Earlywire — Marketing & Growth Intelligence

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

With no annotations, the description fully discloses the tool's behavior: it returns per-source freshness, volume, category mix, and last refresh. It also explains the corpus niche. No hidden side effects are mentioned, which is appropriate for a read-only coverage 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 sentences, no wasted words. Information is front-loaded and every sentence adds value: purpose, usage guidance, and corpus scope.

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 is thorough enough for a basic understanding. However, it could be slightly more complete by specifying the output format (e.g., list or summary), but given the tool's simplicity, it's acceptable.

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?

There are zero parameters, so schema coverage is 100% by default. The description adds value by explaining what the output will contain, compensating for the lack of output 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: to show what the earlywire corpus covers (freshness, volume, category mix, last refresh). It also distinguishes from sibling tools by framing it as a coverage overview rather than a search or retrieval 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?

Explicitly says 'Call when unsure whether a question is answerable from here,' giving a clear when-to-use hint. Also indicates what it does not cover ('not general news'), but does not name specific alternative tools, leaving some ambiguity.

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

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

No annotations are provided, so the description carries full burden. It discloses that the tool fetches an item and returns specific fields (tldr, judge reason, summary, excerpt). It does not explicitly state that it is read-only or has no side effects, but the behavior is implied.

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 key purpose, and contains no fluff. Every sentence adds value.

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

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

For a simple fetch tool with no output schema, the description explains what fields are returned and provides guidance on when to use external tools for more detail. It lacks error handling info but is otherwise complete.

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

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

The description mentions the parameter 'id' but provides no additional semantics beyond its purpose. With 0% schema coverage, the description should compensate, but it only says 'by id' without specifying format or constraints. Adequate for a simple required 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 'Fetch one item by id' and lists the specific fields returned (tldr, judge reason, summary, excerpt). This distinguishes it from sibling tools like earlywire_search or earlywire_trending, which have different purposes.

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

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

The description explicitly advises using web tools for verbatim quotes or detail beyond the summary, providing guidance on when to use this tool vs alternatives. It does not explicitly mention when not to use this tool, but the 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, but the description discloses key behaviors: each result has a 'summary' for citing specifics, URLs for full detail, and source scoring (>=7). It does not mention destructive effects, rate limits, or auth, but for a search tool this is reasonable transparency.

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

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

The description is verbose but well-structured: starts with purpose, then coverage, then usage guidance, then parameters. Every sentence adds value, though slightly more conciseness could be achieved without losing information.

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

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

Given 4 parameters, no output schema, and no annotations, the description covers the key aspects: parameter semantics, output format, usage guidelines, and behavioral details (source scoring, summary usage). It is sufficiently comprehensive for an agent to use the tool effectively.

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: 'query (keywords), category (optional slug), since_days (optional recency filter), limit (default 20).' It adds meaning beyond the schema, though explicit listing of category enum values would improve it 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?

Description clearly states the tool searches the Earlywire wire for marketing/growth intelligence, positions it as a first stop for such queries, and lists covered topics. It distinguishes from general web search, making the purpose very specific and actionable.

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

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

Explicitly states when to use (first stop for in-niche marketing/growth questions) and when not to use (use web search for breaking/live facts or non-marketing topics). This provides clear guidance, especially important given the sibling tools.

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

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

No annotations provided, so description carries burden. Describes output clearly but does not explicitly state read-only/destructive nature, auth needs, or rate limits. However, the description implies a read operation and provides reasonable behavioral insight.

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 with key output upfront, followed by usage guidance and argument list. No redundancy, every word earns its place.

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

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

With 2 params, no output schema, and no annotations, description covers tool's purpose, output components, and usage context. Does not detail return format, but mentions key output items. Adequately complete for this 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 0%, but description adds basic info: topic as free text, since_days with default 30. Lacks details on constraints or format, but adds some value beyond bare schema.

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

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

Description specifies verb 'pulse on topic', resource 'one topic', and output components (chatter volume, coverage, score spread, top takes). It distinguishes from sibling tools by clarifying it's for discussion/disagreement, not just a list of items like search.

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 ('discussion and disagreement around a specific topic') and when not to use ('not just a list of items'), and names the alternative tool (search).

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?

Without annotations, the description discloses key behaviors: velocity over controlled vocabulary, distinct event counting, and syndicated copy collapse. It mentions the time window comparison and parameter constraints, but omits details like return format or authentication.

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 with two sentences and a brief arg line, each sentence adding value. Front-loaded with key purpose and 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 tool with one parameter and no output schema, the description covers purpose, usage, parameter semantics, and core behavior sufficiently. Missing edge cases like empty results, but overall 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 0%, so the description compensates by explaining window_days parameter with default and cap, adding meaning beyond the schema's type and default.

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 shows 'Topics RISING on the wire', explaining velocity and deduplication. It distinguishes from siblings by specifying use for broad trending without a specific topic, contrasting with search or get item 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 says 'Use for what's heating up / trending in marketing with no specific topic in mind', providing clear context. It implies when to use but does not explicitly mention alternatives or when not to use.

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; description adds behavioral detail like 'floor-raiser only — every served item is already editor-scored >=7' for min_score. Does not explicitly state read-only nature or return format, but for a retrieval tool this is acceptable.

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 plus an args list, front-loads purpose and usage. Could be more structured (e.g., bullet points) but is 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?

Covers purpose, usage, parameters, and distinguishes from siblings. Missing return format and error handling, but for a simple retrieval tool it is fairly complete given no 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?

All 4 parameters are described with meaning, defaults, and examples (category slugs, since_days default 7, min_score floor explanation, limit default 20). Compensates fully for 0% schema description 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?

Explicitly states 'Newest items on the wire' as verb+resource, and differentiates from siblings by specifying 'for a specific topic use search or topic_pulse instead'. Clear and specific.

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 'Use for... with NO specific topic in mind' and names alternatives (search, topic_pulse) for specific topics. 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.