Agent Router

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

With no annotations provided, the description must disclose behavior. It correctly states the tool is synchronous and returns immediate responses, and that it's free. However, it does not cover error handling, timeout behavior, or response structure (though an output schema exists, which mitigates the latter).

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: a clear first sentence for purpose, a key feature line, and inline parameter documentation. It is concise without being terse, though the parameter list could be slightly more compact.

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 4 parameters, 0% schema coverage, and an existing output schema (which covers return values), the description covers parameters well, mentions the source of agent URLs, and notes free usage. It lacks explicit error handling details but is otherwise complete for typical use.

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 description coverage is 0%, so the description must compensate fully. It explains each parameter: agent_url (source from discover_agents), message (the input), context_id (for multi-turn conversations), and timeout_seconds (default 30, max 120). This adds significant meaning 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 clearly states it 'call any public A2A-compatible agent directly by its endpoint URL', providing a specific verb and resource. It distinguishes from sibling tools like discover_agents (which finds agents) and wait_for_task (which is for tasks), 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.

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

The description explains when to use this tool (to call agents synchronously) and references discover_agents for finding agent URLs. It also notes that it's free and requires no login, but does not explicitly state when not to use it or mention alternatives beyond discover_agents.

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 discloses behavioral traits: it performs web navigation, interacts with pages, and produces structured results. It also adds cost and runtime details, which are helpful for agent planning.

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 four sentences, front-loading the purpose and actions. It includes relevant metadata (cost, runtime) without excess. Slightly more concise could remove 'supports the Main Agent' but overall efficient.

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 covers core functionality and output type, but lacks details on error handling, authentication, or limitations. The presence of an output schema (not shown) may compensate, but the description alone is moderately complete.

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 sole parameter task_description has no schema description (0% coverage). The tool description implies it should contain a navigation goal but does not specify format, examples, or constraints, leaving ambiguity.

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 performs goal-oriented web navigation, listing specific actions like searching, clicking links, and form interaction. It distinguishes itself from sibling tools such as researchagent by focusing on browser-based task completion.

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 does not provide explicit guidance on when to use this tool versus alternatives like researchagent. It mentions supporting the Main Agent but lacks when-not-to-use scenarios or prerequisites.

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 adds operational details beyond annotations (which are absent): cost of 4 credits and expected runtime of ~30s, plus a concrete list of generated outputs. While it does not explicitly state the tool is read-only or non-destructive, the planning nature minimizes that concern. The added cost/runtime context is valuable.

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 two concise sentences plus cost/runtime, front-loaded with the core action and an output list. Every sentence carries essential information 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 tool accepts a payload with 10 parameters, but the description covers only about half of them, introduces an invalid 'weather' parameter, and provides no details on required fields or optional parameter formats. Even with an output schema present, the input side is insufficiently documented for correct invocation.

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 description coverage is 0%, so the description must compensate. It names several inputs (destination, experience level, group size, budget, activities) but omits required trip_days and optional fields like pets, children, transport, session. Critically, it mentions 'weather' which is not present in the schema, potentially misleading the agent. No value formats or constraints are provided.

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 uses the specific verb 'Plan' with the resource 'complete camping trips', and enumerates the input dimensions (destination, weather, experience level, group size, budget, activities) and output artifacts (packing lists, meal plans, safety checklists, itineraries, campsite recommendations, emergency preparation). This clearly distinguishes it from sibling generic agents.

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?

Usage is implied by the tool's domain-specific purpose—an agent needing camping trip planning would naturally select this tool. However, there is no explicit 'when to use' or 'when not to use' guidance, nor any reference to alternatives among the sibling tools.

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 carries the burden of behavioral disclosure. It mentions cost and runtime, which is transparent about resource usage, and implies a read-only analysis. However, it does not describe side effects, data persistence, or what happens to the feedback.

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 concise (3 sentences), front-loaded with the core purpose, and includes useful additional info (cost and runtime). 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 the existence of an output schema, the description need not detail return values, but it lacks explanation of the input parameter. It is adequate for a simple analysis tool but leaves gaps in parameter understanding.

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 description coverage is 0%, and the description does not explain the parameter 'approache/strategie' beyond implying it is the input approach. The parameter name has a typo and no formatting or constraints are provided.

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 that the tool analyzes complex approaches and strategies, identifies strengths and weaknesses, and provides structured feedback with suggestions and alternatives. This is specific and distinguishes it from sibling tools like 'firstprinciplesanalyst' or 'exploitagent'.

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 lacks guidance on when to use this tool versus its siblings. It does not mention prerequisites, exclusions, or contexts where alternatives would be better. The cost and runtime are provided but do not constitute usage guidelines.

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, so description fully covers behavior: returns two types with call methods, free, filtered by health, and includes uptime/latency. No contradictions.

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-organized with sections, bullet points, and no redundant information. Every sentence is informative and necessary.

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?

Covers all relevant aspects: tool purpose, agent types, usage patterns, cost, and output fields. With output schema present, no gaps remain.

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?

Describes both parameters (query: keywords for capability; limit: max results, default 10, max 25) with practical usage notes, adding value beyond schema defaults.

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 discovers agents to call, distinguishes between platform and registry agents, and explains how to call each type. Differentiates from siblings like a2a_call_agent and wait_for_task.

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 detailed usage instructions for both agent types, including method of invocation and filtering by health. Lacks explicit when-not-to-use but gives sufficient context.

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. The description includes cost and runtime, which are useful but insufficient. It does not disclose side effects, authorization needs, or data persistence implications.

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?

Two sentences, but one is tautological ('Agent created via direct DB write') and the other provides only metadata. Not appropriately concise because it omits essential information.

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 no annotations and 0% schema coverage, the description is severely incomplete. It fails to explain the tool's purpose, input format, behavior, or output.

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 single parameter 'payload' is an open object with no property definitions. Schema description coverage is 0%. The description offers no guidance on payload structure or expected values.

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 'Agent created via direct DB write' does not state the tool's function. It reads as a fact about creation, not an actionable verb. The name 'exploitagent' hints at malicious use but is not clarified.

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?

No guidance on when to use this tool versus siblings like 'a2a_call_agent' or 'researchagent'. No prerequisites or context provided.

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 provided, so description carries full burden. Discloses cost (1 credit) and runtime (~30s), and states it does not deliver ready-made solutions. However, does not describe output format or side effects beyond 'atomic-level insights and strategic perspectives.'

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?

Description is concise and front-loaded with purpose. Includes practical info (cost, runtime) but could be slightly tighter. Every sentence adds value, though the last sentence on 'supporting the Main Agent' is somewhat redundant.

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 single parameter and presence of an output schema, description covers core purpose and behavioral restrictions. However, lacks details on input quality expectations or how output integrates with other agent tasks.

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 0%; only parameter is 'task_description' under a nested payload. The description does not elaborate on what constitutes a good task description or provide examples, leaving the agent to infer from the name alone.

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 describes the tool as decomposing complex tasks using first-principles thinking, providing guidance without ready-made solutions. Distinguishes from siblings like 'browsernavigationagent' or 'scientificresearchagent' by its analytical, non-solution nature.

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?

Mentions supporting the Main Agent in understanding and structuring problems, but does not explicitly state when to use this tool versus alternatives. Lacks 'when not to use' guidance or specification of prerequisites.

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 carries full burden for behavioral traits. It discloses that the tool returns metadata and optional SKILL.md body, mentions response includes version, stats, install command, and SKILL.md content, and states free usage. It implies read-only operation by 'Fetch', but does not explicitly confirm idempotency or no 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.

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

The description is concise with no wasted words. It front-loads the purpose, then provides usage context, response summary, and parameter details. 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 the existence of an output schema, the description adequately covers what the tool does, how to use it, and parameter behavior. It could mention error handling (e.g., if slug not found), but overall it is sufficiently complete for a simple fetch 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?

Schema description coverage is 0%, so the description compensates by explaining each parameter: slug is 'ClawHub skill slug (lowercase, from search_skills)' and include_content with default true and effect. This adds valuable meaning beyond the schema 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 'Fetch a ClawHub skill by slug', specifying the action (fetch) and resource (skill). It differentiates from sibling tools by referencing search_skills for obtaining the slug, making the purpose distinct.

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 the slug from search_skills results' and notes 'Free to use — no API key required.' This helps the agent select the tool correctly. It lacks explicit 'when not to use' but is otherwise clear.

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 the full transparency burden. It discloses operational traits such as autonomy, cost (2 credits), expected runtime (~20s), and that it returns structured recommendations. However, it does not mention whether the tool invokes other tools, requires specific permissions, or has side effects, 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.

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

The description is concise and front-loaded, stating the purpose in the first sentence and adding operational details (cost, runtime) in a clear, uncluttered manner. Every sentence contributes value with no 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?

Despite having an output schema and a single parameter, the description is incomplete for an autonomous agent tool. It lacks guidance on when to use it versus sibling tools, does not explain its underlying behavior or dependencies, and provides no examples or limitations. The cost/runtime info is useful, but overall, a complex agent tool needs more context to be safely and correctly invoked.

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 context signals report 0% schema description coverage, so the description must compensate. It only says 'for business problems,' which vaguely maps to the 'query' parameter but does not explain query format, examples, or constraints. The schema's query property does have a description, but per the given signal, this is not counted, so the description adds minimal semantic value.

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 performs autonomous strategic analysis with a specific NEXUS lens (competitive positioning, portfolio economics, defensibility) and returns structured recommendations. It is specific about the resource and output, though it doesn't explicitly distinguish itself from sibling analysis tools like firstprinciplesanalyst.

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 only says it is 'for business problems,' which is broad context but provides no explicit when-to-use versus alternatives or exclusions. With many sibling agents (e.g., researchagent, firstprinciplesanalyst), there is no guidance on choosing this tool over others.

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

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

No annotations are provided, so the description carries full burden. It mentions cost (6 credits), runtime (~60s), and that it evaluates alternatives and connects sources. However, it does not detail failure modes, constraints, or what happens if the reasoning fails. The transparency is adequate but not exhaustive.

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 a single focused paragraph that front-loads purpose, then mentions cost and runtime. It is concise and avoids redundancy. However, it omits parameter information, which would improve completeness without adding much length.

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's complexity (high-effort reasoning) and the existence of an output schema, the description explains purpose, behavior, cost, and runtime but fails to describe the input parameter. This omission makes it incomplete for practical use, as the agent does not know what to provide in the query.

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 description coverage is 0%, meaning the input schema provides no descriptions for the 'payload' or 'query' parameters. The description does not mention the input at all, failing to explain what kind of query the agent expects or how to structure the payload. This is a critical gap since the description should compensate for the lack of schema descriptions.

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 this tool is for complex tasks requiring deep, multi-step analysis and problem-solving, and lists specific use cases like planning and architecture design. The name and context imply it is the high-effort variant among low, medium, and high siblings, effectively distinguishing its purpose.

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 implies usage for challenging tasks but does not explicitly state when to avoid this tool or mention alternatives like reasoninglelegationlow or medium. It provides context for use but lacks clear when-to-use and when-not-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 provided, so description carries full burden. It outlines behavior (clarify problems, outline steps, make decisions) and adds cost/runtime, but does not delve into internal mechanisms or side effects. Adequate but could be more specific, e.g., whether it modifies external state.

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?

Three concise sentences plus a cost/runtime line. Front-loaded with purpose, no extraneous words. 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 an output schema exists (not shown), description need not detail returns. Covers purpose, usage, cost, runtime. Lacks mention of failure modes for complex tasks, but overall complete for a simple delegation 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?

Input schema has one parameter (payload containing task) with 0% description coverage. The description does not explicitly describe the task field or payload structure, leaving the agent to infer input format from context. Fails to compensate for missing schema descriptions.

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 defines the tool as a low-effort reasoning agent for simple to moderate tasks, performing lightweight reflection and structured thinking. It explicitly contrasts with full deep analysis, and the sibling names (reasoningdelegationhigh, reasoningdelegationmedium) reinforce its niche.

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 context: use when full deep analysis is not required, and mentions cost (2 credits) and runtime (~45s) to guide selection. It implies appropriateness for simple tasks but lacks explicit when-not or alternatives among other sibling agents.

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 provides some behavioral details: cost of 4 credits, runtime of ~50s, and an outline of the reasoning approach. However, it lacks disclosure about failure modes, limitations, or side effects, which would be needed for full transparency.

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 concise with no wasted words; it packs purpose, behavioral traits, cost, and runtime into a few sentences, well-structured and easy to parse.

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 presence of an output schema, the description adequately covers the tool's purpose, behavioral context, and cost/runtime. The sibling tools provide context that this is one of three effort levels, making the description complete for this ecosystem.

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 only parameter 'task' has an empty schema description, and the tool description does not add any specifics about what the task string should contain (e.g., format, length, or examples), leaving the parameter under-specified.

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 it is a medium-effort reasoning agent for moderate to complex tasks, using structured multi-step thinking. It distinguishes itself from siblings by specifying 'medium-effort' and contrasting with high and low effort versions.

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 the tool is 'suitable for tasks that require thoughtful reasoning but do not justify maximum computational intensity', providing clear guidance on when to use it. However, it does not explicitly exclude tasks or suggest alternatives, leaving some ambiguity.

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 cost (3 credits) and expected runtime (~60s), and outlines the research process (compares sources, highlights uncertainties). However, without annotations, it doesn't cover failure modes, rate limits, or authorization needs, which would enhance transparency.

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 a clear sequence of actions and outputs. It is concise but includes necessary details like cost and runtime. Minor redundancy, but overall efficient.

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 covers the research process comprehensively, listing outputs like key terms, studies, and open questions. It includes cost and runtime. The presence of an output schema further covers return values, making the description nearly complete.

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 has 0% description coverage for the 'topic' parameter. The description only mentions 'on a given topic', adding minimal semantic value. It does not explain the parameter's format, constraints, or expected content.

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 conducts 'thorough, iterative internet research' and identifies its outputs, making the purpose clear. However, it does not differentiate itself from sibling tools like 'scientificresearchagent' or 'exploitagent', which may have overlapping functionality.

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 no guidance on when to use this tool versus its siblings, such as 'scientificresearchagent' or 'exploitagent'. It only states its general function, leaving the agent to decide without explicit context.

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 isolated and stateless sandbox, cost (2 credits), and expected runtime (~45s). Mentions identifying what is achievable within constraints, but could be more explicit about specific limitations (e.g., network access, library availability).

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?

Three sentences plus cost/runtime make it relatively concise. However, there is some redundancy (e.g., 'concrete, structured outputs' and 'verified results and actionable insights' overlap slightly).

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?

Has output schema so return values handled, but the single parameter's semantics are unclear. Given low complexity, the description should fully specify how to invoke the tool, but it leaves ambiguity between code vs. task description.

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 schema description coverage at 0%, the description must clarify the parameter 'task_description'. However, it ambiguously says 'Executes code' while the parameter seems to describe a task. It does not explain whether to provide code or a description.

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 it executes Python or JavaScript code in an isolated, stateless sandbox for validating, testing, or solving tasks. It distinguishes itself from sibling tools like reasoning agents by focusing on actual code execution.

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?

Implicitly suggests use when code execution is needed, but lacks explicit guidance on when to use alternatives (e.g., reasoning agents for logic-only tasks). No 'when not to use' or direct contrast with siblings.

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 that it retrieves academic literature, synthesizes evidence, and distinguishes evidence levels. It also includes cost and runtime. However, no annotations are provided, and the description does not cover authentication needs, data sources, or limitations such as possible inaccuracies or domain restrictions.

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 concise, consisting of two sentences plus cost and runtime. It is well-structured and front-loaded with the core purpose. However, it could be slightly more efficient by omitting the redundant 'Supports the Main Agent' phrase.

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?

An output schema is present, so explaining return values is not necessary. However, the description lacks usage context, parameter details, and behavior expectations. Given the complexity (sibling tools, 0% schema coverage), more completeness is needed to avoid ambiguity.

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 input schema has only one parameter with 0% description coverage, and the tool description does not add any meaning beyond the parameter name 'task_description'. Given the low coverage, the description should compensate but fails to do so.

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 analyzes tasks from a scientific perspective, retrieves evidence from academic literature, and produces structured insights. However, it does not distinguish itself from sibling tools like 'researchagent' or 'firstprinciplesanalyst', which may have overlapping purposes.

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 no guidance on when to use this tool versus alternatives. It does not mention when not to use it, prerequisites, or exclusions. Sibling tools with similar names suggest potential confusion without explicit differentiation.

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 provided, so description must disclose behavior. It states it searches a live registry, is free, and describes returned fields. It does not mention any destructive actions or rate limits, but the read-only nature is clear.

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?

Description is well-structured with a clear action line, registry context, result details, and parameter explanations. No unnecessary words; front-loaded with 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 the existence of an output schema (not shown but indicated), the description covers necessary context: purpose, registry info, what results contain, and follow-up guidance. Complete for a search 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?

Schema coverage is 0%, but description compensates fully: explains query as natural language capability with examples, and clarifies top_k with default and max limit. Adds significant meaning beyond the raw 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?

Clearly states the tool performs semantic search over the ClawHub skills registry. Distinguishes itself by mentioning results include slug, author, summary, install hint, canonical URL, and suggests following up with get_skill for full details, setting it apart from siblings like get_skill or discover_agents.

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 states it's free with no API key required, and implies use for finding skills via natural language queries. Provides a follow-up action (get_skill) but does not explicitly exclude alternatives among siblings like discover_agents.

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 discloses important behaviors: cannot execute code, cost of 3 credits, expected runtime ~50s, and 'without speculation'. It also outlines what it investigates, providing good insight into its operation.

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 concise, with each sentence adding value: purpose, investigative areas, support role, limitations, and cost/runtime. No fluff or 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?

For an expert analysis tool with an output schema, the description covers core purpose, limitations, and runtime but omits input specification and output format. The input parameter is not explained, which is a notable gap.

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 description coverage is 0%, and the description does not explain the 'task_desciption' parameter. It only broadly mentions solving tasks, leaving the input meaning unclear. The description should at least define what a task description should contain.

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 performs expert-level software engineering analysis using repository and code evidence, and lists specific investigations (architecture, dependencies, etc.). While it does not explicitly differentiate from siblings, the specialization is implied by the tool name and context.

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 mentions supporting the Main Agent with actionable guidance and cannot execute code, giving some usage context. However, it does not provide explicit when-to-use or when-not-to-use guidance, nor mentions alternative tools.

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 carries full burden and does well by disclosing action scope (does not execute or delegate) and resource usage (cost, runtime). However, it omits whether it modifies state or requires specific access privileges.

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 concise, front-loads the core purpose, and adds non-redundant details (cost, runtime). Every sentence adds value, though the cost/runtime info could be moved to annotations if available.

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 lacks mention of output format (though output schema exists) and prerequisites. It also doesn't clarify if 'Main Agent' imposes constraints on who can call it. For a tool with one parameter, additional context about expected use within the agent ecosystem would improve completeness.

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 has 0% description coverage, so the description must compensate. The parameter 'task_description' is self-explanatory, and the description adds context by mentioning 'complex user requests or problems', but does not detail expected input format or constraints.

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 generates structured iterative execution plans from complex requests, specifying what it does (task breakdowns, phase sequencing, knowledge flow) and what it does not do (execute or delegate). This distinguishes it from sibling tools that are execution or analysis agents.

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 implies usage for planning phases but does not explicitly state when to avoid this tool or provide comparisons to siblings like 'reasoningdelegationhigh' or 'constructivecritic'. The phrase 'Supports the Main Agent' suggests context but lacks clear alternative 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?

With no annotations, the description must disclose behavior fully. It mentions cost and runtime, but fails to state side effects, return values, or required permissions, leaving 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.

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

The description is succinct with two sentences, but the brevity sacrifices critical information such as when to use and parameter details. It is adequately front-loaded but not optimally informative.

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?

Despite low complexity and the presence of an output schema, the description lacks details on payload format, agent capabilities, and return structure, making it incomplete for effective agent use.

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 description coverage is 0%, and the description adds no meaning to the 'payload' parameter beyond the empty schema definition. The agent's expectations for payload content are entirely unspecified.

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 delegates a task to TestAgent, using a specific verb and resource. However, it does not differentiate TestAgent from sibling agents like a2a_call_agent or reasoningdelegationhigh.

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?

No guidance is provided on when to use this tool versus alternatives. The description includes cost and runtime but offers no criteria for task selection or exclusions.

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 description carries full burden. It explains the tool blocks until completion, why it exists (initial wait insufficient), and parameter constraints (max_wait up to 300s). Could mention it returns the final result but output schema covers that.

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?

Description is concise, well-structured with a clear purpose statement, a bulleted list of usage conditions, and an Args section. Every sentence adds value with no waste.

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's async waiting nature, 2 parameters, and presence of an output schema, the description fully covers when to use, how to use, parameter meanings, and constraints. No gaps.

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 has 0% description coverage, but description compensates well: explains task_id as 'task UUID from a platform agent response with STATUS=RUNNING' and max_wait_seconds with default and max values. These add meaning beyond the raw 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?

Description clearly states the tool waits for a platform agent task to complete and return its result. It distinguishes itself from sibling tools like a2a_call_agent by explicitly saying NOT needed for that, and clarifies it's only for the RUNNING status case.

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 when-to-use condition (STATUS=RUNNING after initial 50s wait) and when-not-to-use (STATUS=COMPLETED/FAILED, or for a2a_call_agent). This eliminates ambiguity.

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