GenLayer MCP Server

Server Quality Checklist

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Latest release: v2.2.0
Disambiguation5/5
Each tool targets a distinct aspect of GenLayer development: adding specific features, creating specific contract types, generating code/documentation, or explaining concepts. There is no overlap between tools like `generate_intelligent_contract` and `generate_contract_template` as they differ in scope and output.
Naming Consistency5/5
All tool names follow a consistent `verb_noun` pattern in snake_case (e.g., `create_prediction_market`, `generate_deployment_script`). The verbs are appropriate and uniform, making the set predictable and easy to navigate.
Tool Count5/5
With 16 tools, the server covers the full development lifecycle for GenLayer contracts—creation, modification, learning, documentation, deployment, testing, and integration—without being overwhelming or sparse.
Completeness5/5
The tool surface addresses all key stages of contract development: creating contracts (templates, intelligent contracts), adding features, learning concepts, obtaining docs, generating deployment/testing/integration code, and debugging. No obvious gaps exist for the stated purpose.
Average 3.2/5 across 16 of 16 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on December 23, 2025
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
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.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral traits. It fails to disclose what gets generated (e.g., file creation, overwriting), required environment, side effects, or response format. The minimal description 'Generate comprehensive testing setups' provides no behavioral detail.
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 15-word sentence that efficiently conveys the core purpose. It is front-loaded and contains no fluff. However, it could be slightly expanded to improve completeness without losing conciseness.
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 (4 parameters with enums, no output schema), the description is incomplete. It does not explain the output format, file locations, dependencies, or any side effects. For a code generation tool, key usage context is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
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. However, the description adds no additional meaning beyond what the schema provides. It mentions only broad test types (unit, integration, e2e) while the schema includes finer-grained options like consensus_tests and llm_behavior_tests. The description fails to compensate or enrich parameter understanding.
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 'Generate' and the resource 'testing setups for GenLayer contracts', and lists included types (unit tests, integration tests, end-to-end testing strategies). It distinguishes from siblings like generate_contract_template or generate_debugging_guide by focusing specifically on testing.
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 vs alternatives, prerequisites, or when not to use it. The context of sibling tools suggests specialization, but the description itself lacks explicit usage 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?
No annotations are provided, so the description carries the full burden. It only says 'Add Equivalence Principle validation' but does not disclose whether the contract_code is modified in place or returned as new, what side effects occur, or what success/failure conditions exist. This is insufficient 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that immediately states the core function. It avoids extraneous detail and is front-loaded with the key action.
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 (4 parameters, one enum, no output schema) and lack of annotations, the description is inadequate. It does not explain the Equivalence Principle, what the validation does, how tolerance affects behavior, or what the tool returns. The tool modifies contract code but no indication of the result format.
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 baseline is 3. The description adds context by linking the validation_type to 'non-deterministic operations', but does not add meaning beyond what the schema's enum and descriptions already provide. Tolerance's default and optional nature are already 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 action ('Add Equivalence Principle validation') and the target resource ('contract method'), and adds a purpose ('for handling non-deterministic operations'). It distinguishes from siblings like 'generate_contract_template' which generate new code rather than modify existing methods.
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, what prerequisites are required, or when alternatives might be better. It does not mention that the tool modifies existing contract code or any conditions for its use.
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, the description carries the full burden of behavioral disclosure. It only describes the output generically, without mentioning any side effects, prerequisites, or limitations (e.g., that it generates code snippets requiring manual integration).
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 sentence that front-loads the action and scope. It is efficient and contains no unnecessary 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?
Despite the tool's complexity (4 parameters, nested objects) and the lack of an output schema, the description provides no information about return values, generated code structure, or how the parameters influence the output. It also does not help distinguish from sibling generation tools.
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. The description adds minimal value beyond 'including contract interaction, transaction handling, and event monitoring,' which roughly maps to integration_type enum values, but no additional meaning for individual parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it generates GenLayerJS integration code for frontend applications, listing three specific areas (contract interaction, transaction handling, event monitoring). However, it does not differentiate from sibling tools like generate_contract_interaction_examples or generate_contract_template, which could overlap.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description only states the tool's function, without any context about appropriate 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.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. However, it fails to mention key traits: whether the contract is modified in-place, permission requirements, side effects (e.g., overriding existing settings), or what happens on failure. For a mutation tool, this is critically insufficient.
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 sentence that is front-loaded with the core purpose. It is extremely concise and contains no extraneous words. Every part is meaningful for understanding the tool's high-level function.
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 lack of an output schema and the complexity of modifying a contract, the description is incomplete. It does not explain what the tool returns, whether it modifies the contract in place, or any prerequisites (e.g., contract must exist). More context is needed for safe 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 parameters are already documented in the schema. The tool description adds no extra meaning beyond the schema's parameter descriptions. Thus, the baseline score of 3 applies; no added value to compensate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Add web data access capabilities to an existing contract'. It uses a specific verb ('add') and resource ('web data access capabilities'), making it easy to understand. This purpose is distinct from sibling tools, which focus on other tasks like creating prediction markets or generating contracts.
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 explicit guidance on when to use this tool versus alternatives. It implies usage for existing contracts needing web data access, but offers no exclusion criteria, prerequisites, or mention of alternative tools. With no annotations or further context, an agent lacks decision support.
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 provided, so the description carries full burden. It only states the purpose but discloses no behavioral traits such as whether it is read-only, requires authentication, or produces any side effects. As an explanatory tool, it is likely safe but this is not stated.
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?
Single sentence that covers the main topics concisely. Lists multiple areas but remains efficient. Could be slightly more structured but is well within acceptable brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the well-described schema and no output schema, the description provides enough context for the tool's scope. It covers the primary topics but does not specify the output format or structure, though it is implied to be textual explanation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for all three parameters. The description adds no additional meaning beyond the schema's enum values and defaults. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it explains GenLayer storage patterns, including specific topics like TreeMap/DynArray. It is a specific verb-resource pair that distinguishes from sibling explain tools by focusing on storage. However, it does not explicitly differentiate from close siblings like explain_genlayer_concepts.
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 on when to use this tool versus the many other explain tools among siblings. The description does not mention alternatives or conditions for appropriate 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 provided, so description bears full burden. It only states generation from templates without disclosing side effects, customization behaviors, or whether it saves the contract. The description is too shallow for a 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence is concise and front-loaded with the action. It efficiently conveys the core purpose without fluff, though it could benefit from a short example to improve readability.
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?
Despite having no output schema, the description fails to mention what the tool returns (e.g., the contract text or a file). For a generation tool, this is a critical gap. The description is too brief for the complexity involved (nested parameters, multiple templates).
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 covers all parameters (100%), so baseline is 3. Description adds 'common use cases' which mirrors the enum, but does not explain how 'custom_parameters' interact with 'template_type' or provide extra nuance beyond 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?
Description clearly states the tool generates contracts from predefined templates and lists specific use cases (DAO governance, content moderation, sentiment tracking), which distinguishes it from siblings like 'generate_intelligent_contract' that likely create custom contracts. However, it does not explicitly differentiate itself from all siblings.
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 use when wanting a template-based contract for listed use cases, but provides no when-not-to-use instructions or alternative tool guidance. The lack of context for alternatives like 'generate_intelligent_contract' limits helpfulness.
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 exist, so the description must fully disclose behavior. It mentions 'generate' but does not specify side effects, authentication needs, rate limits, or whether the contract is returned as text or saved. This lack of detail limits transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence covering the core purpose and key features. It is concise with no wasted words, though it could be restructured to front-load the key action more explicitly.
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 6 parameters and no output schema, the description does not explain the tool's return value (e.g., generated contract code) or behavior. It lacks information on what 'generate' entails, leaving critical gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with each parameter described. The tool description adds context by highlighting LLM integration and web access, which correspond to use_llm and web_access parameters, but does not elaborate further. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates a comprehensive GenLayer Intelligent Contract with advanced features like LLM and web data access. It distinguishes from siblings like 'generate_contract_template' by specifying 'Intelligent Contract' and 'advanced features', though the exact boundaries are not fully delineated.
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 use for contracts needing advanced features (LLM, web access) but does not explicitly state when not to use or compare with alternatives. The sibling list provides context, but no direct guidance is given.
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 provided, so description should fully disclose behavior. It only states 'Create' implying a write operation, but lacks details on side effects, idempotency, permissions, or error conditions.
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?
One sentence, front-loaded, no redundancy. Every word adds value: verb, resource, qualification.
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 no output schema and no annotations, the description is insufficient. It does not explain return values, contract behavior, or necessary preconditions. The nested 'metadata_fields' parameter is not addressed despite being shown in schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear descriptions for each parameter. The description adds no further parameter details but provides high-level context ('for semantic search capabilities'). Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Create') and the resource ('GenLayer Vector Store contract') with a specific use case ('semantic search capabilities'). It distinguishes from sibling tools which are unrelated.
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 on when to use this tool versus alternatives, no prerequisites, and no context about the GenLayer platform. The description is minimal and leaves the agent to infer 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 must disclose behavioral traits. It does not mention that the tool is read-only or idempotent, nor does it describe any side effects, output format, or safety guarantees.
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, front-loaded sentence of 20 words that conveys the core purpose without unnecessary detail. It is efficient and easy to scan.
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?
With no output schema, the description should clarify the output type (e.g., code snippets) and any limitations. It omits important context such as format, size, or how the examples are delivered, leaving significant gaps for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the description adds context by summarizing the enum values (methods), but it does not explain subtleties like when to choose read vs write or the difference between SDK and CLI. This is adequate but not enhanced.
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 examples for reading/writing GenLayer contracts using specific methods (CLI, GenLayerJS, Python SDK). It is specific to contract interaction examples, distinguishing it from sibling tools like generate_contract_template, though it does not explicitly differentiate.
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 when examples of contract interactions are needed, but it provides no explicit when/when-not guidance or comparison to sibling tools such as generate_genlayerjs_integration.
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, the description must fully disclose behavior. It mentions AI resolution and web sources but omits critical details such as that it deploys an on-chain contract, any required authorization, side effects, or what happens upon invocation. The use of generic terms like 'advanced' and 'sophisticated' adds little behavioral clarity.
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 sentence that effectively communicates the core action ('Create') and key features. While the adjectives 'advanced' and 'sophisticated' add fluff, the structure is front-loaded and efficient, earning a high but not perfect score for conciseness.
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 creation tool with six parameters and no output schema, the description lacks essential context. It does not specify the result of creation (e.g., returns a contract address), deployment implications, or the lifecycle of the market. This gap leaves the agent without a complete picture of the tool's effect.
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?
All six parameters have descriptions in the schema (100% coverage), so the description adds no new parameter-level information. The description's mention of web sources aligns with the `web_sources` parameter, but does not enhance meaning beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool creates an advanced prediction market contract with specific features like AI-powered resolution and web sources. It distinguishes itself from sibling tools which focus on adding principles or generating other contract components, making its purpose explicit.
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 does not explicitly state when to use this tool versus alternatives or provide usage context. However, the verb 'Create' and the detailed schema imply it is intended for defining a new prediction market, offering a moderate implicit guidance but lacking explicit when-to-use or when-not-to-use advice.
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 implies a read-only operation (explanations) but does not explicitly state side effects, permissions, or return behavior. It is adequate but not rich.
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 concise sentence that communicates the tool's purpose without any unnecessary words. It is front-loaded and efficient.
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 simple nature and good schema coverage, the description covers the basic purpose but does not mention adjustable detail levels or code examples, which are available via schema. It is minimally complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for all parameters. The description adds no extra meaning beyond the schema, so it meets the baseline but does not improve parameter understanding.
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 provides explanations of GenLayer concepts, features, and best practices. It is specific about the resource but does not explicitly differentiate from sibling explain tools like explain_genlayer_types.
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 given on when to use this tool versus alternatives such as explain_genlayer_types or explain_storage_patterns. The description lacks any when-to-use or when-not-to-use information.
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 for behavioral disclosure. It only mentions script generation and network support, but omits details about side effects (e.g., creating files, writing to disk), permissions required, or whether the script is executed or just generated. This is insufficient for an AI agent to assess risks.
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 sentence that efficiently conveys the core purpose and key features (network support and configuration). It avoids fluff but could benefit from slight restructuring or bullet points for readability. Overall concise and front-loaded.
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?
Despite having 5 parameters (2 required) and nested objects, the description is very brief. It does not explain return values (no output schema), how to use the generated script, or any constraints (e.g., file format, dependencies). For a generation tool, this lack of context hampers proper usage by an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, with each parameter described in the schema (e.g., 'script_type', 'network_target', 'contract_path'). The description adds no extra parameter meaning beyond summarizing 'configuration options'. Baseline 3 is appropriate as the schema already documents parameters adequately.
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 it generates deployment scripts for GenLayer contracts, specifying supported networks (localnet, studionet, testnet) and configuration options. It is distinct from sibling tools like 'generate_contract_template' or 'generate_intelligent_contract', which focus on other aspects.
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 when deployment scripts are needed but provides no explicit guidance on when to choose this tool over siblings, nor does it mention prerequisites or exclusions. The network and config mention gives some context, but lacks when-to-use or when-not-to-use instructions.
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 provided. The description says 'generate' but does not disclose side effects (e.g., file overwriting, directory creation), required permissions, or any behavioral constraints beyond the obvious creation action.
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?
Single sentence clearly front-loaded with the tool's purpose. Concise and to the point, but could benefit from bullet points or more structure for readability given the number of parameters.
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?
Despite 7 parameters and no output schema, the description is very short. It does not explain the generated project structure, how parameters interact, or expected outcomes. For a complex generation tool, more contextual details are needed.
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%; every parameter is described in the schema. The tool description adds overarching context (e.g., 'complete project structure') but does not provide additional semantic details beyond what the schema already provides.
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?
Description clearly states it generates a complete GenLayer project structure, including contracts, tests, deployment scripts, and frontend integration. It distinguishes from sibling tools like generate_contract_template or generate_testing_framework which focus on 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for starting a new project, but does not explicitly state when to use this tool versus alternatives like generate_contract_template or generate_deployment_script. No guidance on prerequisites or exclusions.
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, the description carries full burden. It states the tool generates strategies and tools but does not disclose behavioral traits such as side effects (likely none), authorization needs, or output format. Adequate but minimal.
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?
Single sentence that is concise and front-loaded with the main action. No wasted words, though slightly more structure could improve clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple generation tool with 3 parameters and no output schema, the description is fairly complete but lacks detail on the format of the output (e.g., text guide, code snippets) and how it relates to sibling tools. Missing output schema limits 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 coverage is 100% and includes clear descriptions for all parameters. The description adds context by mentioning logging, error handling, and testing approaches, but does not significantly enhance understanding beyond 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?
Description clearly states it generates debugging strategies and tools for GenLayer contracts, mentioning specific areas like logging, error handling, and testing. This differentiates it from siblings like generate_contract_template or generate_testing_framework, though not explicitly.
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?
Implies usage for debugging contracts but provides no explicit guidance on when to use versus alternatives like generate_testing_framework, nor when not to use. Lacks exclusion 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. It describes an informational tool with no side effects, but it does not disclose any specific behavioral traits beyond the 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, focused sentence that efficiently lists covered topics. No unnecessary words; front-loaded with the core action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (informational, 3 parameters fully described, no output schema needed), the description adequately covers the scope. It mentions categories and includes examples/comparisons implied by parameters.
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 baseline is 3. The description adds an overview of topics (including 'best practices') not explicit in parameter descriptions, but it does not provide additional semantics for individual parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: explaining GenLayer's type system with specific topics (primitive types, collections, etc.). It distinguishes itself from siblings like 'explain_genlayer_concepts' and 'explain_storage_patterns'.
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 understanding the type system but does not explicitly state when to use this tool over alternatives or when not to use it. No exclusions or alternatives are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the behavioral burden. The verb 'fetch' implies a read operation, but the description does not explicitly state it is read-only or disclose any other behavioral traits like caching or permissions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences that are front-loaded and contain no unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one optional parameter and no output schema, the description provides adequate context about its purpose and what it returns (documentation). It could specify output format or size limits, but is largely sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the schema already describes the 'topic' parameter clearly with enum values. The description echoes this but adds no new meaning beyond 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 tool fetches the latest GenLayer API documentation from the official source, listing specific topics. It is distinct from sibling tools which focus on generating or adding content.
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 explains when to use the tool (to get up-to-date documentation) and implies it is for information retrieval, but does not explicitly mention when not to use it or provide alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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