glimind

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

With no annotations, the description fully discloses behavior: returns a verdict, success rate, latencies, error signatures, breakage info. Also states it's 'Cheap and fast; safe to call routinely,' assuring the agent of low cost and safety.

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

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

The description is concise and well-structured: opens with a directive, explains return values, gives usage advice, and assures safety. Every sentence adds significant value without redundancy.

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

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

Given the tool's simplicity (1 parameter, no output schema), the description is complete. It covers what the tool does, what it returns, and when to use it, meeting all needs 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?

The schema already describes the 'toolId' parameter, but the description adds valuable context on the format ('server id' or 'server/tool_name'), which is not in the schema description. This extra guidance helps the agent provide correct input.

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: checking if an external MCP tool or API is working right now. It specifies the action ('check') and resource ('tool reliability') and distinguishes from sibling tools by focusing on real-time health status.

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 to 'Call this BEFORE invoking any external MCP tool or API' and provides concrete use cases: deciding whether to call a tool, choosing fallbacks, tuning timeouts and retries. Also mentions benefits ('prevents wasted calls').

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. It explains the tool returns tool ids ranked by semantic similarity and filtered to live tools, which is sufficient for a discovery tool. However, it does not disclose error handling or edge cases, but such details are less critical for this purpose.

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 that front-load purpose and usage, then contrast with siblings. Every sentence adds value with no redundancy.

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

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

Given the tool's simplicity (3 params, no output schema), the description is largely complete. It covers purpose, when to use, examples, and output hints. It could specify the output format more precisely, but 'returns ready-to-use tool ids' is adequate for context.

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

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

Schema coverage is 100%, so baseline is 3. The description reinforces parameter meaning (e.g., plain-language query, default limit 8, max 25) but does not add new details beyond what the schema already provides in property 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: to find LIVE tools based on a plain language task description. It distinguishes itself from sibling tools 'find_alternatives' and 'find_related_tools' by noting they require a tool identifier, whereas this tool takes a free-text query and is the entry point for new 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 instructs when to use ('when you do NOT yet know which tool to use') and when not to (contrasts with alternatives requiring a tool id). Provides examples like 'send a slack message' and clarifies it ranks by semantic similarity and filters to live tools.

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

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

No annotations provided, so description carries full burden. It explains the ranking ('ranked by capability similarity and real traffic') and that results are ready-to-use. Lacks mention of caching or freshness, but covers key behavioral traits.

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 purpose and usage. Every sentence adds value, no wasted words.

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, so description explains return: 'Returns ready-to-use tool ids you can switch to immediately.' Sufficient for simple tool with 2 params and no nested objects. Could detail output structure more, but 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 coverage is 100%, so baseline is 3. Description adds no extra meaning: for 'task' it repeats 'Optional coarse task tag.', and for 'toolId' it matches schema. No additional context 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 'Find LIVE tools that do a similar job to a given tool', specifying verb and resource, and distinguishes from sibling tools like 'find_related_tools' by emphasizing 'LIVE' and 'working substitute'.

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 when to use: 'call this when a tool is down, rate-limited, or missing, to get a working substitute'. Provides clear context and purpose.

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. It discloses that results are based on 'real usage' data, indicating a data-driven approach. However, it omits details like whether results are deterministic, how data is aggregated, or any security/reliability implications.

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

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

The description is two sentences: first states purpose and data source, second gives use cases. No wasted words, information is front-loaded and easy to parse.

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

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

For a simple tool with one required param and no output schema, the description covers the core purpose, data source, and use scenarios. It could mention the output format (e.g., list of tool names) but is not incomplete given the lack of 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?

The only parameter, toolId, is described as 'The tool to find companions for.' Schema coverage is 100%, so baseline is 3. The description adds no extra meaning beyond the schema, which is adequate but not enhanced.

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 discovers tools commonly used together with a given tool, based on real usage. It distinguishes itself from siblings like 'discover_tools' (broader) and 'find_alternatives' (replacements) by focusing on complementary co-use.

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

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

The description provides explicit use cases: finding complementary tools for a task or discovering better-fitting tools. While it doesn't explicitly state when not to use it, the context is clear and aligns with the tool's purpose.

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

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

No annotations, so description carries full burden. Discloses cursor advancement on each poll, return fields (tool id, severity, link), and implies read-only fetching. Does not discuss rate limits or error handling, but sufficient for understanding core 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?

Three concise sentences: first defines core action and cursor, second gives usage context, third describes return. Every sentence essential, no redundancy, front-loaded with main action.

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, description explains return fields. For a simple poll tool, it provides sufficient context: how to use, what happens on each call (cursor advances), and integration with watch_tool. Could mention token expiration but still complete for basic usage.

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

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

Schema coverage is 100%, but description adds context that token acts as a cursor from watch_tool, and explains polling behavior. This supplements the schema descriptions, adding value beyond parameter names.

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 it fetches new reliability alerts for a subscription token, specifies cursor behavior, and differentiates from sibling watch_tool by stating 'Use this after watch_tool without a webhook'.

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 to use after watch_tool without a webhook for reacting to outages/recoveries. Implies alternative use of webhooks with watch_tool but does not explicitly state when not to use. Provides clear context for usage.

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

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

Discloses privacy-preserving nature (value-free skeletons, never real values), ranking by recent success, and inclusion of success rate and latency. No annotations provided, so description carries full burden and does well.

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?

Concise single paragraph of 4 sentences, front-loading purpose and usage, with no redundant 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?

With no output schema, the description thoroughly explains what is returned (shape skeletons, privacy, ranking, success rate, latency). Adequate for the tool's low complexity.

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

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

Schema coverage is 100%, so baseline 3. Description reinforces the purpose of each parameter but adds minimal new detail beyond the schema's 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 retrieves a currently-working call pattern for a tool, distilled from real successful calls. This distinguishes it from siblings like discover_tools or 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?

Explicitly advises to use when unsure how to shape inputs or after a tool call failed, with optional filtering. Lacks explicit when-not-to-use cases, but guidance is 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 provided, so description carries full burden. Discloses it's backed by real-usage outcome data and catches breakages before status pages, implying near-real-time read-only listing. No destructive implications, 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?

Two sentences with no waste. First sentence states core purpose and ordering; second adds usage guidance and data-source credibility. Front-loaded and 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?

For a simple list tool with one optional parameter, no output schema, and no annotations, the description provides purpose, ordering, data source, and usage guidance. Lacks return format but acceptable given specification.

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% (single limit parameter with description 'Max results (default 25).'). The description adds no extra parameter-level detail beyond the schema, meeting the baseline expectation for high coverage.

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 lists tools with dropped success rates due to outages/regressions, newest and most severe first. This specific verb+resource distinguishes it from siblings like check_tool_reliability (targets single tool) and discover_tools (general discovery).

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 this to steer away from broken tools or monitor health of dependencies. Provides clear context but no explicit exclusions (e.g., when to use check_tool_reliability instead), though sibling names imply 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, the description carries the burden. It states the verdict output (proceed, proceed_with_caution, avoid) but does not disclose side effects, authentication needs, or error handling. It implies read-only behavior but lacks full transparency for a safety-conscious agent.

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

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

The description is concise and front-loaded with the purpose. It uses efficient language but could be slightly more structured, e.g., explicitly listing the verdict options. Still, it earns its place without 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?

Given a simple single-parameter tool with no output schema, the description fully covers what the tool does, what it returns, and when to use it. It is complete for the tool's complexity.

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

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

Schema coverage is 100% with a single parameter 'toolIds' described as 'Tool identifiers you plan to use (up to 50).' The description adds context about passing every tool but does not significantly enhance understanding beyond the schema.

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

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

The description clearly states the tool's purpose: 'Pre-flight a WHOLE task in one call' to get a compact verdict and recommendation for each tool. It distinguishes from checking tools one at a time, likely referencing sibling tools 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 once at the beginning of a multi-tool task instead of checking tools individually. It provides clear context but does not explicitly list when not to use it or name alternatives, though the context implies the alternative is checking one by one.

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

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

Discloses that it returns a health verdict, recipe, and live alternatives, and indicates whether the tool is down or risky. No annotations are present, so the description adequately covers the behavioral aspects for a non-destructive meta-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?

Three concise, front-loaded sentences with no wasted words. Each sentence serves a distinct purpose: what it does, how it combines calls, and what information it provides.

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 adequately explains the return values (verdict, recipe, alternatives). However, it lacks details on how alternatives are identified or what 'coarse task tag' entails fully.

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

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

Schema coverage is 100%, but the description adds value by explaining that 'task' tailors the recipe/alternatives, going beyond the schema's basic description.

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

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

The description clearly states the tool is a preparatory step before using another tool, returning health status, recipe, and alternatives. It uses specific verbs ('prepare', 'returns') and distinguishes it from similar sibling tools by consolidating 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?

Explicitly says 'Make right before using a tool' and 'Use this instead of calling check + recipe + alternatives separately', providing clear when-to-use instructions and naming 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 full burden. It discloses the tool's return value in detail: verdict, reliability score, input schema, recipe, call hints, and fallback chain. However, it does not explicitly state whether the tool is read-only or has side effects. Given it's a meta-tool for routing, it's likely safe, but the description could be clearer on that point.

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 given the amount of information it conveys. It front-loads the core action and then lists details. Minor room for trimming, but overall efficient and well-structured.

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?

Since there is no output schema, the description fully explains what the tool returns: verdict, reliability score, input schema, recipe, call hints, and fallback chain. It also covers usage context. For a routing tool, this provides complete context for the agent to understand the tool's behavior.

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

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

Schema coverage is 100%, but the description adds value beyond the schema: it clarifies that 'task' should be in plain language, notes 'category' is optional and narrows choice, and provides default (3) and maximum (8) for 'fallbacks,' which are not in the schema. This extra information aids correct invocation.

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: given a plain-language task, it returns the single best live tool to accomplish it. It explicitly distinguishes itself from siblings by claiming to replace discover_tools, check_tool_reliability, prepare_tool_call, and find_alternatives in 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?

Provides explicit guidance: 'Call this at the start of a task when you don't already know which tool to use.' This tells the agent exactly when to use it and implies when not to (if the tool is already known, use it directly).

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 mentions cancellation but lacks details on success/failure, side effects, or permissions. Minimal disclosure.

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?

Single sentence, 11 words. Extremely concise and efficient with no wasted words.

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 simple one-parameter tool with no output schema, the description adequately covers what it does and how to use it. Could mention return value, but not essential.

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 parameter 'token' has a description. The description adds context that the token comes from watch_tool, which is helpful and goes 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 verb 'Cancel' and the resource 'subscription created by watch_tool'. It is specific and distinguishes from the sibling watch_tool.

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 states when to use (when you have a subscription token from watch_tool and want to cancel it). No explicit when-not or alternatives, but context is 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 provided, so description carries full burden. It explains push vs pull behavior, auto-detection of webhook type, and return of a token for pull mode. It does not detail side effects like subscription limits or potential overwrites, but the core 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 concise at four sentences, each serving a distinct purpose: aim, modes, use case, and return value. No redundancy.

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

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

With no output schema, the description adequately covers the return (a token) and the two modes. It could mention error handling or subscription management details, but for a tool with 5 optional parameters, it is sufficiently complete.

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

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

Schema coverage is 100% with clear parameter descriptions. The description adds value by explaining the usage pattern for webhook (auto-detected) and token mode, and the relation to get_notifications, going beyond the schema.

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

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

The description clearly states the tool subscribes to reliability alerts for tools or all critical outages. It distinguishes from siblings like get_notifications (poll) and unwatch (cancel), using specific verbs and resources.

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?

Describes two modes (push with webhook, pull with token) and when to call it (to be alerted on breakage/recovery). Implicitly contrasts with one-time check (check_tool_reliability) and polled notifications (get_notifications). Could explicitly mention alternatives, but context is clear.

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