Agent Router

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

With no annotations provided, the description carries the full burden and does well. It discloses synchronous behavior ('Returns the agent's response immediately'), mentions no login required, and includes timeout handling. It doesn't cover potential errors or rate limits, but for a free public tool, the key behaviors are 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?

The description is front-loaded with the core purpose, then behavior, then sourcing, then free/no-login, then parameter arguments. Each sentence adds value, and the structure is logical and scannable. It's appropriately sized for the tool's complexity.

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 moderate complexity, the description covers everything an agent needs: how to obtain the URL, what to send, optional context continuation, timeout settings, and free access. The output schema handles return value explanations, so the description is complete 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, and it excels. Every parameter is explained: agent_url (from discover_agents ENDPOINT field), message (message or task), context_id (optional, for multi-turn), and timeout_seconds (default 30, max 120). This fully compensates 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 the tool's purpose: 'Call any public A2A-compatible agent directly by its endpoint URL.' It uses a specific verb (call) and resource (agent), and distinguishes itself from siblings like discover_agents (which finds endpoints) and other agent tools. The scope is well defined.

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 provides practical guidance: 'Get agent_url values from discover_agents (look for ENDPOINT in results),' and notes it works with any agent on a2aregistry.org or any A2A JSON-RPC endpoint. It also mentions it's synchronous with no task_id, implying when to use it vs async tools, though it doesn't explicitly state when not to use 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?

With no annotations provided, the description carries the full burden. It discloses the main behaviors: accesses websites, searches, clicks, interacts with forms, extracts content, and produces structured results. It also adds a runtime expectation (~30s), which is useful for agent planning. It does not mention limitations or error cases, but for a web agent the core actions are well covered.

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 sentences plus a runtime note, with the primary action front-loaded. Every sentence adds new information: the approach, the specific actions, the output nature, support role, and runtime. No redundancy or filler.

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 tool's purpose, actions, output type, and runtime. An output schema exists, so return format specifics are likely handled there. The single parameter is implied. For a browser agent with a single task description prompt, this is fairly complete, though it could mention the structure of the results more explicitly.

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 single parameter (task_description), so the description must compensate. The description implicitly explains that 'task_description' is the task to complete via 'to complete tasks', but it never explicitly names or describes the parameter. Since the parameter is self-explanatory in context, the description provides marginal value but does not fully compensate for the missing schema 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 states a clear verb ('Performs goal-oriented web navigation') and resource ('browser tool'), then enumerates specific actions (accesses websites, searches, clicks links, interacts with forms, extracts content). It also differentiates itself from the sibling agents by focusing on web navigation and producing structured results from navigated information, which is distinct from the other agent-like tools.

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 tasks requiring web browsing and states it 'supports the Main Agent with verified web-based insights', giving context. However, it does not explicitly compare to alternatives or state when not to use it. The purpose is clear enough that an agent could infer when to call it, but it lacks explicit exclusions or alternative tool references.

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 provided, the description carries the full burden of behavioral disclosure. It mentions an expected runtime (~30s) and lists the generated outputs, which gives some insight into its behavior. However, it does not disclose whether the tool modifies any data, requires specific permissions, or has side effects beyond generating a plan. For a planning tool, this is a moderate gap, so a score of 3 reflects partial 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—two sentences—with no filler. The first sentence front-loads the core purpose and inputs, and the second lists outputs and runtime. Every clause adds value, and the structure is efficient for an agent scanning for 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 tool's complexity (10 parameters, multiple output types) and the lack of annotations or parameter descriptions, the description is insufficient for an agent to correctly populate all required fields. It lists inputs and outputs but does not specify which parameters are required (though the schema indicates payload is required), how to format values (e.g., date ranges, activity lists), or what 'session' refers to. The output schema exists but is not exposed here, and the description does not compensate for the missing parameter semantics. A more complete description would explain each parameter's role and constraints.

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% (all parameter descriptions are empty), so the description must compensate. It explicitly mentions destination, weather, experience level, group size, budget, and activities, which map to several schema properties (destination, experience, people, budget, activities). However, it omits other parameters like trip_days, pets, children, transport, and session, and does not explain their formats or relationships. The description adds some meaning but is incomplete for the full parameter set.

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 states a specific verb ('Plan') and resource ('complete camping trips') with detailed scope: destination, weather, experience level, group size, budget, and activities. It also lists concrete outputs (packing lists, meal plans, safety checklists, itineraries, campsite recommendations, emergency preparation), which clearly distinguishes it from the unrelated sibling agents. There is no ambiguity about what the tool accomplishes.

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 context: it is for planning complete camping trips. While it does not explicitly name alternatives or state when not to use it, the sibling tools are all unrelated agents, so there is no competing camping tool. The usage is strongly implied by the purpose, but the lack of explicit exclusions or alternative routing prevents a higher score.

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 burden of behavioral disclosure. It only mentions an expected runtime (~30s) as a behavioral note, but does not disclose whether the operation is read-only, has side effects, or what happens on failure. Given it is an analysis agent, the lack of any safety or mutation details leaves the agent guessing.

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, containing two sentences plus a runtime note. It is front-loaded with the core purpose and terse enough. The runtime note is an added detail that helps, though it is slightly tangential. No redundant fluff.

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 is relatively simple (one string input, likely text output), and an output schema exists, so return-value explanation is not required. However, the description lacks clarity on the exact input format and does not place the tool within the broader agent ecosystem. It is minimally adequate but misses context that would help an agent use it correctly, such as what constitutes a 'complex approach' or what kind of feedback is expected.

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 0% description coverage, and the description does not mention the parameter at all. The required property 'approache/strategie' is cryptic (with a typo) and the description offers no clarification on what to pass, its format, or expected content. With no schema help, this is a major gap.

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 a specific action: it analyzes approaches/strategies by identifying strengths and weaknesses and provides structured feedback with suggestions. This distinguishes it from sibling tools like 'exploitagent' or 'firstprinciplesanalyst' by its constructive-criticism focus, though it does not explicitly reference siblings. The verb and resource are clear.

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 gives no guidance on when to use this tool versus alternatives. It does not mention any conditions, prerequisites, or exclusions. Siblings are not referenced, leaving the agent to infer usage from the name and description alone, which is insufficient for a set of similarly purposed analysis 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 carries the full burden. It discloses async vs. sync behavior, filtering by is_healthy, and that results include uptime/latency metrics. It also notes the tool is free. It doesn't mention edge cases like pagination limits beyond the max 25 or error handling, but it covers the key behavioral traits an agent needs to know before calling.

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 longer than minimal but well-structured with bullet points and clear sections for each type. It is front-loaded with the purpose and immediately provides actionable calling details. Every sentence adds value, though it could be tightened slightly without losing 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?

The tool has an output schema (though not shown), and the description explains the two result types, their calling conventions, and the metrics shown. Given the complexity of handling two distinct agent types, the description covers everything an agent needs for correct invocation and follow-up. No obvious 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 description coverage is 0%, and the description fully compensates. It explains that 'query' filters by capability with examples, and that empty queries browse top agents. It specifies 'limit' default (10) and max (25). This adds significant meaning beyond the raw schema, making parameters self-explanatory.

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 'find' and resource 'agents', clearly stating the tool discovers both platform and A2A registry agents. It distinguishes itself from siblings like a2a_call_agent by specifying it's for discovery, not calling. The two distinct result types are clearly named and described.

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 when to use this tool (to find agents) and provides distinct calling instructions for each type: platform agents via their MCP tool name with wait_for_task for async handling, and registry agents via a2a_call_agent with the agent URL. This routes the agent to the correct next step and implies the tool is a precursor to those actions.

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 carry the full behavioral burden. It only mentions runtime (~30s) and creation method. It does not disclose side effects, permissions, what happens to the payload, return format, or any other behavioral traits. This is essentially opaque.

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 text is extremely short (two fragments) but this is under-specification, not conciseness. There is no front-loaded purpose or meaningful structure. While it's efficient in word count, it fails to provide essential information, so the conciseness is counterproductive.

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 is a dynamic handler (accepts arbitrary JSON) with no documentation, no annotations, and a description that only gives runtime. Even if an output schema exists, the input and behavior are entirely unspecified. An agent cannot correctly select or invoke this tool with the provided information.

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 one required parameter 'payload' of type object with no properties and additionalProperties=true, and schema description coverage is 0%. The description does not mention or explain the payload parameter at all. With no schema descriptions and no description text, agents have no clue what to pass.

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 does not state what the tool does. 'Agent created via direct DB write' is an implementation detail (how it was created) and 'Expected Runtime: ~30s' is a performance note. Neither conveys the purpose, action, or resource. The name 'exploitagent' hints at exploitation but the description doesn't confirm any specific behavior.

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 alternatives. The sibling list includes many agent-like tools, but the description gives no context for selecting this one over others, nor any exclusions 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?

No annotations are present, so the description carries the full burden of behavioral disclosure. The only behavioral cue offered is 'Expected Runtime: ~60s', which is a useful operational hint, but nothing is said about side effects, state changes, input/output format, or how the agent behaves.

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 brief, but this is under-specification rather than efficient conciseness. The runtime note is cleanly stated, but there is no substantive content to structure; the text fails to earn its place with useful 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?

For an agent tool with no annotations, no parameter explanation, unknown output behavior, and 23 competing sibling agents, this description is severely inadequate. An agent cannot safely or correctly invoke it based on this definition alone.

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. The single parameter is suggestively named 'financial agent' (implying a free-text prompt), but the description offers no guidance on what should be passed, its format, or constraints. The name carries the meaning on its own.

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 'full financial agent' communicates only the domain (financial) and that it is an agent, but not what the agent actually does—analysis, trading, accounting, advisory, or otherwise. It names nothing more specific than its own title, so it barely rises above a tautology.

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?

There is no guidance whatsoever about when to use this tool versus the 23 sibling agents (researchagent, scientificresearchagent, exploitagent, etc.). An agent has no way to decide whether financial_agent is the right choice for a given financial task.

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 burden. It does disclose one meaningful behavioral trait — 'Provides guidance and clarity without delivering ready-made solutions' — which is important for setting agent expectations, plus a runtime estimate (~30s). However, it omits other relevant behaviors such as what the output looks like, whether any side effects occur, or how the tool handles failures or timeouts.

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 efficiently written — roughly three sentences covering purpose, deliverables, and behavioral boundary, capped with a runtime note. The core action is front-loaded. Minor over-packing ('generates atomic-level insights and strategic perspectives' plus 'guidance and clarity') slightly dilutes the impact, but nothing is wasted.

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 simple shape (one required parameter) and the presence of an output schema (so return values needn't be described), the description covers what the tool does, how it behaves (no ready-made solutions), and its expected runtime. It is complete enough for an agent to call it correctly, with only edge-case behavior (failure modes, exact response shape) left unexplained — and the latter is handled by the output schema.

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% (task_description has an empty description), so the tool description must compensate. It does not explicitly describe the parameter, but with only one self-evident parameter (the task/problem to deconstruct) whose meaning is inferable from the tool's stated purpose, the burden is low. This is functional but not explicit — the description could have stated that task_description is the complex problem to be analyzed.

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 a specific verb-resource pair ('deconstructs complex tasks or problems using a first-principles approach') and explains what it produces (components, dependencies, leverage points, atomic-level insights). Purpose is clear, but it does not explicitly differentiate itself from closely related siblings such as reasoningdelegationhigh/medium/low, constructivecritic, or nexus_strategy_analyst, all of which likely occupy similar analytical ground.

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 states the general context ('supporting the Main Agent in understanding and structuring the problem') which implies when it is useful, but it never names alternatives or gives explicit when-to-use vs. when-not-to-use guidance. With numerous sibling tools (constructivecritic, nexus_strategy_analyst, reasoningdelegation*) that could overlap, the lack of explicit routing criteria leaves the selection decision partially to inference.

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 supplied, so the description carries the full burden. It discloses the read-only, echo-returns-input behavior and adds a latency note ('Expected Runtime: ~10s'), which is useful. However, it doesn't clarify error cases, empty-query handling, or whether any side effects occur, leaving some 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?

Three short sentences with zero filler; the core behavior is front-loaded, and the runtime note is a genuinely useful addition. Every sentence earns its place.

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 a single-parameter demo tool with an output schema present, the description covers the echo behavior, the testing use case, and the runtime expectation. The main residual gap is the unexplained payload wrapper, but this is minor given the tool's simplicity.

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. 'Echoes back any text query' correctly maps the semantic of the required 'query' field, but it never explains the 'payload' wrapper object that sits above it, nor the schema's additionalProperties. Partial compensation only.

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 states a specific verb and resource ('echoes back any text query'), and frames it as a demo/testing utility. It stands apart from the research/expert siblings by its explicit demo-and-test purpose, though it doesn't name an alternative tool directly.

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 gives a clear intended context ('Useful for testing A2A webhook integrations'), which tells an agent when to reach for it. It stops short of explicit exclusions or named alternatives, but the demo/testing framing is sufficient for a tool of this scope.

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 carry behavioral disclosure. It mentions 'Autonomous' and expected runtime (~20s), and states it returns structured recommendations, but it does not clarify side effects, permissions, or whether it accesses external data. This is a moderate level of transparency but leaves significant unknowns.

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 sentences plus a runtime note, with no filler. It front-loads the core purpose and delivers the key information efficiently. 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?

For an autonomous agent tool with a 20-second runtime and no annotations, the description should cover usage context, behavioral expectations, and parameter semantics. It provides only purpose and runtime, leaving out critical details that an agent would need to decide when and how to call it.

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 reported as 0%, so the description should compensate by explaining the query parameter. The description only repeats that it handles business problems, not how to phrase queries or what kind of input is expected. It adds no meaningful semantics beyond the schema's own description of the query field.

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 strategic analysis with specific lenses (competitive positioning, portfolio economics, defensibility) and returns structured recommendations. It is specific and not a tautology, but does not explicitly name alternatives, so it falls just short of full differentiation from siblings.

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 given on when to use this tool versus sibling agents like firstprinciplesanalyst or reasoningdelegation. The description only implies usefulness for business problems, but lacks explicit conditions, exclusions, or alternative recommendations.

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 full burden. It discloses expected runtime (~60s) and methodology (evaluates alternatives, connects sources, reasons through uncertainty), which is useful. However, it does not mention whether the tool is read-only, whether it has side effects, or what the return format looks like. The behavior is partially transparent but lacks critical details.

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 appropriately concise: two sentences plus a runtime note. It front-loads the purpose and lists use cases, with the runtime as a separate line. No extra fluff, and the structure is clear.

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 a tool with a single required parameter and no annotations, the description should explain what the query should contain and what the output will be (especially since an output schema exists but is not provided). It also lacks any prerequisites or limitations. The description is insufficient for an agent to correctly invoke the tool without additional information.

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 description says nothing about the 'payload' or 'query' parameters. The schema itself has no parameter descriptions (0% coverage), so the agent receives no guidance on what to put in the query field. This is a critical gap, as the description fails to compensate for the missing schema information.

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 this is a high-effort reasoning agent for complex tasks requiring deep analysis, and specifies typical use cases like planning, architecture design, and analytical tasks. It differentiates implicitly through the 'high-effort' label and runtime, but does not explicitly contrast with the low and medium variants, so it falls short of a 5.

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 provides clear context on when to use it ('suited for challenging planning, architecture design, and analytical tasks where accuracy and depth are critical'), but does not explicitly state when NOT to use it or mention alternatives like reasoningdelegationlow or reasoningdelegationmedium. The guidance is present but not exhaustive.

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 burden of behavioral disclosure. It discloses that the agent is 'quick, cost-efficient' and 'minimal computational overhead', and gives an expected runtime of ~45s. However, it does not describe what the output looks like, potential limitations, or behavior beyond basic reasoning. It covers the core behavior but leaves gaps such as error handling and determinism, which are somewhat expected for a reasoning tool but not specified.

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 two sentences that front-load the primary purpose and usage conditions, followed by a runtime note. It wastes no words and is easily scannable. It could be more structured with explicit sections, but it is appropriately sized for a low-complexity tool.

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 low complexity (single parameter, no nested objects), the description provides sufficient context for understanding its role and when to use it. However, the complete absence of parameter semantics means an agent cannot know what to put in the 'task' field, which is essential for invocation. The runtime note adds useful context, and the output schema exists to define return value structure, so the description doesn't need to explain that. But the parameter gap makes it incomplete.

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 schema provides no descriptions for the 'payload' or 'task' fields. The tool description does not mention the parameter at all, providing no additional meaning. Since coverage is 0%, the description must compensate, but it fails to explain what 'task' should contain or how to structure the payload. This is a critical gap for an agent to call the tool correctly.

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 function: a low-effort reasoning agent for simple to moderate tasks, performing lightweight reflection and structured thinking. It explicitly contrasts with 'full deep analysis', distinguishing it from higher-effort siblings. The purpose is unambiguous and actionable.

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 specifies when to use this tool: 'when full deep analysis is not required', implying it is the correct choice for low-stakes or quick reasoning tasks. It doesn't name specific alternatives, but the level distinction (low vs. high/medium) is implied via sibling names. This is clear contextual guidance, though it lacks explicit 'when not to use' or a direct pointer to 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It goes beyond the name by disclosing expected runtime (~50s) and describing the reasoning approach (problem decomposition, evaluation of alternatives, coherent decision-making). However, it does not disclose output shape, failure modes, or whether the reasoning result is deterministic, leaving meaningful gaps for a zero-annotation 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?

The description is efficiently written — two sentences that front-load the tool's identity and purpose, followed by the runtime signal. No wasted words or filler. The expected runtime line earns its place as a practical cost indicator. Slightly more structure (e.g., separating purpose from usage) could improve scannability but is not 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?

An output schema exists and no nested objects are involved, so return value documentation is partially offloaded. However, the definition's main gap is the undefined task parameter semantics and missing guidance on how detailed the input should be, which is critical for a delegating tool. The complexity is moderate and useful context is present, but the input contract is underspecified, keeping this at a 3.

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% — the task parameter's description is empty. The description implies the payload includes a task ('handle moderate to complex tasks') but never specifies what a good task string should contain, its expected granularity, or formatting. With a single crucial parameter and zero schema coverage, the description should compensate far more than it does.

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 is a 'medium-effort reasoning agent' handling 'moderate to complex tasks' with structured multi-step thinking, which distinguishes it from reasoningdelegationhigh and reasoningdelegationlow by effort tier. The verb+resource is reasonably clear (delegate reasoning at medium intensity), though it doesn't crisply define what the agent physically returns or produces, so it falls just short of a 5.

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 suitability guidance: 'suitable for tasks that require thoughtful reasoning but do not justify maximum computational intensity,' which tells the agent when this tier fits relative to the high/low siblings. The ~50s expected runtime offers a cost signal. It lacks explicit exclusions or named alternatives, but the effort-tier framing makes the when-to-use decision reasonably inferable.

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 exist, so the description carries the full behavioral burden. It does disclose meaningful process behavior (compares sources, validates information, highlights uncertainties) and a concrete runtime expectation (~60s). However, it does not state whether the operation is read-only, how results are delivered (beyond 'structured insights'), or how failures/source unavailability are handled.

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 action and each subsequent sentence adds distinct value (subtopics, validation, open questions). The runtime note is a useful trailing detail. It is slightly list-heavy, but there is 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?

For a single-parameter tool with an output schema present, the description covers the process, the nature of outputs, and timing, which is reasonably complete. It does not cover failure modes, limitations of the research, or how the output schema maps to the described 'insights and open questions,' leaving some ambiguity for an autonomous agent.

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% — the 'topic' property has an empty description — so the description must compensate. It only repeats 'on a given topic,' adding virtually nothing about topic specificity, length, scope, or how to phrase the research query well. The nested payload structure is left to the schema 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?

The description uses a clear verb-resource pair ('Conducts iterative internet research on a given topic') and enumerates concrete outputs (key terms, subtopics, facts, studies, current developments, insights, open questions). However, it does not differentiate from research-flavored siblings such as scientificresearchagent, exploitagent, or firstprinciplesanalyst, so an agent cannot readily pick between them based on this text alone.

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 closing phrase 'to support the Main Agent’s understanding' gives only implied context for when to use it. There is no explicit when-to-use/when-not-to-use guidance, no stated exclusions, and no reference to alternatives (e.g., scientificresearchagent for domain-specific work). With several overlapping research tools, this is a real gap.

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 present, the description carries the full behavioral burden, and it does so well by enumerating what is scanned and what the final report contains. It also discloses the expected runtime of ~180s. It stops short of stating whether the scan is strictly read-only, what authentication is required, or what happens if the site is unavailable, but overall behavior is clearly conveyed.

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 compact and front-loaded: the core action appears first, supported by a concise checklist of scanned elements and the final deliverable. The runtime note is a single useful addition with no filler, making every sentence purposeful.

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 a specialized audit agent with an output schema, the description gives sufficient high-level context: what it scans, what it returns, and how long it takes. The main gap is the under-specified URL parameter and lack of any prerequisite or limitation note, which prevents it from being fully complete for 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?

The schema provides only a single 'url' property with an empty description, and the tool description does not compensate for the 0% schema description coverage. The description mentions auditing sites, which implicitly points to the URL parameter, but it does not clarify expected URL format, whether the URL must be a domain root or any public page, or how it should be passed inside the payload.

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 opens with a specific verb and resource: it conducts an evidence-based audit of websites against current Russian legislation. It further clarifies scope by naming scanned artifacts (public pages, forms, cookies, documents, payment scenarios, external services) and the deliverable (report with risks, sources, remediation plan), making it distinguishable from generic research or browsing siblings.

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 intended use case is implied strongly: use this tool when a Russian-legal compliance audit of a website is needed. However, the description does not explicitly say when not to use it, does not mention prerequisites such as site accessibility or ownership, and names no alternatives 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?

With no annotations provided, the description carries the full disclosure burden and handles it well: it reveals the sandbox is isolated and stateless, that outputs are concrete and structured, that it identifies limitations of what is achievable, and even discloses an expected runtime of ~45s. For a stateless sandbox with no side effects to report, this is a strong disclosure, though it doesn't address output schema shape or error behavior.

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 action, flows logically through outputs and constraints, and tucks the runtime estimate into a clean final line. It's about three substantive sentences plus the runtime note — appropriately sized, with no filler 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?

An output schema exists, so explaining return values is not strictly required, but zero annotations and 0% schema coverage on the single parameter mean the description must bridge the gap. It covers purpose, constraints, and runtime well, but the unresolved question of what task_description must contain leaves the definition incomplete for safe 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 coverage is 0% — the only field, task_description, has an empty description. The description mentions executing Python/JS code but never clarifies whether task_description should contain raw code to run or a natural-language description of a task for the agent to solve. This is genuine ambiguity for the sole required parameter, and the description does not compensate for the empty schema field, so an agent may populate it incorrectly.

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 states a specific verb and resource ('Executes Python or JavaScript code in an isolated, stateless sandbox') with explicit purposes (validate, test, solve tasks). This distinguishes it from siblings like softwareengineeringexpert by emphasizing actual code execution in a sandbox. It's clear, though it doesn't explicitly name which sibling it replaces or contrasts with.

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 gives useful context — use it when you need concrete, structured outputs from actual execution and to verify what's achievable within sandbox constraints. However, it never names alternatives like softwareengineeringexpert or testagent, nor does it state when NOT to use this tool. The 'Supports the Main Agent' line hints at its role as a verifier, but exclusions and alternative routing are absent.

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 burden. It discloses an expected runtime of ~60s and the output nature (structured, expert-level insights), which is helpful. However, it does not mention any side effects, data access requirements, or limitations, leaving gaps in behavioral transparency 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.

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

The description is concise, with the core purpose stated in the opening sentence and supporting details in two more sentences. The runtime note is useful. No redundant information is present, and the structure flows logically from purpose to capabilities to expected behavior.

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?

While the description covers the tool's purpose and provides a runtime estimate, it omits essential context about the input parameter. With zero parameter description coverage and no guidance on usage boundaries, the description is not complete enough for an agent to correctly invoke this tool. The presence of an output schema partially compensates for return-value documentation, but the input semantic gap remains.

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 'task_description' has an empty description in the schema (coverage 0%), and the tool description offers no additional guidance on how to formulate the task description. The agent receives no semantic information about this parameter, making it impossible to know what content or format is expected. This is a critical gap.

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 verb 'Analyzes' and the resource 'tasks, questions, or problems', and specifies the scientific perspective and academic literature focus. It distinguishes itself from generic research agents by emphasizing 'distinguishing established findings, emerging evidence, and uncertainties', though it does not explicitly name a sibling alternative, leaving some ambiguity against 'researchagent'.

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 scientific use cases ('strictly from a scientific perspective', 'academic literature') but provides no explicit when-to-use versus when-not-to-use guidance. It does not reference any sibling tools or exclusions, leaving the agent to infer when this specialist is appropriate over the many other available 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?

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 provided, the description carries full responsibility for disclosing behavioral traits. It does add useful details: sponsored products are labelled and only shown when relevant, and the expected runtime is ~15 seconds. Yet it does not clarify whether the tool performs any side effects (e.g., placing orders, writing data) or if it is purely read-only/generative, leaving some uncertainty.

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 sentences plus a runtime note. It front-loads the core purpose and then lists concrete deliverables. Each phrase adds value, though the list of outputs could be slightly more compact. Overall, it is well-structured and not verbose.

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 is moderately complex with several mentioned outputs, and the description covers the key deliverables and a behavioral quirk. However, it omits critical how-to details about the query parameter and does not specify any limitations or error scenarios. The presence of an output schema helps, but the description alone is not fully complete for a tool with a single ambiguous input.

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 for the single 'query' parameter. The description mentions the types of information (fridge, guests, cuisine, time, budget) but never maps them to the query string format, nor does it explain how the query should be structured. This leaves the agent guessing about how to populate the payload.

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 states a specific verb ('creates') and a clear resource ('a complete home restaurant experience'), then enumerates concrete deliverables: menus, missing ingredients, shopping handoff, guest table passport, and friend message. This is richly detailed and clearly distinguishes the tool from sibling agents like financial_agent or researchagent.

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 when to use the tool by naming the inputs (fridge contents, guest count, cuisine, time, budget) and the outputs, making its purpose clear. However, it provides no explicit guidance on when NOT to use it or which sibling tools serve as alternatives. No exclusions or comparison to other agents are given.

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 conveys several key behaviors: sponsored products are always labelled and only shown when genuinely suitable, cart changes require explicit user approval, and an expected runtime of ~15s. These operational details exceed what is minimally expected.

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: three clear sentences plus a runtime note. The main purpose is front-loaded, and constraints (sponsored products, cart approval) are succinctly listed without unnecessary detail.

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 core functions and constraints well, and the presence of an output schema reduces the need to explain return values. However, it lacks explicit input expectations and usage guidance, making it incomplete for a complex multi-step 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?

The schema has a single required 'query' field with an empty description (0% coverage). The tool description implies the query is a food-related request by context, but it never explicitly explains what the query parameter should contain, leaving some 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's functions: tracking inventory, considering restrictions, and preparing recommendations. It is specific enough to distinguish from sibling agents like silpo_home_restaurant, though it is a composite of several actions rather than a single verb-resource statement.

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. There are no conditions for selection, exclusions, or mentions of sibling agents, leaving the agent to infer appropriateness.

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 burden. It discloses that the tool cannot execute code and has an expected runtime of ~50s, and commits to 'without speculation.' It does not explicitly state whether it modifies anything, though the read-only nature is implied. The runtime and no-speculation traits are useful context beyond the name.

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, using two short paragraphs. The main purpose is stated first, and the runtime note is separate. There is no redundant content, and every sentence adds information. It could be more structured, but it's appropriately brief.

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 has a single undocumented parameter and no usage guidance. While there is an output schema (so return format may be covered), the description lacks instructions on how to formulate the task description for best results. For an agent with one complex parameter, this is incomplete.

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 sole parameter (payload.task_desciption). The parameter name hints at a task description, but the description never connects it to the expected input. With zero schema coverage, the description should compensate, but it doesn't, leaving the caller to guess what to provide.

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 investigation areas (architecture, dependencies, patterns, quality) and outputs (insights, trade-offs, recommendations). It distinguishes from code execution by noting 'Can't execute code.' However, it does not explicitly name sibling tools for differentiation, so it's clear but not maximally 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?

The description implies it is used when engineering guidance is needed, but provides no explicit when-to-use or when-not-to-use guidance, nor does it reference alternatives. The 'Supports the Main Agent' suggests a role but not selection criteria. No exclusions beyond 'Can't execute code,' which is a capability limit rather than usage 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 provided, the description carries the full burden. It discloses key behavioral traits: it only suggests plans and does not independently execute or delegate, and it provides an expected runtime of ~30s. It does not mention whether it has side effects, requires permissions, or whether outputs are deterministic, but the output schema partially addresses 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 concise: three sentences plus a runtime note, with the core function front-loaded and limitations clearly separated. It avoids redundancy and every sentence adds value, though it could be slightly more structured with explicit 'input' and 'output' labels.

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 moderate complexity, one parameter, and existing output schema, the description covers purpose, limitations, and runtime adequately. It lacks explicit details on how the plan is returned, but that is presumably in the output schema. The description is sufficient for an agent to call it correctly in most planning scenarios.

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 implies the single parameter, task_description, should contain the complex user request or problem, but does not explicitly reference the parameter name or provide formatting/length guidance. This is adequate for a single self-descriptive parameter but not thoroughly detailed.

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 user requests, and specifies what it produces: task breakdowns, phase sequencing, and knowledge flow. It also distinguishes itself by explicitly stating it does not execute or delegate tasks, setting it apart from sibling agent tools that likely do execute.

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 indicates the tool is used for complex user requests/problems and that it supports the Main Agent in preparing plans, which implies when to use it. However, it does not explicitly name alternative tools or provide clear 'use this instead of X' guidance, leaving some inference to the agent.

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 provided, the description carries the full burden of behavioral disclosure. It does disclose that the tool delegates a task and gives a runtime estimate (~30s), which is some behavioral information. However, it doesn't mention side effects, whether the operation is synchronous or asynchronous, or what happens to the delegated task, 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 concise, with only two short sentences and the main purpose front-loaded. However, it is under-specified for the tool's complexity—it omits essential information about inputs and behavior. While concise, it is not appropriately sized because it lacks critical detail.

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 open-ended payload schema, the absence of annotations, and the existence of an output schema, the description is incomplete. It does not explain what should go into the payload or what the agent does with it. The expected runtime is useful but insufficient for an agent to correctly invoke the tool without further assumptions.

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 compensate. It doesn't explain the 'payload' parameter at all, which is the only required parameter. Since the payload is an open object with additionalProperties, the description should provide hints on its expected content or format, but it simply says 'delegates a task' without clarifying what should be passed.

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. It distinguishes from sibling agents by naming the agent, though it doesn't specify what TestAgent actually does, which slightly limits differentiation. Overall, the purpose is evident.

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 the many sibling agent tools. The description only mentions an expected runtime, which is not usage guidance. There is no mention of conditions, prerequisites, or when an alternative agent would be more appropriate.

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.