ARBuilder

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes, but some overlap exists between 'generate_backend' and 'orchestrate_dapp', and between 'ask_orbit' and 'generate_orbit_config'/'generate_orbit_deployment'. The descriptions help clarify, but an agent might occasionally misselect between these closely related tools.
Naming Consistency5/5
Tool names follow a highly consistent verb_noun pattern throughout, using snake_case. All tools start with verbs like 'ask_', 'generate_', 'get_', 'orchestrate_', or 'validate_', followed by a clear noun, making them predictable and readable.
Tool Count4/5
With 19 tools, the count is slightly high but reasonable for the broad scope of Arbitrum development support. It covers multiple domains (Stylus, Orbit, bridging, dApps), though it might feel heavy for a single-purpose server.
Completeness5/5
The toolset provides comprehensive coverage for Arbitrum development, including code generation for various components (backend, frontend, contracts, tests), configuration, deployment, validation, and Q&A support. No obvious gaps exist; it supports full CRUD/lifecycle workflows for the domain.
Average 3.2/5 across 19 of 19 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 1 of 27 community issues answered or closed in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on March 13, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool answers questions, implying a read-only, informational operation, but doesn't disclose any behavioral traits such as response format, potential errors, rate limits, or authentication needs. This leaves significant gaps in understanding how the tool behaves beyond its basic purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any fluff or redundancy. It's front-loaded with the core function and appropriately sized, making it easy to parse and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a Q&A tool with no annotations and no output schema, the description is incomplete. It doesn't explain what kind of answers to expect, how detailed they are, or any limitations (e.g., scope of knowledge). For a tool that likely provides informational responses, more context on behavior and output would be helpful to set proper expectations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds no parameter semantics beyond what the input schema provides. With 100% schema description coverage, the schema already documents both parameters ('question' and 'include_code_example') clearly. The description doesn't elaborate on parameter usage, constraints, or examples, so it meets the baseline for high schema coverage without adding extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Answer questions about Arbitrum bridging and cross-chain messaging patterns.' It specifies the verb ('answer questions') and the domain/resource ('Arbitrum bridging and cross-chain messaging patterns'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'ask_orbit' or 'ask_stylus', which likely answer questions about different topics, so it misses full sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'ask_orbit' or 'ask_stylus' for comparison, nor does it specify prerequisites, contexts, or exclusions for usage. The agent must infer usage based on the topic alone, which is insufficient for clear decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'answers questions', implying it's a read-only operation, but doesn't cover critical aspects like response format, limitations (e.g., accuracy, depth), rate limits, or authentication needs. For a Q&A tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that lists key topics upfront. It avoids redundancy and wastes no words, though it could be slightly more structured (e.g., by grouping topics). Overall, it's appropriately concise for its purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a Q&A tool with no annotations and no output schema, the description is incomplete. It doesn't explain what kind of answers to expect (e.g., text responses, links, code snippets), potential limitations, or how it integrates with sibling tools. For a tool that likely returns varied outputs, more context is needed to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with both parameters ('question' and 'question_type') well-documented in the schema. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain how 'question_type' affects responses or provide examples). With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: answering questions about Arbitrum Orbit chain topics. It specifies the scope with concrete topics (deployment, configuration, validators, etc.), which is more specific than just restating the name. However, it doesn't explicitly differentiate from sibling tools like 'ask_bridging' or 'ask_stylus', which appear to be similar Q&A tools for different topics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It lists topics but doesn't specify prerequisites, exclusions, or compare to sibling tools like 'ask_bridging' or 'generate_orbit_config'. Without such context, an agent might struggle to choose between this and other tools for Orbit-related queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions what the tool generates but doesn't describe how it works: whether it creates files, returns code snippets, requires specific permissions, has rate limits, or what the output format looks like. For a code generation tool with no annotation coverage, this leaves significant behavioral questions unanswered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single sentence that efficiently communicates the core functionality. Every word earns its place: 'Generate TypeScript backend code' (action), 'for Arbitrum dApps' (context), 'Supports NestJS and Express' (framework options), 'with viem integration' (key technology). No wasted words or redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a code generation tool with 6 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the tool actually produces (files? code blocks? project structure?), doesn't mention any constraints or requirements, and provides minimal context about how the generation works. The description alone doesn't give enough information for an agent to understand the tool's full behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the baseline is 3. The description adds minimal parameter context beyond the schema - it mentions 'Supports NestJS and Express with viem integration' which relates to the framework parameter, but doesn't provide additional semantic meaning for other parameters like contract_abi or template. The description doesn't compensate for any gaps since there are none in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate TypeScript backend code for Arbitrum dApps' with specific frameworks mentioned (NestJS and Express) and viem integration. It distinguishes from some siblings like generate_frontend or generate_tests, but doesn't explicitly differentiate from other backend-related tools like generate_messaging_code or generate_indexer.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With many sibling tools available (like generate_bridge_code, generate_messaging_code, generate_oracle, etc.), there's no indication of when backend code generation is appropriate versus specialized code generation tools. No prerequisites or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but provides minimal behavioral information. It mentions the technologies used (wagmi v2, RainbowKit) but doesn't describe what the tool actually produces (files, structure), whether it modifies existing code, error handling, or any limitations. The description is functional but lacks operational transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with just two sentences that directly state the tool's purpose and technologies. Every word earns its place with zero redundancy or unnecessary elaboration, making it efficiently front-loaded with essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a code generation tool with 5 parameters and no annotations or output schema, the description is insufficient. It doesn't explain what kind of output to expect (files, code snippets, project structure), how generated code integrates, or any constraints. The description provides basic purpose but lacks operational context needed for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description adds no parameter-specific information beyond what's in the schema, maintaining the baseline score of 3. It doesn't explain relationships between parameters or provide usage examples.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates Next.js frontend code for Arbitrum dApps using wagmi v2 and RainbowKit, providing specific technologies and target platform. It distinguishes from siblings like generate_backend or generate_tests by focusing on frontend, but doesn't explicitly contrast with other frontend-related tools like generate_bridge_code or generate_messaging_code.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives. While it's clear this is for frontend generation, there's no mention of when to choose it over other code generation tools like generate_bridge_code or generate_messaging_code, nor any prerequisites or constraints for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't mention any behavioral traits such as whether it's a read-only operation, if it has side effects (e.g., creating files or deploying code), rate limits, authentication needs, or error handling. This is a significant gap for a tool with multiple parameters and no structured safety hints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with zero waste—it directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to understand at a glance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (6 parameters, no annotations, no output schema), the description is incomplete. It lacks behavioral context, usage guidelines, and any mention of output or side effects. For a tool that likely generates code with potential deployment implications, this leaves significant gaps for an AI agent to infer correct usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema already documents all parameters thoroughly. The description adds no additional meaning beyond what's in the schema (e.g., it doesn't explain how parameters interact or provide examples). This meets the baseline of 3 when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('generate subgraph code') and the target ('for indexing Arbitrum contracts with The Graph'), which is specific and actionable. However, it doesn't explicitly differentiate from sibling tools like generate_backend or generate_frontend, which might also involve code generation for different components.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With many sibling tools focused on code generation (e.g., generate_backend, generate_frontend, generate_bridge_code), there's no indication of the specific context or prerequisites for choosing 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool 'supports chain config, AnyTrust DAC setup, and custom gas token configuration' but doesn't describe what the tool actually produces (code format, language, structure), whether it's a read-only generation or has side effects, or any constraints like rate limits or authentication requirements. For a code generation tool with no annotation coverage, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise - a single sentence that efficiently communicates the core functionality. It's front-loaded with the main purpose and includes relevant technical context (the SDK used). There's no wasted verbiage or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a code generation tool with 6 parameters and no annotations or output schema, the description is incomplete. It doesn't explain what format the generated code takes (TypeScript? JSON? CLI commands?), what the output looks like, or any behavioral characteristics. With no output schema and no annotations, the description should provide more context about the tool's behavior and results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema - it mentions 'AnyTrust DAC setup' which relates to the 'is_anytrust' parameter, and 'custom gas token configuration' which relates to 'native_token', but doesn't provide additional semantic context beyond what's in the parameter descriptions. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate configuration code for Orbit chain deployment' with specific capabilities (chain config, AnyTrust DAC setup, custom gas token). It distinguishes from siblings like 'generate_orbit_deployment' by focusing on configuration code generation rather than deployment orchestration. However, it doesn't explicitly contrast with all similar tools like 'generate_validator_setup'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With multiple sibling tools like 'generate_orbit_deployment', 'orchestrate_orbit', and 'ask_orbit', there's no indication of when configuration generation is appropriate versus deployment orchestration or general queries. The description mentions what the tool supports but not when to choose it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While it mentions what the tool generates, it doesn't describe what 'generate' entails (e.g., returns code snippets, configuration files, or full deployment scripts), whether it requires authentication, rate limits, or what happens with existing deployments. For a complex 10-parameter tool with no annotations, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that states the core purpose upfront. It could be slightly more structured by separating the three deployment types with commas or bullets, but it's appropriately sized with zero wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex tool with 10 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what 'generate' produces (code format, language, structure), doesn't mention error conditions or validation requirements, and provides minimal guidance on parameter usage. The description should do more to compensate for the lack of structured metadata.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so all parameters are documented in the schema. The description mentions the three deployment types which correspond to the 'deployment_type' enum values, adding minimal context about what each type includes. However, it doesn't explain relationships between parameters (e.g., that 'rollup_address' is only relevant for 'token_bridge' type). Baseline 3 is appropriate given high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates deployment code for Orbit chains and specifies three supported deployment types (rollup, token bridge, full). It uses specific verbs like 'generate' and identifies the resource as 'deployment code for Orbit chains', but doesn't explicitly differentiate from sibling tools like 'generate_orbit_config' or 'orchestrate_orbit'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description lists the three deployment types supported but provides no guidance on when to choose one type over another, what prerequisites exist, or when to use this tool versus alternatives like 'generate_orbit_config' or 'orchestrate_orbit'. There's no explicit when/when-not guidance or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions using RAG context and version-aware generation, but doesn't cover important aspects like whether this is a read-only operation, what permissions are required, rate limits, error handling, or what the output format looks like. For a code generation tool with 7 parameters, this leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences that cover the core functionality, context mechanism, and version support. Every phrase adds value, though it could be slightly more front-loaded by leading with the primary purpose before mentioning implementation details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a code generation tool with 7 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool returns (code snippets? full files? error formats?), doesn't mention authentication requirements, and provides no guidance on error conditions or limitations. The description assumes too much about what users already know.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 7 parameters thoroughly. The description adds minimal value beyond the schema - it mentions 'version-aware generation' which relates to target_version, and 'RAG context' which relates to context_query, but doesn't provide additional semantic context about how parameters interact or affect generation quality.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates Stylus/Rust smart contract code based on requirements, specifies it uses RAG context for examples, and mentions version-aware generation. It distinguishes itself from siblings like generate_frontend or generate_tests by focusing specifically on smart contract code, though it doesn't explicitly contrast with generate_bridge_code or other code generation tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like generate_bridge_code or generate_oracle. It mentions using RAG context and version-aware generation, but doesn't specify scenarios where this tool is preferred over other code generation tools or when it should be avoided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool generates test cases but doesn't reveal critical traits like whether it's a read-only or mutating operation, authentication needs, rate limits, output format, or error handling. For a tool with 4 parameters and no annotations, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded with a single sentence: 'Generate test cases for Stylus smart contracts.' It wastes no words and directly communicates the core purpose, 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 parameters, no annotations, no output schema), the description is incomplete. It lacks behavioral details, usage guidelines, and output expectations, which are crucial for a generation tool. Without annotations or an output schema, the description should provide more context to be fully helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds no parameter-specific information beyond what the input schema provides. Since schema description coverage is 100%, the schema already documents all parameters well, including enums and defaults. The description doesn't compensate with extra context, so it meets the baseline for high schema coverage without adding value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate test cases for Stylus smart contracts.' It specifies the verb ('Generate') and resource ('test cases for Stylus smart contracts'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'validate_stylus_code' or 'generate_stylus_code,' which could involve testing-related functions, so it misses full sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for test generation, or comparisons to sibling tools such as 'validate_stylus_code' or 'generate_stylus_code,' which might overlap in testing or code generation. This lack of usage context leaves the agent without clear direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool 'generates code' but doesn't clarify what type of code (e.g., scripts, configuration files, smart contracts), where the code is output, whether it's executable or requires additional steps, or what permissions/authentication might be needed. The description is too vague about the tool's actual behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that gets straight to the point without unnecessary words. It's appropriately sized for the tool's complexity, though it could potentially be more specific about the type of code generated.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 7 parameters (including complex enums and arrays), no annotations, and no output schema, the description is insufficient. It doesn't explain what the generated code looks like, how it should be used, what dependencies it might have, or provide any examples. The description leaves too many open questions about the tool's actual functionality and output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 7 parameters thoroughly. The description mentions 'managing Orbit chain validators, batch posters, and AnyTrust DAC keysets' which aligns with the 'target' parameter enum values, but adds no additional semantic context beyond what's already in the well-documented schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'Generate code for managing Orbit chain validators, batch posters, and AnyTrust DAC keysets.' It specifies the verb ('generate code') and resources (validators, batch posters, keysets), but doesn't explicitly differentiate from sibling tools like 'generate_orbit_config' or 'generate_orbit_deployment' that might also generate Orbit-related code.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With many sibling tools like 'generate_orbit_config', 'generate_orbit_deployment', and 'generate_bridge_code', there's no indication of what distinguishes this code generation tool from those others or when it should be selected.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool generates code but doesn't clarify what 'generate' entails—whether it produces complete deployable contracts, snippets, or documentation. It also omits details like authentication requirements, rate limits, or whether the output is deterministic based on inputs, which are critical for a code-generation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary elaboration. Every word contributes directly to understanding the tool's function, making it highly concise and well-structured for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (5 parameters, no output schema, and no annotations), the description is minimally adequate but lacks depth. It doesn't explain the output format (e.g., code files, documentation) or behavioral aspects like error handling, which are important for a code-generation tool. However, the high schema coverage partially compensates for these gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all parameters. The description adds no additional meaning beyond what's in the schema, such as explaining how 'prompt' influences the generated code or clarifying the relationships between parameters like 'oracle_type' and 'include_stylus'. Baseline 3 is appropriate when the schema handles parameter documentation effectively.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: generating Chainlink oracle integration code for Arbitrum dApps. It specifies the verb ('generate'), resource ('Chainlink oracle integration code'), and target platform ('Arbitrum dApps'). However, it doesn't explicitly differentiate from sibling tools like 'generate_backend' or 'generate_frontend' that might also produce code for dApps.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'generate_backend' or 'generate_frontend' that might overlap in generating code components for dApps, nor does it specify prerequisites or contexts where this tool is particularly appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'get concept explanations' and 'debug code issues,' implying it returns informative responses, but doesn't detail response format, potential limitations (e.g., accuracy, depth), rate limits, or authentication needs. For a Q&A tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves beyond basic purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, stating the core purpose in the first sentence and adding a supplementary note in the second. Both sentences earn their place by clarifying scope and capabilities. It avoids redundancy and is appropriately sized for a tool with four parameters, though it could be slightly more detailed given the lack of annotations.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (Q&A with four parameters), no annotations, and no output schema, the description is moderately complete. It covers the purpose and hints at usage but lacks details on behavioral traits, response format, and error handling. For a tool without structured output or safety annotations, more context on what to expect from the tool's operation would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema, only implying that parameters like target_version enable 'version-specific guidance.' It doesn't provide additional context on parameter usage or interactions. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't detract either.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Ask questions about Stylus development, get concept explanations, or debug code issues.' It specifies the verb ('ask questions') and resource ('Stylus development'), and distinguishes from most siblings (e.g., generate_* tools, orchestrate_* tools) by focusing on Q&A rather than code generation or orchestration. However, it doesn't explicitly differentiate from other ask_* tools like ask_bridging or ask_orbit, which may have overlapping domains.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through 'Supports version-specific guidance' and the input schema's parameters (e.g., question_type, target_version), suggesting it's for Stylus-related inquiries with optional specificity. However, it lacks explicit guidance on when to use this tool versus alternatives like ask_bridging or ask_orbit, or when to prefer code-generation siblings for similar tasks. The guidance is present but not fully articulated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool generates code but doesn't mention whether it requires specific dependencies, how the code is delivered (e.g., as a file or snippet), error handling, or any rate limits. This leaves significant gaps for a code-generation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste—it front-loads the core purpose and succinctly lists supported operations without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of blockchain bridging and no annotations or output schema, the description is inadequate. It doesn't explain what the generated code looks like, any required setup (e.g., SDK installation), or error scenarios, leaving the agent with insufficient context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional parameter semantics beyond implying support for ETH/ERC20 and L1/L2/L3 operations, which aligns with the bridge_type enum but doesn't provide extra value over the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Generate TypeScript code'), resource ('Arbitrum asset bridging'), and scope ('using the Arbitrum SDK. Supports ETH/ERC20 bridging L1<->L2 and L1->L3'), distinguishing it from sibling tools like generate_frontend or generate_tests that handle different code generation tasks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like ask_bridging or generate_messaging_code. It mentions what the tool supports but offers no context about prerequisites, typical use cases, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool 'generates' code, implying a creation operation, but doesn't specify whether this is read-only or has side effects (e.g., file writes), what permissions are needed, or any rate limits. The description adds minimal context beyond the basic action, leaving key behavioral traits unclear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('Generate TypeScript code for Arbitrum cross-chain messaging') and adds supporting details without waste. Every word contributes to understanding the tool's scope, making it appropriately sized and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (cross-chain messaging code generation), lack of annotations, and no output schema, the description is moderately complete. It covers the purpose and supported message types but misses details like output format (e.g., code snippets or files), error handling, or dependencies. For a code-generation tool with 2 parameters, this is adequate but has clear gaps in behavioral context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters ('message_type' with enum values and 'include_example' with default). The description adds some semantic context by mentioning 'L1->L2 retryable tickets and L2->L1 messages', which loosely maps to the 'message_type' enum, but doesn't provide additional details like code format or examples. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate TypeScript code for Arbitrum cross-chain messaging.' It specifies the verb ('Generate') and resource ('TypeScript code'), and mentions the domain ('Arbitrum cross-chain messaging'). However, it doesn't explicitly differentiate from siblings like 'generate_bridge_code' or 'generate_frontend', which reduces clarity about its unique scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by listing supported message types ('L1->L2 retryable tickets and L2->L1 messages'), suggesting it's for cross-chain messaging scenarios. However, it lacks explicit guidance on when to use this tool versus alternatives (e.g., 'generate_bridge_code' for general bridging or 'generate_frontend' for UI code), and doesn't mention prerequisites or exclusions, leaving usage context somewhat vague.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions scaffolding with scripts but doesn't disclose behavioral traits like whether this creates files locally, requires specific permissions, has side effects, or involves rate limits. The description adds minimal context beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core action ('scaffold a complete Orbit chain deployment project') and lists included components without unnecessary words. Every part earns its place by specifying scope.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (8 parameters, no annotations, no output schema), the description is adequate but incomplete. It covers the purpose and scope but lacks details on behavioral aspects, output expectations, and usage context, which are needed for a comprehensive understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 8 parameters. The description adds no additional meaning or clarification about parameters beyond what the schema provides, such as how 'prompt' influences scaffolding or interactions between parameters. Baseline 3 is appropriate given high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('scaffold') and resource ('complete Orbit chain deployment project'), listing all components included (chain config, rollup deployment, token bridge, validator management, node configuration). It distinguishes from siblings like generate_orbit_config or generate_orbit_deployment by emphasizing a comprehensive project setup rather than individual components.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives like generate_orbit_config or generate_orbit_deployment is provided. The description implies usage for full project scaffolding but doesn't specify prerequisites, exclusions, or comparative contexts with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral context. It states the tool scaffolds a monorepo but doesn't disclose what 'scaffold' entails (e.g., file creation, directory structure, dependencies), whether it overwrites existing files, requires specific permissions, or handles errors. This leaves significant gaps for a complex generation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every element (verb, resource, components) earns its place, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex tool with 6 parameters, no annotations, and no output schema, the description is insufficient. It lacks details on what the tool returns (e.g., file paths, success status), behavioral traits like idempotency or side effects, and integration with sibling tools. This leaves the agent with incomplete operational context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, providing clear documentation for all 6 parameters. The description adds no parameter-specific information beyond implying components are generated, which is already covered by the schema. Baseline 3 is appropriate since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('scaffold') and resource ('template-based dApp monorepo'), listing the exact starter components (contract, backend, frontend, indexer, oracle). It distinguishes this tool from siblings like generate_backend or generate_frontend by indicating it creates a complete monorepo with multiple components rather than individual parts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for generating a full-stack dApp from templates, but provides no explicit guidance on when to use this tool versus alternatives like generate_backend or generate_frontend for individual components, or orchestrate_orbit for Orbit-specific setups. There's no mention of prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool returns 'step-by-step commands' but doesn't cover other aspects like whether it's read-only (implied by 'Get'), error handling, rate limits, or authentication needs. For a tool with no annotations, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded: two sentences that directly state the purpose and usage guidelines without unnecessary details. Every sentence earns its place by providing essential information efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, 100% schema coverage, no output schema, no annotations), the description is adequate but incomplete. It covers purpose and usage but lacks behavioral details like output format or error handling, which are important for a tool returning workflow guidance. No output schema exists, so the description doesn't need to explain return values, but it could benefit from more context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no parameter-specific information beyond what's in the schema, such as explaining the 'workflow_type' enums or 'network' defaults. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get structured workflow information for Stylus development. Returns step-by-step commands for build, deploy, test operations.' It specifies the verb ('Get'), resource ('structured workflow information'), and scope ('Stylus development'), though it doesn't explicitly differentiate from siblings like 'get_stylus_context' or 'orchestrate_dapp'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: 'Use this when the user needs guidance on development workflows.' This indicates when to use the tool, but it doesn't specify when not to use it or name alternatives among siblings, such as 'ask_stylus' for queries or 'generate_stylus_code' for code generation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context about version-aware search and prioritization of results, but does not cover aspects like rate limits, authentication needs, error handling, or the format of returned results. This leaves gaps for an agent to understand operational constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by additional context in a second sentence. Every sentence adds value without redundancy, making it efficient and well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (5 parameters, no annotations, no output schema), the description is adequate but incomplete. It covers the purpose and some behavioral context, but lacks details on output format, error cases, or performance characteristics, which could hinder an agent's ability to use it effectively without trial and error.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description implies search functionality and version prioritization, but does not add specific meaning beyond what the schema provides, such as explaining how 'target_version' interacts with 'query' or detailing the 'rerank' algorithm. Baseline 3 is appropriate given high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Retrieve') and resource ('Stylus documentation and code examples from the knowledge base'), specifying it's for finding examples, patterns, and documentation for Stylus development. It distinguishes from sibling tools like 'ask_stylus' or 'generate_stylus_code' by focusing on retrieval rather than generation or questioning.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('to find examples, patterns, and documentation for Stylus development') and mentions version-aware search. However, it does not explicitly state when not to use it or name specific alternatives among sibling tools, such as 'ask_stylus' for queries or 'generate_stylus_code' for code generation.
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 specifies the validation method ('via cargo check'), output format ('structured errors with Stylus-specific fix guidance'), and a requirement ('Requires Docker'). However, it doesn't mention potential side effects like resource usage or execution time limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in three sentences: first states the core function, second provides usage timing, third describes output and requirement. Every sentence adds essential information with zero 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.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a validation tool with no annotations and no output schema, the description provides good context about what the tool does, when to use it, and behavioral requirements. However, without an output schema, it could more explicitly describe the return structure (error codes, line numbers, fixes) or error handling for edge cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain code format expectations or Cargo.toml template details). This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Compile-check Stylus Rust code via cargo check') and resource ('Stylus Rust code'), distinguishing it from sibling tools like 'generate_stylus_code' (which creates code) and 'get_stylus_context' (which retrieves context). It explicitly mentions the tool's function of returning structured errors with fix guidance.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does 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 ('Use AFTER generating code to verify correctness'), which clearly differentiates it from code generation siblings. It also implies an alternative (not using it after generation would lead to unverified code), though it doesn't name specific alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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