Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Despite no annotations, the description fully discloses behavioral traits: it is read-only, only reads .py and README.md files, and explicitly states that file creation, editing, deletion, and shell execution are not available. It also details error conditions and return structure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and concise: a single sentence for purpose, bullet points for return values, clear error conditions, and usage recommendations. Every sentence adds value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is fully complete given the tool's complexity and the presence of an output schema. It covers all aspects: input, output, error handling, usage context, and relationships to sibling tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, and the description adds significant value beyond the schema by explaining the 'path' parameter as manifest-relative and describing how to discover valid paths, and for 'max_chars' explaining the range, default, and truncation behavior.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool's purpose: 'Return the source text of a Logic Lab .py file or README.md.' It uses a specific verb and resource, and distinguishes from siblings by mentioning alternatives like get_algorithm_summary, search_algorithms, and get_manifest.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear usage guidelines: it recommends calling get_algorithm_summary first to confirm relevance before fetching full source, and suggests search_algorithms or get_manifest for discovering valid paths. It also indicates when not to use this tool (e.g., for other file types or operations).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses return format for manifest vs non-manifest paths, explicitly states it never returns source code, and indicates it is cheaper. No annotations, so description carries full burden. Could mention error handling but overall transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with clear sections, front-loaded purpose, each sentence adds value, no redundancy. Efficient use of space.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Comprehensive given single parameter and presence of output schema. Covers both manifest and non-manifest cases, return fields, and directs to sibling tools for further needs.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but description adds meaning beyond schema: explains how path validity affects output, and suggests using search_algorithms to discover valid paths. Adds context to parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states the tool returns a short summary without full source, and distinguishes from sibling tool get_algorithm by specifying what it returns (metadata, readme excerpt) and that it never returns source code.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly tells when to use (assess relevance before full fetch, cheaper) and when not (need source code, then use get_algorithm). Provides clear alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses the tool is read-only and returns cached data, which is helpful. However, no annotations are provided, and it does not mention cache freshness or expiration, leaving a minor gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with a one-line summary, uses a clear bullet list for entry structure, and concludes with usage guidance. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that an output schema exists (context signal), the description appropriately focuses on structure and usage. It covers the essential behavioral context (read-only, cached) and entry format, making it complete for a no-parameter tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With zero parameters, baseline is 4. The description adds no parameter-specific info, which is acceptable since none are needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns the full Logic Lab art algorithm manifest as a JSON object, and explicitly differentiates from search_algorithms by stating when to use each.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly says to prefer search_algorithms for filtered discovery, and use get_manifest when the full entry list or category enumeration is needed, providing clear when-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden for behavioral transparency. It thoroughly explains the return structure (intent, combinations, tip) and the composition of each combination (name, description, moods, layers). It notes that suggestions are live manifest entries, implying a read operation. However, it omits details like authorization requirements or rate limits, but given the nature of the tool (recommendation, no side effects), this is acceptable.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections: main purpose, return fields breakdown, and usage instruction. It is relatively long but each sentence adds value (explaining structure, roles, follow-up actions). Minor redundancy could be trimmed, but overall it is appropriately sized for the complexity of the output.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the existence of an output schema (not shown but stated as present), the description still explains the return structure in detail, covering all fields and sub-fields such as 'layers' with their components. It also provides context on how the tool works (curated recipes, dynamic algorithm lookup). This makes the description complete for the tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description adds significant value beyond the schema. For 'intent', it provides examples ('cosmic void with particle trails') and explains its role in ranking. For 'count', it clarifies default, acceptable range, and ranking mechanism. This extra context helps the agent understand parameter semantics beyond the schema's bare definitions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Suggest multi-layer algorithm combinations for a given artistic intent.' It specifies that it returns ranked combinations with structured details. The distinction from sibling tools (e.g., search_algorithms, get_algorithm) is implicit but clear, as this tool focuses on curated layered recipes with dynamic lookup per layer.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit use-case guidance: 'Use this tool to plan layered generative artworks from a text description.' It also advises following up with get_algorithm_summary or get_algorithm for details. While it does not explicitly state when not to use this tool or compare directly with alternatives, the context of sibling tools makes the usage straightforward.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description fully carries the behavioral burden. It discloses that the tool never reads source files, applies automatic synonym expansion, and returns an empty list for no matches (not an error). These traits are clearly stated and not contradicted by any annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose and uses short, direct sentences. It efficiently covers all key aspects without redundancy. Every sentence adds value, and the structure is easy to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 3 parameters (1 required) and an output schema exists, the description provides comprehensive context: behavior, return shape, synonym expansion, category values, limit constraints, and a recommended workflow. There are no gaps for an agent to misinterpret the tool's capabilities.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining synonym expansion (e.g., 'flow' matches 'fluid') and providing usage context for the query parameter. It also clarifies that category is case-insensitive and lists available values. The limit parameter's range and default are restated, but the workflow context enriches understanding beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool searches the Logic Lab manifest by keyword, category, or visual intent. It clearly indicates what it returns (manifest entries with relevance score) and what it does not (read source files). The recommended workflow distinguishes it from siblings like get_algorithm_summary and get_algorithm.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage guidance: use for discovery, then follow with get_algorithm_summary and get_algorithm. It explains how to narrow results by combining query with category, and clarifies that an empty list is not an error. This helps the agent decide when to use this tool vs alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Describes return format (dict with mood, style, profile_summary, results), explains style re-ranking, and error handling. No annotations provided, so description carries full burden — and it clearly communicates all behavioral aspects of a read-only search tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Compact yet comprehensive: one-sentence purpose, bullet-like return field listing, behavioral description, usage comparison, and explicit mood list. No redundant sentences; every line earns its place. Front-loaded with key purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given three parameters (one required), no annotations, and presence of output schema (though not shown), the description covers all important aspects: return structure, parameter effects, error states, and sibling tool distinction. Highly complete for the tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, baseline 3. Description adds value beyond schema: explains style parameter re-ranking behavior ('matched against algorithm metadata to boost ranking'), and limit parameter sorting ('sorted by combined mood-profile and style score'). These non-trivial additions justify a 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool searches algorithms by creative mood, and explicitly distinguishes from sibling tool search_algorithms by specifying when to use each. It lists the exact 12 available moods, making the purpose highly specific and differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit guidance: use when you have a specific visual mood, prefer search_algorithms for free-text queries without clear aesthetic direction. Also documents error behavior for unrecognized moods and lists available moods, enabling correct agent decision-making.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.