glimind

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 tool's output (verdict, live success rate, latency, error signatures, endpoint, pricing, breakages), its non-destructive nature, and that it is cheap and fast. 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?

The description is relatively long but every sentence adds value. It is front-loaded with the main purpose and includes implementation details. Slightly verbose but still effective.

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 one parameter and no output schema, the description is very complete. It covers purpose, usage, return values, pricing, and even how to call the endpoint. No gaps left.

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 for the single parameter is 100%, so the schema already documents the parameter well. The description does not add new information about the parameter beyond what the schema provides, hence baseline 3.

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 checks if an external MCP tool or API is working right now, with a specific verdict and live metrics. It uses a specific verb ('check') and resource ('tool reliability'), and distinguishes itself from sibling tools by being a pre-call reliability check.

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 explicitly says to call this before invoking any external MCP tool or API, and provides guidance on using it to decide whether to call a tool, choose a fallback, or tune timeouts. It also notes it is cheap and safe to call routinely.

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 transparency burden. It explains the returned data (tool ids, schema, endpoint, pricing) and filtering (live, ranked). It doesn't explicitly state safety (read-only), but the nature implies no side effects. The description does not conflict with any annotation.

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 somewhat long but every sentence adds value. It front-loads the main purpose and use case. Could be slightly more concise, but it is well-structured and comprehensive.

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 output schema and no annotations, the description covers input, output, behavior, and use case completely. It mentions it's the discovery entry point for new tasks. All necessary information for an AI agent to select and invoke the tool is present.

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

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

Schema coverage is 100%, and description adds meaning beyond schema by providing examples for query ('send a slack message', 'convert currency') and indicating that query is a plain-language description. It also explains the optional limit and category, adding context not in 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 the verb 'Find LIVE tools' and the resource 'tools that can accomplish a task'. It distinguishes itself from sibling tools find_alternatives and find_related_tools by noting that those require a known tool id, while discover_tools takes a free-text query.

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 'call this when you do NOT yet know which tool to use', providing clear context. It also contrasts with siblings that need a tool id, giving when-not guidance implicitly.

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 returns ready-to-use tool IDs with connection endpoints, and that results are ranked by capability similarity and real traffic. It does not mention side effects, auth, or error handling, but for a retrieval tool this is adequate. No contradiction with missing annotations.

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

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

The description consists of two sentences with no wasted words. The first sentence clearly states purpose and usage, the second explains what is returned. It is front-loaded with the most critical 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 the tool has only 2 parameters with full schema coverage and no output schema, the description is fairly complete. It explains the return value and ranking criteria. It could be improved by mentioning pagination or error handling, but for a simple retrieval tool, it is sufficient.

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 100% (both parameters have clear descriptions). The description does not add new meaning beyond what the schema provides; it only mentions the tool's purpose which implicitly relates to the required parameter. Baseline score of 3 is appropriate.

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: find LIVE alternatives for a given tool. It specifies the use case when a tool is down, rate-limited, or missing, and distinguishes from siblings by emphasizing 'LIVE', ranking by capability and traffic, and returning connection endpoints for immediate switch.

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 explicitly states when to use this tool: 'when a tool is down, rate-limited, or missing, to get a working substitute'. It implies when not to use (e.g., for general discovery), and context from sibling names like 'find_related_tools' and 'discover_tools' provides 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?

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It only states 'from real usage' but does not explain if the tool is read-only, requires authentication, has rate limits, or what the output format is. This lack of detail leaves the agent uncertain about side effects and expectations.

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, front-loaded with the primary purpose, and every word contributes meaning. It is efficient 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 has no output schema and no annotations. The description omits what the return value looks like (e.g., list of tool names, with confidence scores?), making it incomplete for the agent to anticipate the result. For a tool relying solely on description, critical details about output are missing.

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 'toolId' has a description 'The tool to find companions for,' which adds meaningful context beyond the schema's type constraint. Since schema coverage is 100%, baseline is 3, and the description adds moderate value by clarifying the role of the parameter.

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: discovering tools commonly used together with a given tool from real usage. It uses specific verbs ('Discover') and resource ('tools that other agents commonly use TOGETHER'), and distinguishes from sibling tools like 'find_alternatives' and 'discover_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 explicitly advises when to call the tool: 'to find the complementary tools you'll likely need next for a task, or to discover better-fitting tools you didn't know existed.' It provides clear context but does not mention when not to use it or specify exclusions relative to 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?

With no annotations, the description explains the cursor advancement (events since last poll) and return fields (tool id, severity, link). It lacks details on rate limits or authentication requirements beyond the token, but the core behavior is 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?

Two sentences, front-loaded with the action and resource, no wasted words. Each sentence adds meaningful 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 description explains the polling state (cursor advancement), return format, and prerequisite (watch_tool). For a simple tool with no output schema, it is sufficiently complete for an agent to use correctly.

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 covers both parameters (limit, token) with descriptions. The description adds value by explaining the token's origin from watch_tool and the default limit behavior, going beyond 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 the tool fetches new reliability alerts for a subscription token, specifying the resource (alerts) and action (fetch). It distinguishes itself from sibling tools like watch_tool (setup) and unwatch (teardown) by focusing on polling.

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 recommends using this after watch_tool without a webhook to react to outages/recoveries, and notes that no public endpoint is required. This provides 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 discloses privacy-preserving nature (value-free skeletons), ranking by recent success, and inclusion of success rate and 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?

Description is a single paragraph of about three sentences, front-loaded with purpose. It is concise and covers key aspects, though slightly more structure could improve readability.

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?

No output schema, but description explains return value shape (skeletons with success rate and latency). Both parameters are described well, and context signals indicate high schema coverage. Adequate for a lookup 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 100%, so baseline is 3. Description adds that 'task' is optional for narrowing recipe, but does not add significant meaning beyond 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?

Description clearly states the tool gets a currently-working call pattern for a tool, with specific verb 'Get' and resource 'working recipe'. It distinguishes itself from sibling tools like discover_tools and preflight_tools by focusing on successful call patterns.

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 two usage scenarios: when unsure how to shape inputs, and after a tool call failed. Provides clear context for use, though does not explicitly mention when not to use it.

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 discloses the key behavioral traits: it returns a memoryBlock to be saved, it is a read-once instruction tool, and it covers the tool set, pricing model, and workflow. 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?

The description is somewhat long but well-structured with clear instructions and front-loaded purpose. Every sentence provides value, though minor trimming could improve conciseness.

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 no parameters, no annotations, and no output schema, the description is exceptionally complete. It explains what the tool does, when to use it, what it returns, and how to handle the output, leaving no 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?

No parameters (0 params), baseline is 4. The description does not need to add parameter info since there are none.

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: 'Read this ONCE to learn how to use Glimind fully and optimally'. It distinguishes itself from sibling tools like route_task by specifying that this tool is for initial learning and setup, while route_task should be used for actual tasks.

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 provides usage context: 'Call this at the start of a session or any time you're unsure how to get external data.' It also instructs to consult Glimind FIRST via route_task instead of guessing tools, giving clear when-to-use and 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?

No annotations are provided, so the description carries the full burden. It mentions being backed by real-usage outcome data and catching breakages early, implying a read-only query. However, it doesn't explicitly state safety or non-destructive nature, leaving room for 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 three sentences with no redundancy. It front-loads the purpose and provides usage rationale efficiently.

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?

With no output schema, the description should hint at return format but does not. It mentions listing tools but omits what fields are returned. However, for a simple list tool, it is partially adequate.

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 100% for the single 'limit' parameter. The tool description does not add additional meaning beyond the schema's parameter description, so baseline 3 is appropriate.

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 lists tools with dropped success rates (breakages), ordered by newest and most severe. It distinguishes from siblings like 'check_tool_reliability' by focusing on breakages from real-usage data.

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 explicitly says 'Call this to steer away from tools that are currently broken, or to monitor the health of tools your task depends on.' This provides clear context for when to use, though it lacks mention of when not to use or explicit 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?

No annotations are provided, so the description carries the full burden. It discloses that the tool returns a compact verdict and recommendation, but does not mention any side effects, permissions required, or error handling. It is adequate but lacks deeper behavioral context.

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 consists of two sentences that are front-loaded with the core purpose and usage. 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 no output schema, the description explains that the tool returns a verdict and recommendation for each tool, listing the three possible verdicts. It does not detail the exact structure or error handling, but it is fairly complete for an agent to understand.

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 'toolIds' is described in the schema as an array of strings. The description adds the useful constraint 'up to 50', which is not in the schema. However, it does not elaborate on the expected format of identifiers further.

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 pre-flights a whole task by checking multiple tools in one call, returning a verdict and recommendation for each. It distinguishes itself from checking tools one at a time and differentiates from siblings like check_tool_reliability.

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 explicitly says to call this at the beginning of a multi-tool task instead of checking tools one at a time, providing clear context. However, it does not explicitly state when not to use it or list alternatives beyond the implied comparison.

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 the description must fully disclose behavior. It describes returns (verdict, recipe, alternatives) but does not mention potential side effects, permissions, or whether it is read-only. While it explains what it does, it lacks depth on behavioral traits such as idempotency or required 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 consists of two concise sentences with no wasted words. All information is front-loaded and every sentence adds value: the first explains the tool's purpose, the second gives usage guidance.

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 no output schema, the description adequately covers the return values (verdict, recipe, alternatives) and the tool's role in the workflow. It addresses sibling tools indirectly. Slightly incomplete regarding edge cases or failure modes.

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

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

Schema coverage is 100% and the description repeats the schema's parameter descriptions almost verbatim (e.g., 'Optional coarse task tag'). It adds no new meaning beyond what the input schema already provides. Baseline 3 is appropriate.

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

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

The description clearly states it is the call to make before using a tool, returning health, a recipe, and alternatives. It distinguishes itself from siblings like check_tool_reliability, find_alternatives, and get_working_recipe by consolidating their functions into one call.

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 instructs 'Use this instead of calling check + recipe + alternatives separately.' and explains when to proceed or use alternatives. Provides clear context for when to use this tool versus its 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?

With no annotations, description carries full burden. States it's free, keyless, privacy-preserving, and that the SDK's execute() does this automatically. No contradictions, but could mention idempotency or rate limits; still strong 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?

Description is a single paragraph but well-structured: starts with purpose, then value, then instructions. Could be slightly more front-loaded, but no wasted sentences. Efficient and 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?

Given 5 parameters, no output schema, and no annotations, the description is fully complete. It explains purpose, when to use, what to send, what to avoid, and even provides a sibling reference (SDK's execute). No gaps for an agent to invoke correctly.

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

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

Schema coverage is 100%, baseline 3. Description adds value by specifying that toolId should come from route_task's chosen.toolId, errorClass should be normalized, and clarifies what NOT to send (inputs/outputs/prompts).

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 verb 'report' and resource 'outcome of a tool call', specifying fields to include and exclude. Distinguishes from siblings by focusing on feedback/learning after routing, unique among sibling 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?

Explicitly says 'Call it every time you use a routed tool' and explains it's free and keyless, providing clear context for use. Does not explicitly list when not to use, but the condition 'after calling a tool' is sufficiently bounded.

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 comprehensively describes what the tool returns (verdict, reliability, schema, recipe, hints, endpoint, pricing, fallbacks) and its behavior (single call replacing multiple). 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?

The description is verbose and packed with details, which is informative but could be more concise. It is front-loaded with the core purpose, making it structurally acceptable, but some redundancy exists.

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 complexity (7 params, no output schema), the description is highly complete. It covers input, output, filtering options, pricing, fallback chains, and even provides a use-case reminder. No critical 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 coverage is 100% with descriptions, but the description adds meaning by explaining parameter effects (e.g., view options, freeOnly, requireAutomatable) and how they influence results. It goes beyond basic schema documentation.

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 the tool's purpose: to return the single best live tool for a given task, along with all details needed to call it. It explicitly distinguishes from sibling tools like discover_tools, check_tool_reliability, etc., by noting it replaces multiple calls.

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 clearly states when to use: 'Call this at the start of a task when you don't already know which tool to use'. It also mentions alternatives and filters like freeOnly and requireAutomatable, providing clear decision 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 provided, so the description fully bears the burden. It details the behavior: returns endpoint, input schema, example args, free/paid info, how-to-pay, and fallbacks. It doesn't mention side effects (likely none), but is thorough for a read-like operation. Minor gap: no explicit mention of idempotency or lack of mutations.

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 paragraph, front-loaded with the core purpose and then details. It's efficient but could be slightly more structured with bullet points for readability. Still, 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?

No output schema, but the description explains what each result contains. For a complex tool with 7 parameters, it provides enough behavioral and output context. It lacks explicit error handling or edge-case behavior, but overall complete for planning 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 100%, and the description adds significant context: 'compact' vs 'full' view, 'plain-language sub-tasks (max 10)', 'cap per-call price', and 'only consider FREE tools'. It enriches the schema with usage intent and 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?

The description clearly states it's a batch version of route_task, taking an array of sub-tasks and returning best live tools for each, collapsing multiple round-trips. This distinguishes it from the sibling route_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?

Explicitly advises using this tool 'at the start of a multi-step task to plan every step at once', implying it's for batch planning, while the singular route_task is for single steps. No exclusions or alternatives needed beyond that.

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 states the action (cancel subscription) but does not disclose side effects, idempotency, or error cases. For a simple cancellation, this is adequate but not informative.

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?

A single, front-loaded sentence with no wasted words. Every word earns its place, making it highly 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?

Given the tool's simplicity (1 param, no output schema), the description covers the essential purpose and parameter. However, it could mention whether the operation is safe or idempotent, especially without annotations.

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 mentions the token parameter but adds no meaning beyond the schema, which already describes it as 'The subscription token' with 100% coverage. Baseline 3 is appropriate.

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 'Cancel' and the resource 'subscription created by watch_tool', and implicitly distinguishes from siblings like watch_tool and get_notifications.

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 explicitly ties usage to prior use of watch_tool, providing clear context. However, it lacks explicit guidance on when not to use or alternatives, but the sibling list and context make it 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?

With no annotations, the description fully explains the behavior: subscribes to alerts, offers push (webhook) or pull (token) modes, auto-detects webhook type, and returns a token for management. It does not disclose limits or idempotency, but for a subscription tool the behavioral coverage is adequate.

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 4 sentences, concise and front-loaded with the primary purpose. Every sentence adds value without redundancy. The structure flows logically from what the tool does to how to use it.

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 output schema, the description mentions the return token but lacks detail on token format or management beyond 'poll with get_notifications'. It fails to explain the minSeverity parameter, which is important for controlling alert severity. The description is mostly complete for the two main modes but has 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?

The schema already covers all 5 parameters (100% coverage). The description adds value by explaining the webhook/token trade-off, auto-detection, and the relationship to get_notifications. However, it does not elaborate on the minSeverity parameter or the distinction between toolId and tools.

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 subscribes to reliability alerts for tools or all critical outages, with a specific verb 'subscribe' and resource 'tools'. It distinguishes from siblings like get_notifications (for polling) and unwatch (for unsubscribing) by explaining both push and pull modes.

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 the tool: to be notified immediately of breakages or recoveries. It contrasts push vs pull modes and references get_notifications for polling. However, it does not explicitly contrast with check_tool_reliability or list_breakages, leaving some ambiguity about when those alternatives are better.

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