super-agent-skill

by Akash1070

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

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Latest release: v16.0.0
Disambiguation3/5
Many tools share the 'generate_*_blueprint' pattern with highly specific domains (e.g., generate_seo_metadata_blueprint vs generate_seo_aeo_geo_blueprint vs audit_seo_aeo_geo_readiness), which could cause confusion between similar-but-distinct tools. However, each tool targets a distinct output domain, so boundaries are mostly clear, but the sheer number and similar phrasing make misselection possible.
Naming Consistency4/5
The majority of tools follow a consistent 'verb_noun' pattern with verbs like generate, get, audit, log, search, scout, detect, modernize, diagnose, evaluate. There are minor inconsistencies: 'ponytail_codebase_modernizer' uses a different structure, and some verbs are specific but not uniform. Overall, the pattern is predictable and readable.
Tool Count2/5
63 tools is far beyond the typical well-scoped range and approaches extreme mismatch. While the server covers a broad range of developer assistance (from design to deployment to marketing), the sheer number makes it unwieldy for an agent to choose from. The count is excessive for a single server, likely leading to selection overhead.
Completeness4/5
The tool surface covers an impressively wide range of developer needs: design, backend, frontend, deployment, security, SEO, AI workflows, memory, and even marketing. However, there are some gaps: no explicit tool for database schema generation beyond API contracts, no tool for code refactoring beyond modernization, and memory tools seem focused on logging but missing a delete/update function. But given the broad scope, the coverage is quite comprehensive.
Average 3.3/5 across 55 of 63 tools scored. Lowest: 2.7/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 27 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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.
Add a glama.json file to provide metadata about your server.
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?
With no annotations provided, the description must carry full behavioral disclosure. It only states the tool 'generates' blueprints, but does not describe output format, depth of detail, any prerequisites, or side effects. It also fails to clarify that the tool is likely a read-only generator, leaving significant behavioral ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise and easy to read. However, it includes extraneous information (Shopify API) that is not in the schema and fails to cover all valid options, making it structurally imperfect despite its brevity.
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?
The tool has only one parameter and no output schema, so the description should compensate by explaining what a blueprint entails and how to select the right domain_type. It does neither effectively, and the mismatch between description and schema leaves the agent with an incomplete picture of the tool's capabilities.
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?
The schema covers 100% of parameters, so the baseline is 3. The description adds context for e-commerce and CMS options but omits the wasm_webassembly_setup value entirely and introduces a non-existent 'Shopify API' value. This partial and inconsistent explanation misleads rather than clarifies the enum semantics.
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 identifies the tool as generating production integration blueprints for e-commerce (Stripe Checkout, Shopify API) and headless CMS (Payload, Strapi, Sanity) domains. It specifies concrete resources. However, the mention of Shopify API does not match the enum, and the enum includes wasm_webassembly_setup which is entirely omitted, creating ambiguity about the tool's actual scope.
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?
There is no guidance on when to use this tool versus the many sibling blueprint-generator tools (e.g., generate_fintech_trading_blueprint, generate_observability_blueprint). No explicit conditions or alternatives are provided, leaving the agent to guess based solely on the name and vague description.
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 is the sole source of behavioral disclosure. It only states what the tool generates but does not mention side effects, whether it returns data or sends a notification, any permissions required, or whether it modifies state. Given the mutation-like nature of 'generating' a banner, the description is insufficiently transparent.
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 with no filler. It front-loads the main action and specifies the content. It is appropriately sized for the tool's simplicity, though it could potentially include usage guidance without becoming overly long.
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 all parameters documented in the schema and no output schema, the description leaves out critical context such as the return format, how the banner is delivered, and what 'Karpathy safety compliance' means. An agent might struggle to know what to do with the output. The tool is simple, but the description is not complete enough for confident invocation without further assumptions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the baseline is 3. The tool description adds some context by connecting parameters to 'before-and-after metrics' and 'refactored components', but it does not provide explicit parameter-specific details beyond what the schema already states. The mention of 'Karpathy safety compliance' is orthogonal to the parameters and does not clarify their semantics.
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 an In-IDE improvement banner and lists its content (before-and-after metrics, refactored components, Karpathy safety compliance). It specifies a concrete verb, resource, and output artifact. However, it does not explicitly differentiate this tool from the many sibling 'generate_*' tools, so it misses the additional distinction that would warrant a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
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 exist, or how it differs from alternatives. It does not mention any context, exclusions, or recommended conditions. An agent is left to infer when this tool should be used instead of other generate_* 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?
No annotations are supplied, so the description carries the full burden of behavioral disclosure. It mentions 'Generates' and 'high-CTR' but does not explain whether the operation is non-destructive, what the output format looks like, how many variations are returned, or any rate/limits. The description offers minimal behavioral insight beyond the verb.
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?
A single sentence with no filler, front-loads the core purpose, and packs the structural framework into a compact parenthetical. Every word earns its place.
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 absence of an output schema and annotations, the description should describe the return shape and clarify platform coverage. It does neither, and it contradicts the platform enum. The tool is simple, but the description still leaves meaningful gaps for correct invocation.
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 coverage is 100%, so the schema already documents both parameters. The description adds the copy-structure detail, which is useful for understanding what offer_summary should contain, but it actively misrepresents the platform parameter by listing only Meta, Google, and Twitter while the enum includes linkedin_b2b. This is a misleading semantic contribution.
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 states a specific verb ('Generates') and resource ('Meta, Google, and Twitter ad copy variations') and gives a clear structural breakdown (Hook -> Problem -> Solution -> CTA), which is helpful. However, it omits LinkedIn from the platform list even though the schema's enum includes linkedin_b2b, creating an inconsistency that blurs the tool's scope.
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?
There is no guidance on when to use this tool versus its many siblings such as generate_marketing_copy_blueprint, generate_hormozi_offer_blueprint, or generate_brand_positioning_matrix. No conditions, exclusions, or alternative routing are provided, leaving the agent to infer when this specific ad-copy generator is 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only says 'generates,' implying a read-only output, but it does not specify whether the tool has side effects (e.g., storing memory), what happens on failure, or that it returns a structured output. This is a significant gap for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with zero filler. It front-loads the action verb 'Generates' and lists the key outputs in a clear, parallel structure. Every word earns its place.
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 simple 2-parameter schema and no output schema, the description should still clarify the return format or any special behavior. It doesn't; the tool name suggests a 'matrix' output but that is not confirmed. No mention of required background knowledge or failure conditions. An agent would need to guess or rely on other context to fully use this tool correctly.
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 both parameters are fully documented in the schema. The description adds no extra meaning about the parameters themselves; it only mentions outputs like 'pain-point mapping,' which is not tied to specific parameter semantics. The baseline for high schema coverage is 3, and the description doesn't elevate it.
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 states a specific action (generates) and a clear resource (brand positioning matrix components: hooks, category framing, pain-point mapping). It is not a tautology and clearly distinguishes itself from the many other generate_* tools in the sibling list, though it doesn't explicitly name an alternative.
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, nor any prerequisites or conditions. The context is solely implied by the tool's name and description; the agent must infer that it is for brand positioning work, but there is no explicit 'use this when...' statement.
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 'generates pipelines' but does not mention whether it modifies files, requires permissions, produces a return value, or has side effects. The description is too vague about the actual behavior beyond the generation action, leaving significant ambiguity.
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, well-structured sentence that front-loads the primary action and key resource types. Every phrase adds value—mentioning parsing, chunking types, and supported file formats—without redundant or fluff content. It is appropriately sized for the tool's simplicity.
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, no annotations, and only two parameters, the description should offer more context about what the generated pipeline looks like, how to use it, and what the agent can expect. The description is essentially a list of capabilities without explaining the output format or usage flow, leaving the agent with incomplete information to call the tool correctly.
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 covers both parameters (document_type and chunk_size) with descriptions. The description adds context by listing the exact document types and chunking methods, which aligns with the enum values, but it does not add any detail about chunk_size beyond what the schema provides. Since schema coverage is 100%, this meets the baseline of 3 but does not exceed it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'generates' and the specific resources: document parsing, chunking (with chunking types named), and embedding pipelines for three file types. It is specific and differentiates from siblings, which focus on other blueprint types. However, it does not explicitly contrast with any sibling, so it's slightly below a perfect 5.
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 gives no guidance on when to use this tool versus alternatives. It does not mention any sibling tools, prerequisites, or situations where this tool is preferred. The context of when to use it is only implied by the tool's name and description, leaving the agent to infer the appropriate scenario.
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 files but does not mention output format, whether it overwrites existing files, if it requires any prior setup, or what the return value looks like. Critical behavioral details for a generation tool are missing.
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 effectively communicates the core function. It is front-loaded with the action and object, and there is no tangential or redundant content. A slightly more detailed description could be justified, but the current length is 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?
For a simple generation tool, the description explains the purpose well, but it lacks details about the expected output (no output schema) and any side effects. While the parameter descriptions are clear, the absence of behavior disclosure leaves gaps for an agent needing to know how to handle the result. Moderate completeness given the tool's simplicity.
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 input schema has 100% coverage with descriptive parameter explanations (e.g., 'Core mission summary' for summary). The description does not add any additional meaning beyond what the schema already provides. Since the schema is comprehensive, a baseline 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 the tool generates 'standardized llms.txt and llms-full.txt files' with a specific purpose (indexing by AI crawlers). It uses a specific verb (generates) and names the exact resources. However, it does not explicitly differentiate from the many sibling 'generate_*_blueprint' tools, though the resource name is distinct enough to avoid confusion.
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. It doesn't mention prerequisites, appropriate usage context, or when not to use it. The intended use is only implied by the description and not stated explicitly.
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 only mentions what is generated, not any side effects, prerequisites, output format, or potential impacts (e.g., whether it writes to disk, requires API keys, or has rate limits). For a generation tool, the lack of output details is a notable gap.
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 packs the core purpose and key components without excessive fluff. It is front-loaded with the main verb and resource, though it could be slightly more compact by moving examples later. Efficiency is good.
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 3-parameter tool with no annotations and no output schema, the description should clarify what a 'blueprint' contains, what the return value looks like, and when it should be chosen over similar tools. None of these are addressed. An agent calling this would know it produces a blueprint but not what to do with the result or how it differs from other blueprint generators.
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 having a descriptive line. The description adds some architectural context (e.g., mentions LangGraph/LangChain) but does not elaborate on individual parameters beyond what the schema already provides. Since schema does the heavy lifting, a baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Generates') and resource ('production Retrieval-Augmented Generation (RAG) architecture blueprints') and enumerates key components (Vector DBs, local LLMs, LangGraph/LangChain). This clearly distinguishes it from sibling blueprint tools like generate_fintech_trading_blueprint or generate_observability_blueprint. However, it doesn't explicitly name the sibling alternatives, so it loses a point for not directly contrasting with them.
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. There is no 'Use this when...' or mention of scenarios that would rule out other blueprint generators. Given the large set of sibling tools, an agent has to infer that RAG-related requests map here, but that is not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears the full burden of behavioral disclosure. It states the technologies involved but does not describe the output format, side effects, dependencies, or whether it is read-only. The agent is left without knowledge of what to expect after calling.
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, front-loaded sentence that immediately conveys the core purpose. It is concise, though the enumeration of technologies adds length without significant value.
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?
The tool lacks an output schema, so the description should explain what the generated blueprint contains and how parameters influence it. It does not mention return format, structure, or usage examples, making it incomplete for a generation tool with only this description.
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 both parameters are already documented in the schema. The description adds a list of technologies but does not clarify how they relate to specific parameter values or provide additional semantics beyond what the schema offers.
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 production authentication blueprints and specifies relevant technologies (better-auth, NextAuth, OAuth2, etc.). It is specific about the resource and action, though it does not differentiate from sibling blueprint generators beyond the auth domain.
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 the many other blueprint generators in the sibling list. It implies usage for auth needs but provides no conditions, exclusions, or 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 must carry the full burden of behavioral disclosure. It states it 'Recommends' a stack, implying a read-only operation, but does not explicitly state side effects, required permissions, output format, or any constraints. The description is silent on whether this is a safe call, what the return structure looks like, or if it has any rate limits. This is a significant gap for an unannotated 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, information-dense sentence that efficiently lists the technology options and the selecting criteria. There is no filler or redundancy, and the key action ('Recommends') and subject are front-loaded. It earns a high score for conciseness, though it could be structured slightly better with explicit separation of alternatives.
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 recommendation tool with two parameters and no output schema, the description is incomplete. It does not describe the expected output (e.g., a single stack vs. a breakdown), any constraints or limitations, or how to interpret the recommendation. It also lacks guidance on when to prefer this over the sibling 'get_free_developer_stack_recommendation'. Given the simplicity of the tool, a bit more context would make it fully usable.
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%, with both 'project_type' and 'expected_scale' already documented in the schema. The tool description adds a high-level link to 'project scale, speed, and database requirements', which loosely maps to the parameters but does not provide syntax, formatting, or examples beyond what the schema already gives. Since the schema is thorough, the description adds minimal value, but does not contradict or omit necessary semantics.
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 'Recommends high-performance backend architecture' and lists specific technologies (PocketBase, Appwrite, Express, FastAPI, PostgreSQL + Prisma/Drizzle). It indicates the decision criteria (project scale, speed, database requirements), giving a clear purpose. However, it does not explicitly distinguish itself from the sibling tool 'get_free_developer_stack_recommendation', relying on the word 'high-performance' as a subtle differentiator rather than naming alternatives.
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 any alternatives. The description mentions input criteria but does not address exclusions, prerequisites, or how this differs from other recommendation tools like 'get_free_developer_stack_recommendation'. An agent receives no help choosing between this and similar 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?
No annotations are provided, so the description carries the full burden. It describes the output content but does not disclose whether the tool is read-only, mutating, reversible, or what the response format is. For a generation tool, it likely returns recommendations without side effects, but this is not stated, leaving uncertainty for the agent.
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, well-structured sentence that front-loads the primary action ('Generates tailored design systems') and lists key components efficiently. It avoids redundancy and is appropriately sized for the tool's complexity. Slightly long due to enumeration, but still concise.
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?
With no output schema and no annotations, the description partially explains what the tool produces (design system elements) but omits the output structure, usage constraints, and any side effects. It also lacks guidance on how it differs from similar tools, making it incomplete for an agent to confidently choose and use it.
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 both parameters (project_domain and theme_preference) are documented. The description adds marginal value by mentioning 'based on your project domain' and listing some aesthetic styles, but it does not explain parameter syntax, formats, or how they map to the output beyond what the schema already provides. 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 tailored design systems' and lists specific elements (color palettes, typography pairings, aesthetic styles, layout patterns, animation rules). This is specific and informative, distinguishing it from generic tools, though it doesn't explicitly differentiate from sibling tools like get_ui_component_blueprint or audit_ui_design_aesthetic.
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 implies it's used for generating design recommendations based on project domain, but there is no explicit 'use this when' or mention of scenarios where another tool would be more appropriate. Sibling tools cover related areas, but no differentiation is offered.
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 responsibility for disclosing behavioral traits. It does mention a side effect ('automatically logging into persistent agent memory'), which indicates a write operation, but it omits details such as whether this overwrites existing memory, whether it requires authentication, or potential rate limits from the GitHub API. The description is vague about the memory update mechanics and any risks, leaving significant gaps for an agent.
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, dense sentence that front-loads the primary action (querying GitHub API) and the secondary effect (logging to memory). It is efficient and avoids redundant wording, though it could be slightly more structured for clarity. The information is presented in a logical order with no wasted phrases.
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 that this tool performs a network call and a persistent memory write, the description is incomplete. It does not specify what the tool returns (no output schema), the exact nature of 'architectural patterns' being logged, or how it relates to the sibling get_latest_trending_repos. The absence of usage guidance and behavioral details makes it difficult for an agent to know the full scope of side effects and expected 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 both parameters (min_stars and domain_query) are fully described in the schema. The description itself does not add semantic value beyond what the schema provides, except implicitly associating domain_query with 'domain' and memory logging. Since the schema already covers parameter meaning, the 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 the tool queries GitHub's search API for trending repos/libraries and logs patterns to persistent memory. It is specific about the action and resource, but does not explicitly differentiate from the sibling get_latest_trending_repos, which likely performs a simpler query without memory updates. The purpose is unambiguous enough for an agent to understand its core function.
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 gives no explicit guidance on when to use this tool versus alternatives. It implies continuous skill updates ('automatically logging new architectural patterns') but does not state when to prefer it over a plain repo query like get_latest_trending_repos, nor any prerequisites or conditions. An agent would have to infer usage context.
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 disclosing behavioral traits. It mentions output style (responsive, dark OLED glassmorphism, real-time metric cards) but does not disclose side effects, return format, approval requirements, or any operational constraints. Since this is likely a generation tool, there is no discussion of whether it modifies state or requires external dependencies.
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, tight sentence that starts with the action verb and the object, then lists specific libraries and styling keywords. Every phrase adds information — no filler or redundancy. It is front-loaded with the core purpose and efficiently communicates the main selling points.
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 tool with 2 enum parameters and no output schema, the description is reasonably complete for selecting it, but it does not describe what the generated dashboard components look like in terms of return structure (e.g., code snippets, file structure, or how to integrate them). It implies a generation of frontend code, but an agent might need more details on expected output to use the result correctly. Given the tool's simplicity, this is adequate but not exceptional.
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% — both parameters (chart_library and dashboard_niche) already have descriptors in the schema. The description adds context about production quality and aesthetics but does not add new meaning to the parameters themselves (e.g., which niche maps to which chart style). The baseline of 3 is appropriate because the schema does the heavy lifting and the description offers marginal extra interpretation.
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 function: it generates production dataviz dashboard components, naming specific chart libraries (Recharts, Chart.js, Tremor) and design themes. This is a specific verb-resource pair that is immediately understandable. However, it does not explicitly differentiate from sibling tools like generate_fintech_trading_blueprint or generate_observability_blueprint, which could overlap depending on the niche.
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 does not mention scenarios where this is preferred over more niche generators (e.g., fintech) or when to avoid it. No exclusions or alternative tool names are given, leaving the agent to infer usage from the tool name alone.
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 only mentions generating a roadmap, without specifying output format, length, how stages influence the output, or any side effects. The phrase 'step-by-step' gives a hint, but overall the behavioral detail is minimal.
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 with no filler. 'Zero-BS' and 'step-by-step execution roadmap' are front-loaded, making the purpose immediately clear. Every word earns its place.
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 tool with only two parameters and no output schema, the description is adequate for understanding the basic action. However, given the context of many sibling tools, there is no disambiguation or detail on how the stage parameter shapes the output, leaving gaps for an agent deciding between this and similar 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% since both parameters have descriptions. The tool description adds no extra meaning beyond what the parameter names and descriptions already convey. It does not explain how 'current_stage' affects the roadmap output, so it meets the baseline but adds no 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 generates a step-by-step execution roadmap for developer value, using a specific verb and resource. However, it does not explicitly differentiate from the sibling 'identify_project_stage_and_roadmap', which could overlap in purpose, so it loses a point on 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?
No guidance is provided on when to use this tool versus alternatives like 'identify_project_stage_and_roadmap'. The description implies general use at any stage of building, but there are no exclusions or explicit context for when this tool is the best choice.
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 fully disclose behavioral aspects. It states the tool 'Generates' workflows, implying a non-destructive operation, but it does not indicate what the output looks like (e.g., YAML content, file path), whether it requires a repository context, or any other side effects. The lack of such details means the agent cannot anticipate the tool's full behavior, earning a low score.
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, well-structured sentence. It front-loads the core purpose ('Generates production GitHub Actions YAML workflows') and then lists specific areas. Every word adds value; there is no redundancy or extraneous detail. This is an excellent example of concise writing.
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?
The tool is a generation tool with one parameter and no output schema, so the description should explain the return value or how the output is delivered. It currently only states what the workflow covers, not what the agent receives (e.g., YAML string, file creation). It also lacks any prerequisites or usage examples. While the description is adequate for basic understanding, it is incomplete for fully anticipating the tool's behavior, so a 3 is appropriate.
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 provides full documentation for the only parameter (workflow_type) with an enum and description. Since schema coverage is 100%, the baseline is 3. The tool description does not add significant meaning beyond the schema; it only mentions general activities like testing and deployment but does not map them to specific enum values. Thus, it stays at the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Generates' and the resource 'GitHub Actions YAML workflows', and specifies the scope ('production', 'automated testing, security scanning, Docker building, and zero-downtime deployment'). It is specific enough to convey the tool's function, though it does not explicitly differentiate from siblings like generate_docker_setup or get_deployment_checklist, so it does not earn a 5.
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?
There is no guidance on when to use this tool over its siblings. It does not mention alternatives, nor does it specify conditions like when a user needs a full CI/CD workflow versus a single Docker setup. The only implicit guidance is the tool's name and description, which is insufficient for an agent to confidently choose it among many generation 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?
No annotations are provided, so the description must carry the full behavioral burden. 'Converts' implies a transformation, but it does not disclose whether the tool has side effects, the return format (just a string? a structured object?), or any dependencies. The agent has no indication of what a successful invocation produces or what happens on failure.
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?
A single, front-loaded sentence conveys the core purpose with no filler. Every word adds value, and the key concept ('Karpathy verification checklist') appears early.
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?
The tool is simple (one param, no output schema), so the description is almost sufficient. However, it does not explain what a 'Karpathy' checklist entails or how the output will be structured, which could leave an agent uncertain about the nature of the returned artifact. A brief clarification of the expected output format 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?
The schema describes the single parameter with 100% coverage ('Coding task to create checklist for'), so the description adds minimal extra meaning. The term 'goal-driven' hints at what kind of task is appropriate, but the schema already communicates the basic semantics. 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 action ('converts'), the input ('coding task'), and the output ('goal-driven Karpathy verification checklist with explicit test criteria'). It specifies the type of checklist and its emphasis, which distinguishes it from siblings like 'generate_test_suite_blueprint' or 'evaluate_coding_plan', though it does not explicitly name alternatives.
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, exclusions, or situations where another sibling tool would be more appropriate. The agent must infer usage solely from the purpose.
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 states it generates a report, but does not mention any side effects, limitations, or output details. An agent cannot infer whether this is a read-only operation or if it triggers any state changes, making transparency inadequate.
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 with no filler. It front-loads the verb 'Generates' and immediately specifies the resource, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no parameters, output schema, or annotations, the description is the only source of guidance. It does not explain what the report contains beyond 'bug prevention rules and best practices', nor how to interpret or use the output. Given the many similar sibling tools, an agent would need more context to confidently select and invoke it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and the schema is empty, so the description does not need to explain parameters. Per the baseline rule for zero parameters, a 4 is appropriate since the schema fully documents the absence of inputs and there is nothing for the description to add.
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 curated report of community-proven bug prevention rules and best practices, with a specific source (100+ developers). It is distinct enough from siblings like get_karpathy_rules, but does not explicitly differentiate itself from other rule-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?
No guidance is given on when to use this tool versus alternatives. Since there are many similar generation tools in the sibling list, an agent would have no explicit criteria to choose this one. The description only states what it does, not when it should be preferred.
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 only states the tool's purpose and output (a blueprint) but does not reveal important behavioral traits such as output format, detail level, how parameters affect the output, or any limitations. The agent gets no insight into what the returned blueprint looks like or how to interpret it.
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 one concise sentence with no fluff. It front-loads the primary purpose and immediately lists the components covered. Every word earns its place; there is no redundant content.
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 tool that outputs a structured recommendation, the description is adequate but lacks details about the output format. It lists the components, which gives the agent an idea, but does not explain how the inputs influence the blueprint or what the deliverable will look like. Given no output schema, a bit more context (e.g., 'returns a step-by-step guide' or 'selects providers per category') 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?
Both parameters have explicit descriptions in the schema (app_type: 'Type of application being built'; primary_language: 'Primary backend / framework'), and schema coverage is 100%. The description adds no additional semantic detail beyond what the schema provides, so the 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 the verb 'Recommends' and the resource: a 'production-grade $0/month full-stack infrastructure blueprint' listing the components (Hosting, DB, Auth, etc.). It is specific and unambiguous, but it does not explicitly distinguish itself from sibling tools like get_backend_stack_recommendation, though the name and scope imply a free-tier full-stack focus.
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 gives no guidance on when to use this tool versus alternatives. It does not mention scenarios that favor this tool over sibling recommendations (e.g., when a zero-cost stack is desired) or any exclusions. The usage context must be inferred entirely from the name and description, which is insufficient.
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 must convey behavioral details on its own. It states the tool writes to a 'persistent memory database', which implies a write operation, but it does not disclose whether logging is idempotent, whether duplicate entries are possible, or what the success/error response looks like. For a mutation tool, this is a significant transparency gap.
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 clear sentence, front-loaded with the action and scope. It is efficient and free of filler, though it could be improved by adding usage guidance or behavioral notes, but those are separate dimensions. Structure is clean and easy to parse.
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 two-parameter tool with no output schema and no annotations, the description is incomplete. It tells what the tool does but not when to use it (usage context) or what happens on invocation (behavioral guarantees). While the schema covers parameter semantics, the description leaves the agent without guidance on when this tool is appropriate or what results to expect, making it insufficient for reliable selection.
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%, as both category and preference have descriptions in the schema. The description adds loose synonyms (e.g., 'styling taste' for design, 'tech stack choice' for tech_stack) that map to the enum values, but does not provide any new operational meaning beyond what the schema already documents. This meets the baseline for high 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 uses a specific verb ('Logs') and resource ('user preference, styling taste, tech stack choice, or coding habit') and names the destination ('persistent memory database'). This distinguishes it from sibling read tools like get_agent_memory and search_agent_memory, as well as from log_mistake_autopsy, by clarifying what type of information is stored.
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 does not explicitly state when to use this tool versus alternatives. It implies usage for capturing user preferences but gives no contrast with log_mistake_autopsy (for mistakes) or memory-read tools. There is no 'when not to use' guidance or mention of alternative tools to prefer in specific cases.
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 disclose behavioral traits, but it only states the scanning action. It does not mention whether the operation is read-only, what output format to expect, or any potential side effects. Dropping the burden means an agent cannot anticipate runtime 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 a single, concise sentence that front-loads the verb and purpose. Every word contributes meaning without 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 tool with one parameter, no output schema, and no annotations, the description leaves out critical details such as expected results, error conditions, and whether the analysis is synchronous. An agent would have insufficient context to call it correctly and interpret the outcome.
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 already describes the code_snippet parameter (100% coverage). The description adds context about what the tool detects, which implies the snippet should be source code, but it does not specify requirements like language support or size limits. It only slightly enhances the schema's description.
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 scans code for specific issues: dead code, unused imports, redundant re-render triggers, and bloated dependencies. This specific verb and resource set it apart from sibling tools like audit_performance_bottlenecks.
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. With many sibling tools that could overlap (e.g., audit_performance_bottlenecks, modernize_legacy_code), the description does not explain why this tool is the right choice or under what conditions it should be preferred.
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 burden of behavioral disclosure. It clearly states the tool 'Generates' content, implying a non-destructive creation action. However, it does not explain any side effects, required permissions, output format, or potential variability. The description is basic but not misleading; it adds some context beyond a pure tautology.
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, well-structured sentence that front-loads the primary outputs. It avoids fluff and stays within one line, making it easy to scan. No redundancy or unnecessary detail is present.
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?
The tool has only two parameters and no output schema, so the description must explain what is returned. It names the three deliverables (resume, portfolio README, brand positioning) but does not clarify how they are presented (e.g., separate files, combined document) or how inputs influence each. For a generation tool producing multiple artifacts, some additional detail on output structure would be helpful, though the description is not seriously deficient.
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%: both parameters (developer_role, key_achievements) have meaningful descriptions in the schema. The tool description does not add any additional meaning or clarify edge cases beyond what the schema already states. Since the schema fully documents each parameter, the baseline 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 uses a specific verb ('Generates') and clearly names the resources ('developer resume, GitHub portfolio README, and personal brand positioning'). It gives enough detail to understand the core function. It does not explicitly differentiate from sibling tools like generate_brand_positioning_matrix or generate_developer_value_walkthrough, but the unique combination of outputs makes it reasonably distinct.
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?
There is no guidance on when to use this tool versus alternatives. The description does not mention any conditions, exclusions, or mention of sibling tools. Given the large list of sibling generation tools (e.g., generate_brand_positioning_matrix, generate_marketing_copy_blueprint), an agent lacks criteria to choose this specific tool over others with overlapping goals.
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 carry the full behavioral burden, but it only lists output types and gives no insight into side effects, validation rules, required permissions, or return format. It does not contradict annotations (none exist), but it omits critical behavioral context for a generation tool, such as whether it validates character limits or produces deterministic output.
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, front-loaded sentence that efficiently lists all output categories. It avoids unnecessary words and directly communicates the core deliverable, though it could be slightly more structured with separate clauses for readability.
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?
The description outlines what is generated but does not clarify the shape of the output (e.g., a structured object, concatenated HTML, or separate strings) or any constraints like character min/max for meta descriptions. Given that the input schema is well-documented and no output schema exists, the tool would benefit from a note on how results are returned, making this minimally complete but not fully contextual.
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%, with every parameter already documented in the schema (e.g., page_title as 'Title of the page or web application'). The description adds no additional parameter meaning, so it rests at the baseline of 3 expected when the schema fully covers parameter semantics.
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 states a specific verb ('generates') and a detailed list of outputs (HTML head tags, OpenGraph, Twitter Cards, JSON-LD, sitemap metadata), making the tool's purpose crystal clear. It distinguishes itself from sibling tools like generate_seo_aeo_geo_blueprint by focusing narrowly on metadata generation rather than broader SEO strategy.
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. Sibling tools like generate_seo_aeo_geo_blueprint might overlap, but the description does not mention any selection criteria, prerequisites, or exclusions. The agent is left to infer usage from the name and description alone.
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 bears the full burden of behavioral disclosure. It only states the output intent ('generates blueprints') without any mention of side effects, whether the tool is read-only, what the response format is, or if any state is stored. Given that the tool name starts with 'get', it likely reads/generates data, but this is not explicitly stated, and there is no information on output structure or delivery.
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 that efficiently conveys the verb, key deliverables (layouts, navigation stacks, UI blueprints), and target frameworks. There is no filler or redundant information. It is appropriately sized for the tool's simplicity.
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 and no annotations, the description must explain what an agent can expect from the call. It does not describe the return value (e.g., JSON, code, diagram), any prerequisites, or behavior such as rate limits or authentication. While the tool has only two parameters, an agent cannot fully anticipate the result or any constraints, making the description incomplete for safe and correct invocation.
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 already provides complete descriptions for both parameters (framework and screen_type) with enums, giving 100% coverage. The description adds minimal semantic value beyond what the schema indicates — it mentions the concrete frameworks and screen types, but those are already enumerated in the schema. Since the schema covers everything, the baseline is 3, and the description does not exceed it.
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 a specific verb ('Generates'), the resource ('production mobile app screen layouts, navigation stacks, and UI blueprints'), and the target frameworks (React Native/Expo, Flutter). It is distinct from sibling blueprint tools like get_ui_component_blueprint (individual components) and generate_fintech_trading_blueprint (fintech-specific), so an agent can tell them apart.
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 does not mention exclusions, conditions, or refer to sibling tools. An agent must infer from the name that this is for mobile app blueprints, but there is no direction on choosing it over more specialized blueprints (e.g., fintech trading) or UI component blueprints. This is a significant gap.
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 only says the tool 'provides' blueprints, but doesn't describe the output format (e.g., whether it returns code snippets, structured text, or a guide), how the style_framework parameter affects the output, or any potential side effects. The behavior is opaque beyond the high-level promise of 'blueprints'.
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, well-structured sentence. It front-loads the core action ('Provides battle-tested production UI layout blueprints') and then enumerates the supported component types in a clear list. There is no redundant or irrelevant text; every word earns its place.
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 explain what the agent can expect as a result. It mentions 'layout blueprints and CSS/Tailwind component structures' but doesn't specify the form of that output (e.g., detailed code, instructions, numbered steps). It also omits whether the style_framework parameter significantly changes the output. For a tool that returns a blueprint, this lack of output detail means the agent may not know how to handle the response correctly.
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?
Both parameters (component_type and style_framework) are fully described in the schema with 100% coverage. The description adds only marginal contextual value by emphasizing 'production' quality and listing component types that already appear in the enum. It does not explain how the style_framework choice influences the blueprint content, nor provide any additional meaning beyond what the schema already conveys.
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 states a specific verb ('Provides') and resource ('battle-tested production UI layout blueprints and CSS/Tailwind component structures'), followed by an explicit list of component types (Landing Pages, Navbars, Sidebars, Command Palettes, Data Tables, Stat Cards). This clearly distinguishes it from sibling tools like audit_ui_design_aesthetic (which audits rather than provides blueprints) and get_mobile_app_blueprint (which targets a different scope). The purpose is unambiguous and specific.
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 does not mention any conditions, exclusions, or alternative tools, even though sibling tools like get_design_system_recommendation or generate_fintech_trading_blueprint could overlap. Usage is only implied by the purpose, 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 full burden. It says 'inspects' which hints at a read-only operation, but it does not explicitly state whether the tool modifies anything, requires special permissions, or has side effects. It also doesn't reveal how the booleans are used or what the output structure will be, leaving significant behavioral ambiguity.
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 with no fluff. It conveys the core action and output without unnecessary detail. It could be slightly more structured (e.g., breaking out the output), but it is appropriately concise for its purpose.
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?
The description lacks detail on what the exact output looks like (e.g., format of the prioritized list) and how the boolean parameters influence the stage. With no output schema or annotations, the description should provide more context for an agent to interpret results, but it does hint at the outcome (stage + recommendations). It is minimally adequate but not comprehensive.
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 each parameter has a clear description in the schema. The tool description does not add additional meaning about the parameters or how they influence the stage determination. Per rubric, baseline is 3 when schema covers parameters well, and the description adds no 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 inspects project state/dependencies and determines the developer stage with recommended tools and actions. The verb 'identify' and resource 'project stage' are specific. While it doesn't explicitly differentiate from siblings like generate_* blueprints, it is unambiguous enough to distinguish its diagnostic role.
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 purpose implies when to use the tool (when you need to assess project stage), but there is no explicit guidance about when not to use it or alternatives. No mention of alternatives or exclusions, so it only implies usage context.
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 only says the tool 'formulates' prompts, revealing nothing about side effects, persistence, auth requirements, read-only status, or return semantics. Since annotations are absent, the description leaves the safety and mutation profile entirely unspecified, which is a meaningful gap.
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?
A single sentence that front-loads the purpose and packs useful examples without excessive fluff. The parenthetical examples earn their place by signaling the enum domain. It's slightly long but well-organized for a tool with only two parameters.
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 two-parameter tool with full schema coverage, the description covers the core purpose adequately. However, with no output schema and no annotations, it omits what the returned prompt template looks like, whether it returns a single prompt or variations, and any behavioral caveats. Adequate but with clear gaps around output 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?
Schema coverage is 100%, so the schema already documents both parameters. The description adds marginal value by giving example use cases that map to enum values (Code Refactoring → code_refactoring, Bug Fixes → bug_investigation), which helps an agent pick a task_type. Baseline 3 is appropriate since the schema handles the heavy lifting and the description's contribution is modest.
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 states a specific verb ('formulates'), a clear resource ('system prompts and prompt templates'), and gives concrete examples (Code Refactoring, RAG Search, Multi-Agent Orchestration, Bug Fixes) that map to the enum values. It distinguishes itself reasonably from sibling 'generate_*_blueprint' tools, which produce domain plans, though it doesn't explicitly call out the difference. A slight deduction because the examples slightly blur the line with siblings like generate_ai_workflow_orchestration.
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 examples of complex AI agent tasks, which gives an agent a rough sense of applicability. However, it offers no explicit when-to-use vs. when-not-to-use guidance and names no alternative tools, relying on the reader to infer scope. For a tool with 60+ sibling generation tools, explicit routing would materially help.
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 must fully disclose behavior. It lists what is detected but does not state the tool's nature (e.g., read-only, static analysis), what it returns (e.g., a report, list of findings), or any side effects or limitations. The absence of these details leaves significant behavioral ambiguity for an agent invoking the 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, front-loaded sentence that efficiently lists the specific vulnerability classes. It avoids fluff and is easy to parse, though it could be more compact by trimming 'defensive' (redundant with 'security audits').
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 absence of an output schema, the description should explain the return format (e.g., a report, severity levels, recommended fixes) and any constraints (e.g., snippet size, language support). It does neither, leaving the agent without a clear picture of what to expect after invocation. The two parameters are documented, but the tool's output is entirely unspecified.
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%: both 'code_snippet' and 'language_or_framework' have meaningful descriptions. The tool description adds no extra parameter context, but the baseline of 3 applies when the schema carries the burden, which it does here.
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 states a specific verb ('performs'), resource ('defensive security audits on code snippets'), and enumerates concrete vulnerability categories (hardcoded secrets, injection, CORS, sanitization, JWT). It clearly differentiates from audit_performance_bottlenecks by focusing on security rather than performance.
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 analyzing code for security flaws, but it does not explicitly state when to use this tool over alternatives, such as generate_security_hardening or audit_performance_bottlenecks. No exclusions or conditions are given; an agent must infer from the focused scope.
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 only says the tool 'evaluates' a plan, but does not reveal what the evaluation returns (score, feedback, pass/fail), whether it is read-only, has side effects, or requires any prerequisites. The naming of principles adds context but not 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that names the verb, resource, and the specific evaluation criteria without any redundant phrasing or fluff. It is extremely efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, so the description should hint at what the evaluation produces. It mentions the principles but does not explain how they are applied or what the outcome looks like. The optional task_scope parameter is not given extra context beyond the schema. Adequate for basic understanding, but an agent cannot fully predict the tool's output or 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?
Schema coverage is 100%, meaning both parameters (task_scope, plan_description) have basic descriptions in the input schema. The tool description adds no additional meaning about these parameters beyond what the schema already provides. The baseline of 3 is appropriate since the schema handles parameter documentation 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 the tool evaluates a proposed technical implementation plan against three named Karpathy principles (Simplicity, Surgical Scope, Goal Verification). This distinctly separates it from sibling tools like get_karpathy_rules (retrieves rules) and generate_verification_checklist (generates a checklist), making the intent unambiguous.
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 gives no guidance on when to use this tool versus alternatives such as get_karpathy_rules or generate_verification_checklist. It lacks any conditions, exclusions, or context that would help an agent decide between them. The agent must infer usage from the name alone, which is insufficient.
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 that the tool 'generates blueprints' but does not mention whether it is read-only, what the output format looks like, whether it requires any external resources, or any potential side effects. For a generation tool, this lack of behavioral context is a notable gap.
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, dense sentence that front-loads the action ('Generates') and the object, then lists the frameworks. There is no fluff or redundant phrasing, though it could be split into two sentences for slightly better 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?
There is no output schema, so the description should explain what the generated blueprint looks like (format, structure, or usage). It only says 'blueprints' without any detail. Additionally, it does not mention any prerequisites or constraints on the task_description, leaving the tool's full contract vague.
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 covers 100% of the two parameters, both with descriptions and an enum for workflow_type. The description adds marginal value by naming the three frameworks (which already appear in the enum) and the concept of 'complex autonomous pipelines', but does not explain any parameter specifics 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 a specific verb ('generates') and a specific resource ('multi-agent AI workflow orchestration blueprints'), and lists the three frameworks (LangGraph, AutoGen, CrewAI) that define the scope. This distinguishes it from sibling blueprint generators like generate_observability_blueprint or generate_fintech_trading_blueprint, which target different 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 for 'complex autonomous pipelines' and multi-agent tasks, but it does not explicitly state when to use this tool versus alternatives. There are no exclusions or named alternatives, though the specialization is clear enough for an agent to infer the intended use case.
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 behavioral burden. It only says 'generates a blueprint' but does not disclose what the blueprint contains, its format, length, or any side effects. Since there is no output schema, the agent has no idea what the return value will look like. The description mentions the framework's value levers but not the tool's actual behavior or limitations.
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 with minimal waste. It front-loads the tool's purpose and key framework concepts. However, it includes specialized jargon ('Grand Slam', 'Dream Outcome', 'Perceived Likelihood') that may reduce clarity for agents unfamiliar with Hormozi's terminology, so it is concise but slightly dense.
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 there is no output schema and no annotations, the description should explain what the generated blueprint includes, its structure, and how the agent should handle the result. It currently only states the high-level purpose. Missing details like optional parameter behavior (guarantee_type) and expected return format make it insufficient for a tool with moderate complexity.
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 parameters are already well documented. The description adds conceptual context (Dream Outcome, Risk-Reversal) that maps loosely to core_outcome and guarantee_type, but it does not provide additional syntax, enumeration, or formatting details beyond the schema. This meets the baseline for full 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 uses a specific verb ('Generates') with a precise resource ('Alex Hormozi Grand Slam Offer Blueprint') and names the underlying framework ($100M Offers). It clearly distinguishes itself from sibling blueprint generators by referencing Hormozi's specific concepts (Dream Outcome, Perceived Likelihood, Time Delay, Effort), so an agent can tell it apart from generic marketing or pricing blueprint tools.
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 Hormozi-style offer creation but does not explicitly state when to choose this tool over alternatives like generate_pricing_tier_blueprint or generate_marketing_copy_blueprint. There is no exclusions or alternative routing. The agent must infer the appropriate context from the framework mentioned, which is adequate but not explicit.
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 only says 'Generates' — it does not disclose whether this writes files into the user's repo, returns a blueprint document, requires any existing infrastructure, or is a read-only inspection. For a generator tool with zero annotation coverage, the absence of any statement about side effects or output form is a meaningful gap, though the description does not mislead.
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?
A single sentence carrying the full payload with zero filler. The primary action and resource come first, followed by tech specificity and target audience. Every element earns its place and the structure is optimally front-loaded.
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 low-complexity two-parameter tool with complete schema coverage, the description adequately conveys the core purpose. However, there is no output schema and no annotations, so the description should have stated what form the generated blueprint takes (document vs. files vs. code) — it does not. It also leaves the optional monitoring_stack's default unspecified. Adequate but with clear gaps on output and 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?
Schema coverage is 100%, with both parameters described ('Application language target' and 'Target monitoring stack'), so the baseline is 3. The description adds minor value by previewing the enum option values — 'Node.js and Python' maps to the language enum, and 'Grafana/Prometheus' hints at the opentelemetry_grafana stack option. It reinforces but does not materially extend the schema semantics.
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 opens with a specific verb ('Generates') and a precise resource: 'production structured logging, OpenTelemetry tracing, and monitoring dashboards.' It names the concrete tech (Grafana/Prometheus) and the language targets (Node.js and Python microservices), which sets it apart from sibling blueprint generators like generate_security_hardening or generate_docker_setup. Even against the similar-sounding generate_telemetry_funnel_blueprint, the explicit mention of OpenTelemetry + Grafana/Prometheus disambiguates it clearly.
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?
There is no guidance whatsoever about when to invoke this tool versus any of the many siblings. It never says things like 'use this when setting up production APM' or 'for performance investigation use audit_performance_bottlenecks instead.' No exclusions, no preconditions, no routing hints are given, leaving the agent to infer applicability from the name alone.
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 only states that the tool 'generates' test suites but does not clarify side effects (e.g., whether it writes files, returns a plan, or requires existing setup), nor does it mention any limitations, permissions, or integration points. This is a significant gap for a tool that is only one sentence long.
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, well-structured sentence with the action verb first, followed by the resource and supporting details. It contains zero redundancy and every word contributes to understanding the tool's function. This is an exemplary concise structure.
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 tool with only two simple parameters and no output schema, the description is adequate for basic use but incomplete for operational clarity. It does not describe what the generated blueprint looks like, how it is delivered (file, console, in-chat), or any additional context an agent needs to interpret the result. Given the absence of annotations and output schema, more detail would be expected.
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%: both parameters have descriptive text and the enum lists valid framework values. The description itself does not add meaning beyond the schema beyond reiterating the frameworks. Since the schema already carries the semantic load, a baseline of 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 states a specific verb ('Generates') and a clear resource ('production test suites'), enumerating three framework options (Playwright E2E, Vitest, PyTest) and key attributes (mocking, fixtures, assertions). This effectively distinguishes the tool from sibling blueprint generators that target other domains, so purpose is unmistakable.
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 gives no guidance on when to use this tool versus its many siblings, such as generate_verification_checklist or generate_github_actions_workflow. It does not mention prerequisites, typical use cases, or conditions where an alternative would be preferable. The agent is left to infer applicability from the name alone.
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 carry the full burden of behavioral disclosure. The description only states that it generates checklists but does not reveal whether the tool is read-only, whether it has side effects, what output format it produces, or any performance characteristics. Since there are no annotations to compensate, this is a significant gap.
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 directly states the tool's purpose without filler. It is front-loaded with the key action and scope, making it easy for an agent to parse quickly. Every word earns its place.
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?
The tool is simple with only two parameters, one being an enum. No output schema exists, so the description must convey what the checklist includes and in what form. The description mentions 'step-by-step production deployment checklists' but does not specify the structure, content depth, or delivery format. While adequate for a basic understanding, it lacks detail that might be important for an agent to set 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?
Schema description coverage is 100%: both 'framework' and 'target_platform' have clear descriptions in the schema. The tool description adds no additional meaning beyond what the schema already provides. It neither clarifies parameter formats nor introduces new constraints. Given high coverage, the baseline score of 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 tool's function: it generates step-by-step production deployment checklists for specified platforms. It names the specific platforms (Vercel, VPS/Docker, Dokploy/Coolify, AWS), which distinguishes it from sibling tools like generate_docker_setup (which focuses on Docker setup) and diagnose_deployment_issue (which addresses problems). The verb 'generates' and resource 'checklists' are explicit.
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 does not mention conditions, exclusions, or refer to sibling tools. For example, it doesn't indicate that for a Docker-specific setup one should use generate_docker_setup, or that for issue diagnosis one should use diagnose_deployment_issue. The agent is left to infer usage from the name and description alone.
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 present, so the description must carry the full behavioral burden. It uses 'Fetches' to imply a read-only operation, and mentions qualities like 'newly updated' and 'high-star', but does not disclose the freshness threshold, star threshold, or any side effects. It also does not explicitly state that the operation is non-destructive. This is adequate but lacks concrete 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 sentence that gets to the point quickly, with the core action and resource placed up front. The phrase 'to keep the agent's knowledge state-of-the-art' adds some justification but is not strictly necessary. It is concise overall, though the category mismatch could be seen as a minor structural flaw.
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?
The tool has one parameter and no output schema or annotations, so the description is the only source of context. It does not explain how results are returned (e.g., format, pagination, ordering), what makes a repository 'high-star' or 'newly updated', or how the enum values map to the prose categories. An agent calling this tool would not fully know what to expect or how to handle the response, making the description incomplete.
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 already provides 100% coverage, including a description ('Category to check for trending repositories') and an enum. The description does not add any meaning beyond the schema; in fact, it introduces a slight inconsistency between the prose categories and the enum values. Since schema coverage is high, the baseline 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 the tool fetches 'newly updated high-star GitHub repositories', which is a specific action and resource. It also names four content areas. However, the areas listed ('AI, DevOps, Design, and Web Development') do not exactly match the schema enum values ('ai_agents', 'ui_ux', 'devops_infra', 'auth_backend'), causing a slight mismatch that could confuse an agent about what values to pass.
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 gives a generic purpose ('to keep the agent's knowledge state-of-the-art') but does not explicitly state when to use this tool versus alternatives. It does not mention any sibling tools or conditions that would route an agent to a different choice. Usage is implied rather than explicit.
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 carry the full burden. It mentions 'removes dead abstractions' which suggests a destructive action, yet also says 'outputs a roadmap', creating ambiguity about whether the tool modifies the codebase or just analyzes it. This lack of clarity is a significant behavioral gap, especially for a tool that may have destructive side effects.
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 one sentence and reasonably concise, but the opening 'Inspired by ponytail and gstack' adds context that may be unnecessary and could confuse agents unfamiliar with those references. The core functionality is front-loaded, though the sentence could be tightened for better structure.
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?
The tool has one parameter and no output schema, so the description must explain the full behavior. It describes the output (roadmap) but fails to clarify whether it performs destructive actions or simply analyzes. Given the potential for mutations, this ambiguity makes the tool under-specified and risks incorrect agent 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?
Schema description coverage is 100%, so the parameter 'project_description' is already documented as 'Summary of the project or repository file tree'. The description adds no additional parameter guidance, which is acceptable given the schema already provides clear semantics. Baseline of 3 applies.
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 states a specific verb and resource: it analyzes repository structures, identifies duplicates, removes dead abstractions, and outputs a consolidation roadmap. It clearly differentiates from siblings like 'modernize_legacy_code' and 'detect_dead_code_and_bloat' by focusing on holistic codebase consolidation and roadmap output.
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 codebase consolidation but does not explicitly state when to use this tool over alternatives or when not to use it. It lacks guidance on selecting this tool versus related ones like 'detect_dead_code_and_bloat', leaving the agent to infer based on purpose.
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 implies a read-only operation through 'Searches', but doesn't explicitly state that no modifications occur. It also doesn't disclose any specifics about result format, pagination, or rate limits. The description is minimally transparent but doesn't contradict anything.
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 states the essential information with zero filler. The content types are listed right after the verb, making it scannable and easy to parse for an agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter search tool, the description covers the core function well but omits details about the return value or how results are presented. Since there is no output schema, the description should at least hint at what the agent can expect back (e.g., matching memories). This is a notable gap that leaves some ambiguity.
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 schema already documents the single parameter 'query' as 'Search keyword or topic query'. The tool description adds no additional detail about accepted formats or behaviors beyond what the schema provides, so it meets the baseline but doesn't exceed it.
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 a verb ('Searches'), a resource ('learned preferences, mistake autopsies, and scouted project rules'), and the method ('by keyword'). It names three specific memory categories, which distinguishes it from generic memory retrieval tools like get_agent_memory. However, it doesn't explicitly compare it to any sibling, so it lacks strong differentiation.
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 like get_agent_memory or log_user_preference. There is no mention of prerequisites, recommended use cases, or exclusions. The agent must infer usage from the name and description alone.
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 responsibility for behavioral disclosure. It states that it generates prompts but does not describe the output format (single prompt vs. variations), any side effects, determinism, or limitations. For a content-generation tool with no output schema, this is a significant gap that leaves the agent uncertain about the return value.
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, front-loaded sentence with no wasted words. It conveys the action, target models, and examples efficiently. However, the examples (e.g., 'hero visual backgrounds') do not exactly match the enum values (e.g., 'hero_background_glow'), which could cause slight confusion. Still, it is concise and to the point.
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?
The tool is relatively simple (2 flat params, 1 enum), but with no output schema, the description should at least hint at what the generated prompt looks like or how many are returned. It doesn't. For an agent deciding whether to use this tool, the missing output format is a moderate gap, making it adequate but not fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides 100% coverage with descriptions for both parameters, including an enum for asset_type. The tool description adds minimal value beyond the schema—it merely reiterates example asset types in prose without deepening understanding of parameter formatting or constraints. 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 a specific verb ('generates'), a specific resource ('tailored prompts'), and the target models (Midjourney / DALL-E / Flux). It lists concrete asset types (hero visual backgrounds, 3D app mockups, glassmorphic icons) and audience (web/mobile apps), which distinguishes it from sibling blueprint generators that produce text/copy. The purpose is unmistakable.
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 when to use this tool (when you need image prompts for visual assets) but does not explicitly state when it is better than alternatives or when not to use it. There is no mention of exclusions or alternative tools, leaving an agent to infer usage from the tool name and description. This is adequate but not explicit.
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 states the tool 'generates' strategies, which implies a read-only generation with no side effects, but nothing beyond that. It does not disclose return format, data sources, or any potential limitations. For a simple generation tool, the lack of negative information is acceptable but not thorough.
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 is front-loaded with the verb and lists the three output types. It is concise, with no filler, and every word contributes to explaining the tool's purpose. Slightly long but still 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 tool's simplicity (one parameter, no nested objects, no output schema), the description is adequate to understand what it produces. However, it lacks details on the output structure or format, and does not clarify how it relates to sibling SEO tools. For an agent deciding between this and other SEO generators, the description alone may be insufficient.
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% for the single parameter `domain_niche`, with a clear description of expected values. The tool description adds no extra semantics about the parameter beyond what the schema provides. Since the schema already documents it well, the baseline of 3 applies.
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 three specific things: programmatic SEO keyword strategies, backlink outreach blueprints, and Google Trends traffic expansion plans. The verb 'generates' plus these resources makes the purpose specific. However, it does not explicitly distinguish from sibling tools like generate_seo_metadata_blueprint or generate_seo_aeo_geo_blueprint, so it lacks explicit differentiation.
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?
Usage is implied by the output list: if an agent needs keyword strategies, backlink blueprints, or traffic expansion plans, this is the tool. But no explicit conditions, when-to-use vs. alternatives, or exclusions are provided. The absence of any guidance on choosing it over closely related SEO siblings leaves the agent to infer.
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 what it generates but does not mention whether it produces code, a plan, or a document; whether it requires any inputs beyond target_layer; or whether it has side effects. The description does not contradict annotations (there are none) but leaves important behavioral details unspecified.
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 with no filler or repetition. It lists the three distinct outputs efficiently and avoids any extraneous detail. Every word contributes to conveying the tool's purpose.
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 tool with only one parameter and no output schema, the description provides adequate context to understand its function and scope. It enumerates the three target layers, which align with the enum values, and is concise. The main gap is the lack of any statement about the output format or expected response, but given the low complexity, the description is largely complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the single parameter target_layer is fully documented with an enum and a per-value description. The tool description implicitly maps its three listed artifacts to the enum options, which adds slight conceptual context, but it does not provide additional syntax, format, or usage details beyond what the schema already offers. This aligns with the baseline of 3 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 uses a specific verb ('Generates') and lists three concrete artifacts (Redis caching layers, CDN stale-while-revalidate headers, database indexing strategies). This clearly defines the tool's scope and differentiates it from sibling blueprint generators that target other domains like observability or auth.
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 does not mention any conditions, prerequisites, or exclusions, and does not reference sibling tools such as audit_performance_bottlenecks or generate_observability_blueprint that might overlap. The agent must infer usage solely from the name and artifact list.
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 states the output content, not safety, permissions, side effects, or response format. For a generation tool, it doesn't disclose whether it mutates state or has any rate limits or dependencies.
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?
One tight sentence with no filler. The central purpose is front-loaded, and the detail about APIs and polling is succinct. Structure is adequate.
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 one-parameter tool with no output schema, the description covers the main functionality but omits output format or expected structure. It is minimal but not egregiously incomplete; an agent can infer the tool creates some sort of blueprint document.
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 provides 100% description coverage, giving the baseline of 3. The description does not elaborate on how the trading_style enum values map to distinct outputs; it only mentions crypto/fintech trading loops, which aligns with one enum value but leaves others unexplained. No added 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 generates quantitative crypto/fintech trading execution loops, naming specific components (risk locks, order execution APIs, market data polling). This distinguishes it from sister blueprint generation tools by its domain-specific focus.
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 when to use it (whenever a trading blueprint is needed) but provides no explicit guidance on when not to use it or which alternative blueprint generator to choose instead. It lacks exclusions or comparisons to siblings.
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 only states what the tool generates, without describing the output format (plain text, structured JSON, multiple variants?), any requirements (e.g., API keys, quality thresholds), or limitations. It also doesn't clarify whether it returns a single string or a list of options. This is a significant gap for a generation tool where output structure is crucial for downstream use.
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 action and lists concrete deliverables. There is no fluff or redundancy. Every word contributes to defining the tool's scope, making it both concise and structurally sound.
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 has four parameters, no output schema, and no annotations, the description is insufficiently complete. It doesn't specify the return format, whether the output is tailored to the target_audience, or whether it produces one headline or multiple variants. An agent calling this tool would lack critical expectations about the response, making integration error-prone. The description covers the what but not the how or the outcome shape.
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%, so each parameter has a description. The description adds context by enumerating the output types that correspond to the copy_type enum, giving agents a hint of what to expect. However, it doesn't explain how parameters like target_audience influence the copy, nor does it provide examples or relationships between parameters. Since the schema already documents the parameters, the description only marginally adds value, keeping it at baseline.
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 'Generates' and the resource 'high-converting SaaS hero headlines, feature value propositions, microcopy CTAs, and Product Hunt / Show HN launch copy'—listing specific deliverables that map directly to the copy_type enum. This makes the tool's purpose unmistakable and distinguishes it from sibling blueprint generators like generate_ad_copy_blueprint or generate_hormozi_offer_blueprint, which target different marketing artifacts.
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 implied usage is clear: use this tool when you need SaaS marketing copy in the specified formats. However, there is no explicit guidance on when *not* to use it, nor any mention of alternative tools for other copy types (e.g., ad copy, offers). The description gives no decision criteria to help an agent choose between this and similar generators, so it relies on the agent to infer context.
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 fully carries the behavioral transparency burden. It does not disclose any side effects, prerequisites, or output format. Crucially, it doesn't clarify whether the tool returns the generated artifacts directly or writes them to a file, nor does it mention any limitations (e.g., requires existing project structure). For a generation tool, these details are important for an agent to avoid incorrect usage.
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, well-structured sentence that front-loads the key outputs (Zod schemas, TypeScript types, OpenAPI specs) before stating the purpose. Every word contributes to clarity, with zero redundancy or filler. It is optimally concise.
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 absence of an output schema and annotations, the description should explain what the tool returns or how it affects the environment. It only states what it generates, leaving the agent uncertain about the tool's behavior — for example, whether it returns code snippets, creates files, or requires specific project settings. This is a significant gap for a tool that expects to be invoked in a codebase.
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 input schema already describes both parameters with clear examples and descriptions, achieving 100% schema coverage. The description adds no additional semantic detail about the parameters, such as constraints, inter-parameter relationships, or formatting rules. Since the schema does the heavy lifting, a baseline score of 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 a specific verb 'Generates' and lists three concrete resources: Zod validation schemas, TypeScript contract types, and OpenAPI v3 specs. It also names the purpose 'frontend-backend type alignment,' making it distinct from sibling blueprint generators which target different domains. No ambiguity.
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 when to use this tool through its stated purpose (when type alignment is needed). However, it provides no explicit guidance on when not to use it, nor does it mention any alternative tools or exclusion criteria. The use case is clear but without direct comparison to siblings, so it's implied rather than explicit.
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?
Annotations are absent, so the description carries the full burden of behavioral disclosure. It states the tool 'transforms' code, implying mutation, but does not clarify whether it returns the modernized snippet, writes to a file, or modifies input in place. It also omits side effects, error conditions, and limitations. This lack of transparency is a significant gap for a tool with no annotation support.
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, well-structured sentence that front-loads the core action ('Transforms legacy code snippets') and packs additional detail into a parenthetical list. It is concise, efficient, and every element contributes to understanding. There is no redundant or filler content.
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?
The tool has no output schema and no annotations, so the description must convey the full context. It explains what transformations are performed but omits critical details: the return value (does it return the modernized code string?), possible errors (e.g., invalid input), and limitations (e.g., might not handle all modern features). Given the moderate complexity (2 params, enums), the description is incomplete and leaves the agent uncertain about post-call behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already provides 100% coverage with descriptions for both parameters. The description adds value by enumerating specific transformation mappings (e.g., 'CommonJS require -> ESM imports', 'Class components -> React Hooks') that clarify what each target_language option implies. This helps an agent select the correct target based on the legacy code's characteristics, going beyond the schema's generic 'Target modernization stack' label.
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 states a specific verb ("Transforms"), a specific resource ("legacy code snippets"), and enumerates concrete transformations (CommonJS->ESM, Class->Hooks, etc.). This clearly distinguishes it from siblings like 'ponytail_codebase_modernizer', which appear to target full codebases rather than snippets. The purpose is unambiguous.
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?
There is no guidance on when to use this tool versus alternatives. It does not mention that for full-codebase modernization one might use 'ponytail_codebase_modernizer', nor does it state any prerequisites, exclusions, or specific scenarios. The agent must infer that it is intended for snippets, but no explicit usage direction is provided.
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 does disclose important aspects like non-root execution, healthchecks, and SSL integration, which signal production-readiness and security considerations. However, it does not specify what the tool actually returns (e.g., file contents as strings, paths, or a zip bundle), nor does it mention whether it writes files directly or if any side effects occur. This ambiguity could confuse an agent about the expected output.
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, compact sentence that front-loads the primary outputs (Dockerfile, docker-compose.yml, Nginx configs) and then adds key features. It is concise without unnecessary filler. Slight improvement could be made by separating the list of features, but the current structure is efficient and easy to parse.
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 generating multiple files and the absence of an output schema, the description is notably incomplete. It does not clarify the format of the tool's result—whether it returns the file contents, suggests filenames, or provides a download path. It also does not mention that app_type is a required parameter or that reverse_proxy has a default value, though these are in the schema. The lack of return-value information is a significant gap for an agent deciding how to handle the 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 baseline is 3. The description mentions 'db/redis integration', which loosely maps to includes_db and includes_redis, but the schema already documents these parameters clearly. The description does not add meaningful detail about parameter usage, such as the effect of app_type on the generated Dockerfile or the default for reverse_proxy, leaving the schema to carry the semantic load.
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 verb 'Generates' and the resources: production-ready Dockerfile, docker-compose.yml, and Nginx reverse proxy configs with SSL. It details features like multi-stage, non-root execution, healthchecks, and db/redis integration, making it easy to distinguish from sibling tools that focus on other aspects like deployment checklists or CI workflows.
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 that the tool is used for generating Docker setup artifacts, but it does not explicitly mention when to use it versus alternatives. Since siblings include get_deployment_checklist and generate_github_actions_workflow, which could overlap in deployment contexts, the lack of explicit guidance on when to choose this tool over others is a gap. No exclusion criteria or alternative references are provided.
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 only states that the tool 'Generates' pipelines, implying it produces a blueprint or plan, but does not disclose output characteristics, constraints, side effects, or whether it is read-only. There is a minor implicit expectation that it yields a design artifact, but details are missing.
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?
A single sentence that directly conveys the tool's purpose and enumerates the supported modalities. There is no filler, and the core information is front-loaded. It is appropriately concise for the simple one-parameter schema.
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 tool with one enum parameter and no output schema, the description is sufficient to enable a basic call. However, it omits any context about what the generated blueprint looks like (e.g., format, sections, level of detail) and does not hint at prerequisites or typical use cases. Given the lack of annotations and output schema, the description could have provided a bit more 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% (the media_type parameter is fully described with an enum). The description adds the specific pipeline names, but these already appear in the enum values. It doesn't enrich the meaning beyond what the schema provides, so it meets the baseline 3 but adds no extra value.
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 states a clear verb ('Generates') and a specific resource ('production Multimodal AI pipelines'), and enumerates the three concrete pipeline types (Whisper audio transcription, Vision LLM image analysis, Synthetic Data generation). This distinguishes it from sibling blueprint generators like generate_fintech_trading_blueprint or generate_observability_blueprint, making the tool's scope unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for multimodal AI pipeline generation, and the enum values give concrete use cases. However, it provides no explicit guidance on when to choose this tool over analogous siblings such as generate_ai_workflow_orchestration or generate_document_processing_pipeline, nor does it state any exclusions or conditions. An agent can infer the domain but not the precise selection criteria.
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 does not mention whether the tool is read-only, any permissions required, reversibility, or side effects. It only states the generated content, leaving safety and operational behavior 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 main purpose and lists four specific deliverables. No redundant or filler content exists.
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?
The description lists the core deliverables but lacks details about output format (e.g., Markdown, JSON), response structure, or any prerequisites. Since there is no output schema, some additional context would be helpful, though the description is adequate for a straightforward content generation tool.
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 three parameters (company_or_app_name, uses_ai_models, collects_user_data) already have clear descriptions. The tool description does not add any additional meaning, such as how parameters influence the output, beyond what the schema 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?
The description clearly states the tool generates GDPR/CCPA compliant Privacy Policy outlines, Terms of Service templates, AI data usage disclaimers, and cookie consent configurations. It uses a specific verb ('Generates') and names concrete deliverables, distinguishing it from sibling blueprint tools (e.g., generate_fintech_trading_blueprint).
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 legal/privacy documents are needed, but provides no explicit guidance on when to choose this tool versus alternatives, nor any exclusions. Among many similar 'generate_*_blueprint' siblings, it is clear based on subject matter, but the guidance is not spelled out.
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 disclosure. It only says 'Calculates' and lists outputs, but does not mention whether the operation is read-only, if there are side effects, or what the return format is. For a calculation tool, this is minimal behavioral transparency; an agent would have to infer safety and output structure.
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?
A single, front-loaded sentence that names the verb and resource immediately, then lists specific outputs. There is zero fluff or repetition, making it optimally concise and scannable.
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?
The tool has no output schema and only three parameters, all documented. The description lists outputs but does not specify the format of the result (e.g., a single number, a report, or a breakdown). For a simple calculation tool, this is a moderate gap—an agent would not know exactly what to expect back, though the inputs are clear.
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 each parameter is documented in the schema. The description adds context by listing metrics (e.g., LTV/CAC ratio) that imply how parameters are used, but does not directly explain parameter relationships or units beyond what the schema provides. This meets the baseline but does not significantly enrich 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 states a specific verb 'Calculates' and resource 'SaaS unit economics metrics', listing concrete metrics (LTV/CAC ratio, payback period, etc.). This clearly distinguishes it from sibling tools that generate blueprints or recommendations, leaving no ambiguity about its function.
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, but the purpose is narrow and obvious (calculating unit economics). No alternative calculation tools are in the sibling list, so usage context is implied rather than explicitly guided. A clear 'use when' statement or exclusion of non-applicable cases is missing.
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?
Since no annotations are provided, the description carries the full burden of behavioral disclosure. It does reveal some behavior: the tool generates scripts that employ stealth tactics (user-agent spoofing, headless browser evasion) and produce structured JSON output. However, it does not disclose other behavioral aspects such as whether the tool actually executes the scraping, any rate-limiting or anti-detection caveats, or what the generated script does beyond the stated techniques. This is a moderate level of transparency but 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, dense sentence of about 25 words. It front-loads the primary action ('Generates production stealth web scraping scripts') and immediately follows with specific technical details. There is no filler or redundant wording; every phrase adds value, making it highly efficient and appropriately sized.
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 tool with only two parameters and no output schema, the description gives a fair overview of what it accomplishes (generating scraping scripts) but lacks details about the output format (e.g., whether the script is returned as text, a file, or a structured object) and potential limitations (e.g., handling of authentication, captchas, or rate limits). Since there is no output schema, the description should offer more context about what the agent can expect as a result, but it falls short of being 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 description coverage is 100%, with both target_url and extraction_type having descriptions. The tool description adds no parameter-specific information, so it relies entirely on the schema. While it does give context about the overall approach (e.g., Playwright/Puppeteer), it does not clarify the meaning of the extraction_type enum values (e.g., when to choose 'static_cheerio' vs 'dynamic_spa_playwright'). The description does not exceed the baseline expectation for a fully documented schema, hence a 3.
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 starts with the verb 'Generates' and the specific resource 'production stealth web scraping scripts', then details the technical approach (Playwright/Puppeteer, user-agent spoofing, headless browser evasion, structured JSON parsing). This clearly distinguishes the tool from sibling blueprint generators like generate_fintech_trading_blueprint, which focus on other domains. The 'stealth' keyword and library names leave no ambiguity about what this tool produces.
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 other blueprint generators, nor does it mention conditions or scenarios where it is appropriate or inappropriate. It does not reference sibling tools or explain how the extraction_type choices map to specific use cases. The only implied context is that it is for web scraping, but no explicit usage rules or alternatives are 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'real-time' and 'directly inside the IDE chat window' but fails to state whether the operation is read-only, what data it accesses or displays, whether it triggers side effects, or what the output looks like. This is insufficient for an unannotated 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, tightly worded sentence that communicates the core action, subject, and location with no redundancy. It is front-loaded with the main purpose and avoids unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter dashboard render, the description gives the basic function but leaves significant ambiguity. It does not clarify what data the dashboard includes, how 'Gold-Level Agent Intelligence' is defined, whether user interaction is required, or any side effects. Without annotations or an output schema, an agent may not know what to expect from the invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4. The description correctly omits any parameter details since there are none, and the empty schema already covers the situation. No additional semantic information is needed or provided.
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 states a specific verb ('Renders') and object ('real-time Gold-Level Agent Intelligence & Memory Dashboard'), and specifies the location ('inside the IDE chat window'). It clearly distinguishes this tool from sibling memory query tools like 'get_agent_memory' and 'search_agent_memory' by focusing on a dashboard presentation rather than data retrieval.
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, no preconditions, and no mention of exclusions. An agent receives no context about the intended use case or how it differs from other memory-related tools beyond the inherent 'dashboard' nature, which is implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the core action (syncing to a global GitHub repo) and mentions the anonymization parameter, but it does not explicitly warn about potential data exposure, irreversibility, authentication requirements, or whether it also pulls from the remote. It implies a one-way push ('so new developers automatically benefit') but leaves ambiguity about bidirectional sync, and does not disclose any side effects beyond the act of syncing.
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 front-loads the action and purpose without any filler. Every word is useful, and it is appropriately sized for a simple tool with one parameter. It earns its place with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations and no output schema, the description is moderately complete: it explains the core purpose and implies external write action. However, it lacks important operational context such as prerequisites (e.g., existence of local autopsies), potential side effects (data becomes public), return value on success/failure, and any authentication or network dependencies. These gaps are significant for a tool that pushes data to a public repository.
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 input schema already documents the sole parameter 'auto_anonymize' with a clear description (e.g., stripping private keys, URLs, personal identifiers) and default true. Since schema coverage is 100%, the description adds no additional meaning about the parameter, which is acceptable per the baseline. The description does not need to repeat parameter details when the schema already covers them.
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 ('Syncs'), the resource being synced ('local learned mistake autopsies and best practices'), and the target ('global community GitHub repository'), making the purpose specific and distinguishable from sibling tools like log_mistake_autopsy (local logging) and get_community_wisdom_report (pulling wisdom). It fully conveys what the tool does.
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, such as after logging mistakes or before generating reports. It only implies the purpose of sharing for community benefit, but does not state prerequisites, exclusions, or conditions that select this tool over others. For example, it does not clarify whether it should be used only after local autopsies exist or how it relates to other memory operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It does list what kinds of performance traps it checks, which adds specific value beyond the generic name. However, it does not state whether the operation is read-only, whether it has side effects, what the output format will be, or any prerequisites. For an analysis tool, these are important but not fully disclosed.
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, efficient and front-loaded with the core action ('Analyzes code for performance traps'). The list of specific examples adds value without excessive verbosity. No redundant phrases or filler. While the list is somewhat detailed, it remains concise and informative.
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?
The description explains the tool's core function and scope, listing several concrete performance issues it identifies. However, since there is no output schema, the description does not explain what the tool returns (e.g., a list of findings, severity scores, or suggestions). It also omits any constraints on input size or format beyond the parameter name. These gaps are notable but not critical for a simple analysis tool.
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 input schema already provides a complete description of the 'code_snippet' parameter ('Code snippet or query to audit for performance bottlenecks'), covering 100% of parameter semantics. The tool description adds minimal extra meaning—it mentions 'code' and 'SQL queries' but essentially reaffirms what the schema already states. Thus, 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 verb 'Analyzes' and the resource 'code for performance traps', with specific examples (React re-render cascades, unindexed SQL queries, N+1 problems, heavy bundle imports, missing image optimization). This distinctly separates it from sibling audit tools like audit_security_vulnerabilities or audit_ui_design_aesthetic, making its purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies that this tool is for performance bottleneck analysis, but it does not explicitly state when to use it versus other audit tools or provide any exclusions. No mention of alternative tools or when not to use it. The usage is inferred from the tool name and description, not explicitly guided.
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 must carry the full behavioral burden. It states it generates snippets, but does not disclose the output format (e.g., list vs. single string), whether it is purely advisory or writes files, or any side effects. It also fails to mention any prerequisites or error conditions, leaving significant behavioral ambiguity 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, information-dense sentence. It front-loads the primary action and enumerates examples without waste, making it highly efficient and scannable.
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 parameter and no output schema, the description covers the core purpose and the parameter's role. It mentions the types of snippets generated, which is adequate for most invocation scenarios. However, it omits details about the exact shape of the return value (e.g., array of snippets vs. combined code blocks) and any limitations, so it is not fully complete, but sufficient for a straightforward generation tool.
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 describes target_stack with a clear enum and description, so coverage is 100%. The tool description adds no additional parameter semantics beyond what the schema already conveys; the baseline of 3 applies because 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 states a specific verb (generates), a clear resource (production security hardening code snippets), and enumerates concrete security measures (CSP headers, rate limiting, JWT rotation, input sanitization). This clearly distinguishes it from sibling tools like audit_security_vulnerabilities (audit vs. generate) and other blueprint generators focused on different 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 when security hardening code is needed for a specific stack, but does not explicitly state when to use it over alternatives like audit_security_vulnerabilities or other generation tools. It lacks exclusions or alternative conditions, leaving it to inference.
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 uses the non-mutating verb 'retrieves,' which implies a read-only operation, but it does not explicitly state that it has no side effects, nor does it mention any authentication or rate-limit considerations. The description is adequate but not fully transparent about behavior beyond the basic retrieval.
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, well-structured sentence that front-loads the core action and immediately lists the relevant categories. No filler or repetition; every word contributes to understanding the tool's function.
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 tool with one optional parameter and no output schema, the description covers the essential purpose and the available categories. It does not describe what the response looks like, but the absence of an output schema and the simplicity of the operation make this a minor gap. The description is sufficiently complete for an agent to invoke it correctly.
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 the parameter 'filter_category' described as 'Category of memory to retrieve' and an enum listing the same categories mentioned in the description. The description adds the purpose ('to guide coding decisions') but no additional per-parameter details, so it provides minimal value 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?
The description states a clear verb (retrieves) and resource (agent's persistent memory) and enumerates the memory categories (user preferences, mistake autopsies, project rules), making the primary function obvious. However, it does not explicitly distinguish this from the sibling search_agent_memory, leaving some ambiguity about when to use one over the other.
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 purpose implies usage (retrieve memory to guide coding decisions) but gives no explicit guidance on when to prefer this over search_agent_memory or the logging/sync tools. There are no exclusions or alternative suggestions, leaving the agent to infer the appropriate context from the description alone.
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. 'Returns' implies a read-only operation with no side effects, but it does not elaborate on any other behavior such as pagination, caching, or determinism. For a simple retrieval tool, this is acceptable 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, tightly worded sentence with no filler. It front-loads the core action and purpose, and every phrase contributes to understanding the tool's function. Perfectly concise.
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 retrieval tool with one optional parameter and no output schema, the description is largely complete. It states what is returned and why, and the schema covers the parameter. It could hint at the format of the returned rules (e.g., list vs. text), but that is a minor gap given the tool's simplicity.
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 parameter 'category' and its enum values are fully described in the input schema. The description adds no additional meaning about the parameter, such as indicating which categories are most common or how they map to the rules. Baseline 3 is appropriate since the schema handles parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Returns') and resource ('Andrej Karpathy's core AI coding rules and guidelines'), and clarifies the purpose (prevent over-engineering, bad assumptions, and collateral edits). This clearly distinguishes it from sibling tools that generate blueprints or audits.
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 does not mention any conditions or scenarios that would select this over the many sibling generation/audit tools, nor does it exclude any use cases. The agent must infer when retrieving coding rules is 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 disclosing behavioral traits. It only states 'Diagnoses' without indicating whether the tool is read-only, what side effects might occur, what the output format is, or any prerequisites (e.g., must provide a sufficiently detailed log). The description does not add meaningful behavioral context beyond the verb itself.
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 immediately defines the verb and the scope. It lists specific issue types without any filler or repetition. This is both efficient 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?
The description gives enough to understand the tool's purpose, but because there is no output schema, it does not hint at the return value or diagnostic format. It also does not mention any constraints like the need for specific error log content. For a diagnostic tool that likely returns analysis/fixes, this is a notable gap, though the problem areas are clearly enumerated.
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 input schema already provides descriptions for both parameters (100% coverage), so the description adds no extra meaning about them. It does list the types of issues, which loosely ties to the error_log content, but it does not elaborate on parameter format, importance, or interplay. Baseline of 3 is appropriate given full 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 states a specific action ('Diagnoses') and clearly enumerates the distinct problem categories it covers (deployment failures, container logs, CORS issues, ISR cache limits, build errors, memory leaks). This makes it immediately distinguishable from siblings like 'generate_docker_setup' or 'get_deployment_checklist', which serve different purposes. The specificity leaves no ambiguity about what the tool does.
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?
While the description does not explicitly state 'use this when you have X' or mention alternatives, the enumerate list of diagnosis targets provides clear context. An agent can infer that this tool is appropriate when encountering deployment or runtime issues. However, it lacks explicit 'when-not-to-use' guidance or references to sibling tools, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the catalog is '100% free' and 'zero credit card required', but does not state that it is read-only, describe the response format, or mention any limitations like pagination. For a simple catalog tool, this is 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence. It front-loads the core purpose ('Provides a searchable catalog') and then lists relevant categories. Every phrase adds value, with no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema, and the description does not explain what the response looks like (e.g., a list of API names, detailed objects, or how many results). It also does not mention pagination or result limits. Given the tool's low complexity, this is a minor gap but still leaves the agent uncertain about the return structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% coverage for the single parameter, describing it as 'API category filter' with enums. The description adds some context by mapping categories (e.g., Finance to finance_crypto, Weather to weather_environment), but this is marginal since the schema already enumerates options. 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 states a specific verb and resource: 'Provides a searchable catalog of top 100% free public APIs and developer services' and even lists example categories (Finance, Geolocation, Weather). It clearly distinguishes from sibling blueprint-generation tools by focusing on discovery rather than generation.
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 purpose implies when to use it (when seeking free APIs), but there is no explicit guidance on when to use it versus alternatives, nor any exclusions. The description does not name sibling tools or conditions for alternative selection, leaving usage to inference.
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 explains what the generated workflow does (receives issues, runs diagnosis, generates fixes, submits PRs) but does not disclose the tool's own behavior such as output format (e.g., YAML content) or any prerequisites (e.g., repo access).
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 primary action ('Generates an autonomous GitHub Action workflow') and efficiently lists the workflow's key capabilities without redundancy.
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 tool with one parameter and no output schema, the description sufficiently explains the purpose and expected behavior of the generated workflow. Minor gaps include not specifying the exact output format or any required permissions, but these are not critical for agent invocation.
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 description does not add any additional meaning beyond the schema's explanation of repo_name. The parameter is self-explanatory, so the baseline score of 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 tool generates an autonomous GitHub Action workflow that handles issue-driven PR generation. It specifies the exact resource (workflow) and its behavior, distinguishing it from sibling tools like generate_github_actions_workflow which is more generic.
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 a specific use case (autonomous PR fixing from issues) but does not explicitly state when to use this tool versus alternatives. There is no mention of conditions or exclusions, leaving the agent to infer based on the tool's name and description.
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 the full burden. It discloses that the tool generates pricing tiers, gating, and billing strategies, which informs the agent of the output nature. However, it does not mention any side effects, limitations, or specifics about the return format. For a generation tool this is adequate but not rich; it doesn't add much beyond the basic output concept.
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 verb and resource, then lists the key components. There is zero waste—every element (tiers, gating, billing) is meaningful and directly relevant to the tool's purpose. It is appropriately sized and immediately scannable.
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 generation tool with three well-described parameters and no output schema, the description covers the essential elements: what it produces and what it includes. It could elaborate on the inputs or the meaning of 'optimal', but given the clarity of the tool's purpose and the parameter coverage, it is sufficiently complete for an agent to call it correctly.
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%—all three parameters have explicit descriptions in the schema. The tool description adds no additional parameter semantics; it only mentions the output. Per the rubric, baseline for high coverage is 3, so this is correct.
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 states a specific action ('Generates'), a concrete resource ('optimal SaaS pricing tiers'), and lists key outputs (Free/Starter, Pro/Growth, Enterprise tiers, feature gating logic, billing strategies). It clearly distinguishes this tool from the many sibling blueprint generators (e.g., generate_ad_copy_blueprint, generate_observability_blueprint) by naming the exact domain and deliverables.
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?
There is no explicit statement of when to use this tool versus alternatives, nor any exclusions. Usage is implied by the tool's name and description—it is obviously meant for pricing-related requests—but no direct guidance is given. This falls into the 'implied usage' category, not full clarity.
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 states what it generates (blueprints with SEO, AEO, GEO), implying a read-only generation operation with no side effects. However, it does not explicitly confirm that no files are written or that it purely returns output, nor does it mention any prerequisites, permissions, or edge cases. Compared to the update_drive example (which received 2 for lacking these disclosures), this tool is safer in nature, but the description still leaves behavioral details unstated.
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, well-structured sentence that front-loads the main purpose ('Generates complete Search Optimization blueprints') and then concisely lists the three components. There is zero wasted wording, and the information density is high. It is both concise and informative, setting a strong example of efficient description writing.
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 generation tool with 4 parameters (2 required) and no output schema, the description covers the essential purpose and output components. It doesn't describe the exact return format, but that is not required since no output schema exists. Given the large set of sibling tools, it could be more explicit about when to select this over related tools, but the purpose is clear enough that an agent can infer when a complete SEO/AEO/GEO blueprint is needed. Minor gap: no mention of what 'blueprint' entails in terms of deliverable 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 the input schema already documents each parameter clearly. The description does not add additional meaning beyond what the schema provides—it doesn't explain how target_keywords maps to the SEO/AEO/GEO generation or how canonical_url influences the output. With full schema coverage, the baseline of 3 applies, and no extra value is added 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 uses a specific verb 'Generates' and a specific resource 'Search Optimization blueprints', then explicitly enumerates three concrete components: Traditional SEO meta tags, AEO direct-answer snippets, and GEO structured entity markdown. This clearly distinguishes it from sibling tools like generate_seo_metadata_blueprint (which likely covers only SEO tags) and audit_seo_aeo_geo_readiness (an audit tool). The purpose is unambiguous and complete.
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 this tool is for generating a complete blueprint covering all three optimization types, but it does not explicitly state when to use this versus alternatives such as generate_seo_metadata_blueprint or audit_seo_aeo_geo_readiness. There is no mention of exclusions, prerequisites, or routing guidance. The usage is inferred from the word 'complete', but no explicit 'when to use' or 'when not to use' instructions are given.
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 clearly states that it 'generates' schemas, which implies a non-destructive, likely read-only operation. However, it does not specify any side effects (e.g., whether it writes files, calls external APIs, or has rate limits) or confirm that it is safe to call repeatedly. For a generation tool this is generally acceptable, but the lack of explicit non-mutating language leaves some ambiguity.
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, densely packed sentence that front-loads the core action and resource. It avoids fluff and effectively conveys the essential information. It could be slightly more structured (e.g., separating use cases), but it is appropriately concise and highly readable.
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?
There is no output schema, so the description should clarify what the tool returns (e.g., format of the generated schemas). It does not state whether the output is JSON, code snippets, or a written document, nor does it mention any limitations or edge cases. Given the tool's low complexity (2 params) and clear purpose, this is a noticeable gap but not critical for selection. An agent could call it and discover the format, but it is incomplete.
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 covers 100% of parameters with descriptions for both app_name and analytics_provider. The description adds context about the purpose of the generated schemas but does not enhance understanding of what the parameters control beyond the schema's own descriptions. Per the rubric, with full schema coverage the baseline is 3, and the description does not elevate beyond that.
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 states a clear verb ('generates') and resource ('product event telemetry schemas') and explicitly lists the target providers (PostHog, Mixpanel, Plausible) and use cases (signup conversion, onboarding milestones, feature usage, churn signals). This distinguishes it from sibling tools like generate_observability_blueprint, which focus on broader observability rather than product event telemetry. The purpose is unambiguous.
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 strong context on when to use the tool by enumerating the specific tracking scenarios it handles. It does not explicitly compare to alternatives or state when not to use it, but the use-case clarity allows an agent to deduce when this tool applies relative to others. No exclusions are mentioned, so it falls short of an explicit when/when-not directive.
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, the description carries the full burden and it discloses the specific behaviors (scanning for Schema.org tags, broken link risks, citation gaps), which goes beyond a generic 'audits' statement. It implies a read-only audit operation, though it does not explicitly state the absence of side effects or describe the output format, a minor gap at this complexity level.
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 that immediately states the action and resource, then lists specific checks. Every word contributes to understanding the tool's scope, with no redundancy or filler.
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?
The description covers the purpose and the nature of the checks, which is sufficient for a simple one-parameter tool without an output schema. While it does not explicitly describe the return format, it implies a report of findings (missing tags, broken link risks, citation gaps), which is adequate for an agent to call it correctly.
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 already provides 100% coverage for the single parameter 'code_or_markdown' with a clear description. The tool description adds context about what the audit checks, but it does not enrich the parameter's meaning beyond the schema; the baseline for full schema coverage is 3, and the description does not add syntax or formatting details.
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 audits HTML/Markdown code for SEO, AEO, and GEO compliance, listing specific checks like missing Schema.org tags, broken link risks, and citation gaps. This distinguishes it from sibling audit tools (e.g., audit_performance_bottlenecks, audit_security_vulnerabilities) by its specific focus on search and AI-readiness.
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 you need to assess code for SEO/AEO/GEO compliance, but it does not explicitly mention when to use this tool versus alternatives like generate_seo_aeo_geo_blueprint or other audit tools. There is no 'when not to use' or reference to competing tools, leaving the routing to inference.
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 exist, so the description carries the full burden. It discloses that the tool rejects specific anti-patterns and returns polish steps, but does not mention whether the operation is read-only, any side effects, input constraints, or performance implications. 'Audits' implies read-only, yet deeper behavioral context is missing.
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 verb and resource, then lists specific anti-patterns and the output type. It is efficient, information-dense, and contains no filler words.
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 tool with one parameter and no output schema, the description and schema together give an agent enough to call it correctly. It explains the input, the checks performed, and the output type. It could optionally reference sibling tools for performance or security to fully prevent mis-selection, but this is not critical for a simple audit tool.
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 already describes the code_snippet parameter as 'The HTML/CSS/React code to audit for visual aesthetic and usability', covering 100% of the parameter meaning. The description adds no extra detail about the parameter's format, scope, or limitations beyond what the schema 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?
The description clearly states the tool audits HTML/CSS code, specifies concrete anti-patterns (plain browser defaults, raw unadjusted colors, missing active/focus states, poor contrast), and declares the output as 'exact polish steps'. This distinguishes it from sibling audit tools that target performance, security, or SEO, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description makes obvious that this tool is for UI design auditing of code snippets, giving an agent enough context to select it over other audit tools. However, it does not explicitly mention when not to use it or point to alternatives for performance or security audits, so it falls short of a 5.
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 must disclose behavior itself. It clearly states it writes to agent memory (a mutation) and the purpose of that write. It does not explain any side effects like overwriting existing entries, idempotency, or storage limits, but the scope is narrow and the description covers the essential behavior without contradicting any structured metadata.
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, compact sentence that front-loads the action, resource, and rationale. There is no filler or redundancy; every word contributes to understanding the tool's function.
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 straightforward logging tool with three parameters and no output schema, the description is sufficient. It tells the agent why and what to log, and the schema covers the specific fields. It omits only minor details like whether the log is immediately retrievable or how it integrates with memory search, but nothing critical for a correct call is missing.
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%, with all three parameters documented (mistake_summary, root_cause, prevention_rule). The description does not add extra parameter-level detail beyond what the schema already provides; it only reinforces the overall purpose. Per the calibration baseline, a high-coverage schema earns a 3 here.
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 states a specific verb ('Logs'), a clear resource ('a bug, mistake, or trap encountered in code') and the intended outcome ('so the agent never repeats the same mistake'). It clearly differentiates from sibling tools like log_user_preference and get_agent_memory by focusing on mistake logging rather than preferences or generic memory retrieval.
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 implies when to use it (when encountering a mistake or trap) and explicitly states the goal of preventing recurrence. It does not explicitly name alternatives or exclusion conditions, but the purpose is unambiguous enough that an agent can infer the right context without additional guidance.
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 burden. It discloses the generation mechanism and mentions a behavioral trait of avoiding hallucinations, which is useful. However, it does not state side effects, auth requirements, or what the output format exactly looks like (beyond 'strategies'). Since this is a read-only generation tool, the lack of side-effect disclosure is not critical, but the description could mention that it returns a textual strategy.
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, information-dense sentence. It front-loads the purpose and packs in method and quality attributes. It is not overly long and no words are wasted. It could be split for readability, but it's effective.
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 (2 params, no output schema), the description is mostly sufficient. It explains what the tool produces (step-by-step strategies) and implies the input variables. It lacks explicit statement of output format or limitations, but for a strategy generator, this is acceptable. The description is complete enough for an agent to call it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description adds value by explaining that the parameters (repo_size and primary_language) feed into the dependency-tree and entry-point-mapping approach, giving context for how they influence the output. This goes beyond the schema's simple field descriptions.
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 function: generating step-by-step strategies for reading, mapping, and understanding complex codebases. It specifies the method (dependency trees, key entry-point mapping) and even includes a quality goal ('without hallucinations'). This distinguishes it from sibling tools, which are mostly blueprint generators for other domains (security, deployment, marketing, etc.).
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 implies when to use it: when the agent needs to understand a complex codebase. It gives clear context but does not explicitly name alternatives or state when not to use it. Siblings like generate_*_blueprint are clearly different, so ambiguity is low, but the description lacks direct exclusion criteria.
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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