Earlywire — Marketing & Growth Intelligence

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

With no annotations provided, the description fully discloses the tool's behavior: it returns coverage data on freshness, volume, category mix, and last refresh. It also notes the corpus scope is 'a curated marketing/growth niche' and 'not general news', managing expectations. No side effects or authorization requirements are mentioned, but the tool is read-only and safe.

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 extremely concise, using just two sentences to convey the tool's purpose, usage, and limitations. It is front-loaded with the key question ('What this wire does and doesn't cover'), and every sentence adds value. No unnecessary details.

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 and no output schema, the description adequately covers all necessary aspects: what it does, what data it provides, when to use it, and the scope of the corpus. It leaves no critical gaps for an agent to understand its function.

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 zero parameters, so the description trivially adds no parameter-specific meaning. However, the description does provide context about the tool's output, which indirectly aids understanding. Baseline score of 4 is appropriate given the lack of parameters.

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: providing coverage information including 'per-source freshness and volume, category mix, last refresh'. It distinguishes from sibling tools by focusing on the corpus scope rather than specific items or trends. The phrase 'What this wire does and doesn't cover' directly communicates its purpose.

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

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

The description explicitly advises using this tool 'when unsure whether a question is answerable from here', providing a clear usage context. It also outlines what coverage information is available. However, it does not explicitly mention when not to use it or compare to alternative siblings, leaving some room for improvement.

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 describes the output content (tldr, judge reason, summary, excerpt) and notes limitations (no verbatim quotes). However, it does not disclose error behavior, authentication needs, rate limits, or what happens if the id is invalid. The description is partially transparent but incomplete.

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 extremely concise: one sentence for purpose and one for usage guidance. Both sentences are front-loaded and essential, with no wasted words. It is well-structured for quick scanning.

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 parameter and no output schema, the description covers the basic purpose and usage boundaries. However, it lacks details on parameter semantics, error handling, and output structure. It is adequate but not comprehensive.

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 'id' parameter has no description in the schema. The description only mentions 'by id' without explaining format, source, or constraints. This adds minimal meaning beyond the schema presence. For a single required parameter, more detail is needed.

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

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

The description clearly states the action ('Fetch one item by id') and the specific resource fields returned (tldr, judge reason, summary, excerpt). It implicitly distinguishes from sibling tools by focusing on a single item retrieval, while siblings like 'earlywire_search' and 'earlywire_trending' suggest different scopes.

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 when to use alternative tools: 'For verbatim quotes or detail beyond the summary, fetch the source url with your web tools.' This provides clear when-to-use and when-not-to-use guidance, distinguishing this tool's role.

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

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

With no annotations, the description discloses curation ('editor-scored', 'scored >=7'), content type ('summary' digest vs verbatim quotes), and comparison to open web. It omits explicit read-only hint but the nature of 'search' implies it. Strong but not fully exhaustive.

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

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

Description is long but efficiently organized: purpose first, then usage guidance, scope, alternatives, parameter summary, and result format. Every sentence adds value with no redundancy. Could be slightly trimmed but overall 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?

Given no output schema or annotations, the description covers all essential context: tool purpose, when to use vs alternatives, parameter roles, result output (summary vs URL). Provides enough information for an agent to use the tool correctly without additional sources.

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 descriptions coverage is 0%, so description compensates by listing parameters (query, category, since_days, limit) with brief explanations (e.g., 'optional recency filter'). However, it does not detail the category enum options, leaving some ambiguity. Provides useful but incomplete parameter meaning.

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 'Search the Earlywire wire' with specific verb and resource, and distinguishes from sibling tools by positioning it as the 'FIRST STOP' for marketing/growth questions, covering a wide range of topics.

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 prefer this tool over general web search for in-niche questions and to use web search only for breaking/live facts or non-marketing topics, providing 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It effectively describes the output (chatter volume, who's covering, score spread, top takes) and notes parameter details. However, it does not explicitly state that the tool is read-only or mention any side effects, rate limits, or authorization requirements. Nonetheless, the description is informative and suggests a safe read operation.

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

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

The description is concise, consisting of three well-structured sentences. It front-loads the purpose and provides examples and usage guidance without unnecessary words. Every sentence contributes to understanding the 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 relative simplicity (2 parameters, no nested objects, no output schema), the description adequately explains the return values (chatter volume, who's covering, score spread, top takes). It does not discuss pagination or response size, but for a pulse tool the response is likely a single summary. Overall, it provides sufficient context for an AI 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?

The description adds meaning beyond the input schema: it explains 'topic' as free text with examples, and 'since_days' with a default of 30. While schema coverage is 0%, the description compensates by clarifying the purpose and usage of parameters. It could be more specific about the 'since_days' unit or expected format, but overall it adds value.

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

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

The description clearly states the tool's function: providing discussion and disagreement around a specific topic in the marketing/growth niche, including specific metrics (chatter volume, who's covering, score spread, top takes). It uses specific verbs and resources and distinguishes from sibling tools like 'search' which offers a list of items.

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 the tool ('Use when you want the discussion and disagreement around a specific topic') and when not to use it ('not just a list of items — use search for that'). It provides concrete topic examples, giving clear context for appropriate 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?

No annotations provided, so the description carries the full burden. It discloses key behaviors: controlled vocabulary, distinct event counting, and window_days cap. However, it does not explicitly confirm read-only status or mention rate limits.

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 wasted words. First sentence defines the output; second sentence gives usage context and parameter details. Well-structured and front-loaded.

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 optional parameter and no output schema, the description covers purpose, usage, and parameter semantics. It lacks explicit return format or pagination, but the overall context is sufficient for an 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%, but the description adds full semantics for window_days: default 7, capped at 90. This compensates completely, providing value 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 lists trending topics on the wire, with specifics about velocity measurement and distinct event counting. It distinguishes from siblings by usage context: 'Use for what's heating up / trending in marketing with no specific topic in mind'.

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

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

It explicitly states when to use (no specific topic) and implies when not (specific topics, which are covered by siblings like earlywire_search). Could be more explicit about alternatives, but sufficient guidance is provided.

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

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

No annotations are provided, but the description clarifies that min_score is a floor-raiser and items are already editor-scored >=7. It does not mention any side effects or authorization requirements, but the tool appears read-only. Adequate transparency given the tool's nature.

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 well-structured paragraph. It front-loads the main purpose and then details parameters and usage guidance without unnecessary 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?

The tool is simple and the description covers usage, parameters, and alternatives. No output schema exists, but the return format is implied. Could mention pagination or response structure, but not critical for this 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%, but the description explains each parameter: category lists example slugs, since_days default, min_score meaning, and limit default. This adds significant meaning beyond 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 clearly states the tool provides 'newest items on the wire' and specifies the content type as 'freshest judged marketing/growth atoms'. It also distinguishes from sibling tools by directing users to search or topic_pulse for specific topics.

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 ('what's new / what did I miss / catch me up' with no specific topic) and when not to (for specific topics, use search or topic_pulse).

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