Content Intelligence API

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

With no annotations, the description carries the full burden. It mentions pricing (pay per call or subscription), which is a behavioral trait, but does not disclose other traits like synchronous execution, rate limits, or error handling.

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

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

The description is concise, with one sentence dedicated to the tool's purpose and another to pricing. It front-loads the core functionality, though pricing details could be relegated to annotations.

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

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

Given the tool's complexity (analyzing multiple aspects) and lack of output schema, the description does not explain what results to expect (e.g., format, structure). This leaves the agent uncertain about how to handle the output.

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% (one parameter with description). The tool description adds meaning by listing the analysis types (summary, sentiment, etc.) but does not elaborate on each parameter beyond what the schema already provides.

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 explicitly states that the tool analyzes text for summary, sentiment, entities, topics, and classification. This clearly defines the verb and resource, and distinguishes it from siblings like sentiment_over_time which focuses only on sentiment over time.

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 no guidance on when to use this tool versus its siblings. It only mentions pricing, not usage context or exclusions. Siblings like extract_content or research_topic might overlap in purpose but are not compared.

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 must disclose behavioral traits. It mentions the core behavior (compare for similarities, etc.) and the cost model (0.01 USDC per call or subscription). However, it omits side effects, authorization needs, rate limits, or what happens on invalid input. The cost disclosure adds some transparency but not enough.

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, each serving a distinct purpose: first describes core functionality, second states pricing. No wasted words, well-structured and front-loaded with the primary purpose.

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

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

The tool has three parameters with full schema coverage but no output schema. The description does not explain return values, error conditions, or behavior for edge cases (e.g., invalid URLs). For a simple tool, it covers the basics but lacks completeness for agent 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%, so baseline is 3. The description adds clarifying context that sources can be URLs or text (already implied by schema descriptions) and mentions an optional aspect focus. This provides marginal added 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 it compares two sources (URLs or text) for similarities, differences, coverage, and bias. It uses a specific verb ('compare') and identifies the resource (sources), distinguishing it from siblings like analyze_text (single text analysis) or competitor_intel (competitive analysis).

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 no guidance on when to use this tool versus alternatives, no prerequisites, and no exclusion criteria. The only additional instruction is pricing (pay per call or subscription), which does not help the agent decide when to invoke the tool.

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 must carry the full burden. It discloses the tool searches the web and extracts content, but does not mention behavioral traits like rate limits, data freshness, or whether it performs real-time API calls. The pricing information is useful but does not cover behavioral 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 long, front-loading the core purpose first. Every sentence adds value: the first sentence defines the function, the second adds method and cost. No extraneous 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 lack of output schema, the description should explain the return format. It says 'provides structured comparison' but does not elaborate on whether it returns a table, JSON, or prose. For a tool with 4 parameters, the description covers the basic workflow but omits details about the structure and limitations of the output.

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 all parameters described. The description adds minimal context beyond the schema: it explains the optional 'aspect' and 'industry' parameters, but this is largely redundant with the schema descriptions. The description does not clarify how these parameters affect the search or comparison logic.

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

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

The description clearly states the tool performs competitive intelligence analysis comparing two companies/entities, searches the web, extracts content, and provides a structured comparison. This sufficiently distinguishes it from siblings like 'compare_articles' and 'analyze_text', which focus on comparing articles or general text analysis.

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 mentions pricing per call or subscription, but provides no guidance on when to use this tool versus alternatives like 'research_topic' or 'extract_content'. There is no indication of prerequisites, exclusions, or typical use cases. The agent lacks context for choosing this tool over 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 bears full burden. It discloses the tool is paid but omits other behavioral traits like rate limits, authentication requirements, or output characteristics (e.g., length, structure). No mention of side effects.

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

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

Two sentences, 14 words, front-loaded with the core function. No fluff, but the brevity sacrifices behavioral and usage details. Could include a bit more without being verbose.

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

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

Given no output schema and no annotations, the description is minimally adequate. It explains the main purpose and parameters but lacks details on return format, error handling, or integration with other tools. Leaves gaps for a 4-parameter 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%, baseline 3. The description adds value by clarifying that the tool accepts 'multiple URLs or topics', reinforcing the flexibility of the 'urls' and 'topics' parameters, and listing format styles that map to the 'format' enum.

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

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

The description clearly states the tool generates a briefing from URLs or topics, specifying supported formats (executive, bullet, detailed). This distinguishes it from siblings like 'analyze_text' or 'extract_content' which perform different operations.

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 mentions pay-per-call and subscription options but provides no guidance on when to use this tool versus siblings (e.g., 'research_topic' vs 'daily_brief'). No explicit when-to-use or when-not-to-use criteria.

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 ad/clutter removal and pricing, but no info on rate limits, errors, authentication, or edge cases.

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 concise sentences. First states purpose, second adds cost and benefit. No wasted words, 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?

No output schema or annotations. Description lacks return value format, error handling, or limitations. Adequate for simple extraction but incomplete.

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

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

Schema coverage is 100% with one URL param. Description adds meaning by specifying output is 'clean readable content' with ads removed, 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?

Clearly states 'Extract clean readable content from a URL' with specific verb and resource. Describes removing ads/clutter/navigation. Distinct from siblings like analyze_text and extract_structured.

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 cost context (pay per call or subscription) but no explicit when-to-use, when-not, or alternatives. Implies use for clean content but lacks guidance vs 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?

No annotations are provided, so the description carries the full burden. It discloses pay-per-call pricing (0.008 USDC), which is a positive behavioral trait. However, it omits other critical behaviors such as authentication requirements, rate limits, error handling, or what happens on failure. This is insufficient for a tool with no annotation safety net.

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, with the core action in the first sentence. It is efficient and includes key differentiators (schema option, pricing). No filler or 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?

Given the complexity (nested objects in schema, no output schema), the description is incomplete. It does not describe the return format, pagination, or data structure. Users are left guessing what the extracted JSON looks like. The presence of a sibling for content extraction suggests the need for clearer differentiation here.

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 the baseline is 3. The description adds minimal extra meaning: it implies the schema parameter is optional ('Define a schema or let AI infer'), but does not elaborate on the structure or constraints beyond what the schema descriptions provide. No significant value added.

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 extracts structured JSON data from a URL, specifying the action and resource. It mentions the option to define a schema or let AI infer, adding clarity. However, it does not explicitly distinguish itself from the sibling 'extract_content', leaving some ambiguity.

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 context on when to use the tool (for structured extraction with optional schema inference), but does not give explicit guidance on when not to use it or suggest alternatives like 'extract_content'. The pricing note is useful but not usage guidance per se.

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 bears full burden. It mentions content hash for change detection and per-call cost, but lacks details on polling frequency, storage, error handling, or idempotency. Important behavioral traits are missing.

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 that are clear and front-loaded, but could be slightly more structured. 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, yet description fails to mention what the tool returns (e.g., content, hash, status). Missing details on error handling and expected behavior for a monitoring tool. Incomplete for a 3-parameter 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 3. Description adds some context (e.g., 'register for ongoing monitoring') but does not significantly enhance understanding beyond the schema descriptions.

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

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

The description clearly states the tool extracts a page with a content hash for change detection, distinguishing it from sibling tools like analyze_text or extract_content which focus on analysis rather than monitoring.

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 guidance on using 'watch' for ongoing monitoring and mentions pricing, but does not explicitly state when not to use this tool or compare with siblings. However, the usage 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?

The description discloses that it searches the web and synthesizes with AI, and mentions cost, but does not explain behavioral traits like return format, caching, or limitations. With no annotations, this is insufficient for 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 two sentences, front-loads the purpose, and is free of fluff. However, it could be slightly improved by including more detail without being verbose.

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

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

Given no output schema and two parameters, the description is too brief. It does not explain return format, pagination, or how synthesis results are presented, leaving the agent underinformed for a research 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 50% (query has description, depth does not). The description adds no additional meaning beyond the schema; the 'depth' parameter with enum values is not explained at all.

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 performs 'multi-source research synthesis on any topic' with web search and AI, providing a specific verb and resource. However, it does not explicitly differentiate from siblings like 'daily_brief' which also does synthesis, so it lacks sibling distinction.

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 mentions pay-per-call cost but provides no guidance on when to use this tool versus alternatives like 'competitor_intel' or 'compare_articles'. No when-not-to-use or context for selection is given.

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

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

With no annotations provided, the description carries the full burden. It discloses pay-per-call cost (0.008 USDC) and subscription option, which is a useful behavioral trait. However, it does not mention non-destructiveness, rate limits, error handling, or what happens to the data, leaving significant gaps.

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

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

The description is very short (two sentences) and conveys the core purpose efficiently. However, it could be slightly more front-loaded by leading with the primary action and cost, rather than repeating 'sentiment' themes. Still, it earns its place with minimal 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?

The tool has three optional parameters, no output schema, and no annotations. The description fails to explain what the tool returns (e.g., a trend chart, scores, or summary). It also omits the 'over time' temporal aspect implicit in the name. For a tool with this complexity, the description is insufficient for an agent to fully understand its 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 description coverage is 100%, so the baseline is 3. The tool description adds no additional parameter guidance beyond what the schema already provides. It does not explain how parameters like 'topic' affect analysis or provide usage examples.

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 analyzes sentiment trends across multiple sources (URLs or texts), and compares/synthesizes sentiment. This distinguishes it from sibling tools like analyze_text (single text analysis) and compare_articles (comparison of articles). However, the 'over time' aspect from the name is not explicitly mentioned, which would improve clarity.

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

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

No explicit guidance on when to use this tool versus alternatives. It mentions pay-per-call cost but does not discuss when not to use it or how it differs from sibling tools like analyze_text or competitor_intel. The context is implied but not directly stated.

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