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Return all occurrences of an identifier'), resource ('in a source file'), and output format ('as line N: <source line>'). It explicitly distinguishes this tool from its siblings by emphasizing it's 'syntactic only (no scope awareness)' and for single-file analysis, unlike other tools that modify code or analyze different aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance with 'Use this when:' and 'Don't use this when:' sections, clearly stating the appropriate context (quick survey for renaming/refactoring) and naming an alternative ('use a full language server') for cross-file or scope-aware analysis. This directly addresses when to choose this tool over alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: it's read-only (explicitly stated), returns a formatted outline with line numbers, and handles multiple programming languages. However, it doesn't mention potential limitations like file size constraints, error handling, or performance considerations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with the core purpose, followed by usage guidelines and an example. Every sentence adds value without redundancy, and the formatting (bulleted-like sections) enhances readability while maintaining brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (single parameter, no annotations, but with output schema), the description is complete enough. It covers purpose, usage, behavior, and parameter context effectively. The presence of an output schema means return values don't need explanation, and the description adequately addresses the tool's role among many editing-focused siblings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does 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 provides an example showing 'file_path' usage with an absolute path format, adding practical meaning beyond the schema's basic string type. However, it doesn't detail path requirements (e.g., relative vs. absolute) or supported file extensions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Return a formatted outline') and resources ('all top-level functions, classes, and methods in a source file'), and distinguishes it from siblings by focusing on structural overview rather than editing or specific symbol operations. It explicitly mentions supported languages (Python, JS, TS, C, C++) and includes line numbers.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidelines with 'Use this when' and 'Don't use this when' sections, specifying contexts (editing unfamiliar files to see structure) and alternatives (when exact target names are known). It even includes a strong recommendation ('ALWAYS a good first call before editing') to reinforce proper usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool is 'read-only', which clarifies it's a safe operation with no mutations. However, it doesn't mention potential errors (e.g., if the file doesn't exist) or performance aspects like handling large files, leaving some 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by usage guidelines and an example, with no wasted sentences. Each part earns its place by adding value: the first sentence defines the tool, the second provides context, and the guidelines and example enhance usability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter, read-only operation) and the presence of an output schema (which handles return values), the description is complete enough. It covers purpose, usage, behavioral safety, and parameter context, addressing all necessary aspects without redundancy.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 1 parameter with 0% description coverage, so the description must compensate. It adds meaning by specifying the parameter in an example ('file_path="/abs/path/to/module.py"'), indicating it should be an absolute path. This provides useful context beyond the schema's basic string type, though it could clarify format expectations more explicitly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Return all import statements') and resource ('in a source file'), distinguishing it from siblings like add_import or remove_import by focusing on read-only retrieval of dependencies. It explicitly mentions the output format ('as a multi-line string'), which adds clarity beyond the tool name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool ('when you need to see a file's dependencies without reading the entire file') and when not to use it ('Don't use this when: You want to add/remove imports'), with clear alternatives named (add_import, remove_import). This directly addresses sibling tool differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does well by explaining the tool mutates files (implied by 'Add'), specifies syntax requirements for different file types, and provides concrete examples. However, it doesn't mention error handling, permissions needed, or whether changes are reversible, leaving some behavioral aspects uncovered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with clear sections: general purpose, format-specific instructions, usage guidelines, and examples. Every sentence adds value - no repetition or fluff. The information is front-loaded with the core purpose, followed by necessary details and clear demarcation of when/not to use the tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (handles multiple file formats with different syntax rules), zero schema description coverage, and no annotations, the description provides comprehensive coverage. It explains the tool's behavior, all parameter semantics, usage context relative to siblings, and includes practical examples. The presence of an output schema means return values don't need explanation here.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does 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 all 4 parameters in detail. It clarifies that `parent_target` has different meanings for different file types (dotted path vs variable name), specifies that `value` should be a 'literal source expression in the target file's syntax', and provides examples showing how each parameter should be formatted for different scenarios.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verb ('Add') and resource ('key-value pair inside a dict-like container'), and distinguishes it from siblings by specifying it's for dict-like containers only (not lists/arrays). It explicitly mentions the supported formats (JSON objects, YAML mappings, TOML tables, Python module-level dict literals), making the scope unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidelines with 'Use this when:' and 'Don't use this when:' sections, naming specific alternative tools (`replace_value` for existing keys, `append_to_array` for lists/arrays). This gives clear context for when to choose this tool versus its siblings, addressing the exact decision points an agent would face.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively communicates key traits: it's 'Read-only' (safety), 'cheaper than reading the whole file' (performance), and returns 'full source text' (output behavior). It also clarifies scope with examples (e.g., returning just a method vs. entire class). However, it doesn't mention error handling or permissions, leaving minor gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by usage guidelines and examples. Every sentence earns its place: the first states what it does, the second provides usage rules, and the third gives practical examples. It's appropriately sized with zero wasted text, making it easy to scan and understand.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no annotations, but with an output schema), the description is complete enough. It covers purpose, usage, behavioral traits, and parameter semantics thoroughly. Since an output schema exists, it doesn't need to explain return values, and the examples bridge any remaining gaps, making it self-sufficient for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does 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 fully. It does so by explaining the 'target' parameter with concrete examples (e.g., 'LRUCache.get', 'project.version'), clarifying what it represents and how it's used. This adds significant meaning beyond the bare schema, making parameter usage clear and actionable.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Return the full source text') and resources ('single named symbol'), distinguishing it from siblings like 'list_symbols' and 'get_signature'. It explicitly mentions what it does not do (read the entire file), making its scope unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use ('when you need to see the implementation of ONE specific function or class') and when not to use ('Don't use this when: You need a structural overview -> use `list_symbols`. You only need the signature -> use `get_signature`'). It names alternatives and includes a cost comparison ('10-20x fewer tokens'), offering comprehensive usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by specifying behavioral details: it removes only the first matching element (not all), uses stripped text equality for matching, works across multiple file formats (JSON/YAML/TOML/Python), and explains format-specific target syntax (dotted paths vs. variable names). It doesn't mention error handling or permissions, but provides substantial 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured: first sentence states core functionality, second explains format variations, then clear usage guidelines, and finally concrete examples. Every sentence adds essential information with zero redundancy, and key details are front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given 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, 0% schema coverage, no annotations, but has output schema), the description provides complete operational context: purpose, usage guidelines, behavioral details, parameter semantics, and examples. The existence of an output schema means return values don't need explanation, and the description covers all other aspects thoroughly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does 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 all three parameters: 'file_path' is implied through examples showing file operations, 'target' is explained as 'dotted path to the array' for config files or 'module-level variable name' for Python, and 'value_match' is defined as the value to match using 'stripped text equality'. The examples further clarify parameter usage with concrete syntax.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Remove the first element matching value_match') and resource ('from an array/list'), with explicit differentiation from sibling tools like 'delete_key' for removing whole keys. It specifies the exact matching criteria ('stripped text equality') and supported file formats (JSON/YAML/TOML config arrays AND Python module-level list literals).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance with 'Use this when: You want to remove a specific item from a list' and 'Don't use this when: You want to remove a whole key -> use `delete_key`', naming a clear alternative. It also implicitly distinguishes from sibling tools like 'append_to_array' by focusing on removal rather than addition.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
GitHub Badge
Glama performs regular codebase and documentation scans to:
- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
Our badge communicates server capabilities, safety, and installation instructions.
Card Badge
Copy to your README.md:
Score Badge
Copy to your README.md:
How to claim the server?
If you are the author of the server, you simply need to authenticate using GitHub.
However, if the MCP server belongs to an organization, you need to first add glama.json to the root of your repository.
{
"$schema": "https://glama.ai/mcp/schemas/server.json",
"maintainers": [
"your-github-username"
]
}Then, authenticate using GitHub.
Browse examples.
How to make a release?
A "release" on Glama is not the same as a GitHub release. To create a Glama release:
- Claim the server if you haven't already.
- Go to the Dockerfile admin page, configure the build spec, and click Deploy.
- Once the build test succeeds, click Make Release, enter a version, and publish.
This process allows Glama to run security checks on your server and enables users to deploy it.
How to add a LICENSE?
Please follow the instructions in the GitHub documentation.
Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear on the server after some time, you can manually trigger a new scan using the MCP server admin interface.
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.
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