Brand Intelligence
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Search and analyze 5,600+ companies across 30 technology verticals.
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Tool Definition Quality
Average 4.2/5 across 7 of 7 tools scored. Lowest: 3.5/5.
Each tool has a clearly distinct purpose with no ambiguity: compare_brands for side-by-side comparison, find_alternatives for competitor discovery, find_by_capability for capability-based search, get_brand for detailed profiles, get_brand_graph for relationship mapping, get_landscape for vertical overviews, and search_brands for keyword searches. The descriptions clearly differentiate their scopes and use cases.
All tools follow a consistent verb_noun pattern with snake_case: compare_brands, find_alternatives, find_by_capability, get_brand, get_brand_graph, get_landscape, and search_brands. The naming is predictable and readable throughout the set.
Seven tools is well-scoped for brand intelligence, covering core operations like search, retrieval, comparison, and analysis without being overwhelming. Each tool earns its place by addressing a specific aspect of brand data exploration and decision-making.
The tool set provides complete coverage for brand intelligence, including search (search_brands), detailed retrieval (get_brand, get_brand_graph), comparison (compare_brands), competitor analysis (find_alternatives), capability-based discovery (find_by_capability), and market overviews (get_landscape). There are no obvious gaps, and agents can navigate from broad searches to deep dives seamlessly.
Available Tools
7 toolscompare_brandsAInspect
Compare multiple brands side by side.
Args:
slugs: List of 2–5 brand slugs to compare (e.g. ["pacvue", "skai", "northbeam"]).
Use search_brands to find slugs.
Returns:
Dict with a "brands" list (each brand's full profile) and a "comparison_dimensions"
list of fields you can use to compare them (tier, vertical, subcategory, tags, signal).| Name | Required | Description | Default |
|---|---|---|---|
| slugs | Yes |
Tool Definition Quality
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 the input constraint (2–5 slugs) and output structure (dict with brands list and comparison dimensions), but lacks details on permissions, rate limits, error handling, or whether this is a read-only operation. The description adds some behavioral context but is incomplete for a tool with no annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with the core purpose, followed by clear sections for Args and Returns. Every sentence earns its place by providing essential information 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 moderate complexity (1 parameter, no output schema, no annotations), the description is mostly complete: it covers purpose, usage, parameters, and return values. However, it lacks behavioral details like error cases or performance characteristics, which would be helpful for full completeness.
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?
With 0% schema description coverage, the description fully compensates by explaining the 'slugs' parameter: it's a list of 2–5 brand slugs, provides an example (['pacvue', 'skai', 'northbeam']), and references 'search_brands' to find them. This adds significant meaning beyond the 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?
The description clearly states the tool's purpose: 'Compare multiple brands side by side.' This specifies the verb ('compare') and resource ('brands'), though it doesn't explicitly differentiate from sibling tools like 'get_brand' (single brand) or 'search_brands' (searching rather than comparing).
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 clear context for when to use this tool: comparing 2–5 brands using slugs, and it references 'search_brands' to find slugs. However, it doesn't explicitly state when not to use it or name alternatives like 'get_brand' for single brands or 'find_alternatives' for other comparison scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
find_alternativesAInspect
Find alternatives and competitors to a given brand.
Searches first within the same subcategory (closest competitors), then broadens
to the full vertical if fewer than 3 subcategory matches are found.
Args:
slug: The brand slug to find alternatives for (e.g. "pacvue").
limit: Max alternatives to return (default 10).
Returns:
Dict with the source brand info, and "alternatives" list sorted by market tier.
Each alternative includes a "match_reason" field ("same subcategory" or "same vertical").
Returns error dict if brand not found.| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | ||
| limit | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does well by disclosing key behavioral traits: the search algorithm (subcategory priority, fallback to vertical), sorting logic (by market tier), error handling (returns error dict if brand not found), and return structure (dict with source brand and alternatives list).
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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by algorithm details, parameter explanations, and return format—each sentence adds value with zero waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, no annotations, no output schema, and 0% schema coverage, the description is largely complete. It covers purpose, algorithm, parameters, and return structure, though it could benefit from more explicit usage guidance relative to siblings.
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%, so the description must compensate fully. It effectively adds meaning beyond the schema by explaining both parameters: 'slug' is clarified with an example ('e.g. "pacvue"') and context ('brand slug to find alternatives for'), and 'limit' is explained as 'max alternatives to return' with its default 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 purpose with specific verbs ('find alternatives and competitors') and resources ('to a given brand'), and distinguishes it from siblings by focusing on competitor discovery rather than comparison, retrieval, or search functions.
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 clear context about the search algorithm (subcategory first, then vertical), but does not explicitly state when to use this tool versus alternatives like compare_brands or search_brands, nor does it mention exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
find_by_capabilityAInspect
Find brands that have a specific capability.
Args:
capability: Capability keyword (e.g. "code-generation", "voice-synthesis",
"payment-processing"). Partial matches supported.
domain: Optional vertical to filter (e.g. "Artificial Intelligence").
limit: Max results (default 20).
Returns:
List of brands with matching capabilities, including their proficiency level.| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| domain | No | ||
| capability | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that partial matches are supported for the capability parameter and mentions the return format includes proficiency levels, which adds useful behavioral context beyond basic input/output. However, it doesn't cover important aspects like rate limits, authentication needs, or error conditions.
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 efficiently structured with a clear purpose statement followed by organized sections for Args and Returns. Every sentence earns its place by providing essential information without redundancy. The formatting with bullet-like sections makes it 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?
Given the tool has an output schema (which covers return values), the description provides adequate context. It explains the purpose, parameters, and mentions the return includes proficiency levels. For a search tool with good parameter documentation and output schema, this description is reasonably complete, though it could benefit from more behavioral 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?
With 0% schema description coverage, the description compensates well by explaining all three parameters. It provides examples for 'capability' and 'domain', clarifies that partial matches are supported for capability, and specifies the default value for limit. The description adds meaningful semantic context that the schema lacks.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Find brands that have a specific capability.' It uses a specific verb ('Find') and resource ('brands'), but doesn't explicitly differentiate from sibling tools like 'search_brands' or 'get_brand', which could have overlapping functionality. The purpose is clear but lacks sibling differentiation.
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. With sibling tools like 'search_brands', 'find_alternatives', and 'get_brand', there's no indication of when this capability-based search is preferred over other search methods. The usage context is implied by the purpose but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_brandAInspect
Get the full intelligence profile for a brand by its URL slug.
Args:
slug: URL-safe brand identifier (e.g. "pacvue", "hubspot", "snowflake").
Use search_brands to discover slugs if unsure.
Returns:
Full brand profile including company overview (3 paragraphs), signal summary,
structured FAQs, vertical, tier/rank, website, tags, and source URL.
Returns an error dict if the brand is not found.| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes |
Tool Definition Quality
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 key behavioral traits: it returns a full profile with specific content details (e.g., company overview, FAQs), and it returns an error dict if the brand is not found. However, it lacks information on rate limits, authentication needs, or pagination.
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 appropriately sized and front-loaded with the core purpose in the first sentence. Each subsequent sentence adds essential value: parameter explanation, usage guidance, return details, and error handling, with zero wasted content.
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 (single parameter, no output schema, no annotations), the description is complete enough. It covers purpose, parameter semantics, usage guidelines, return content, and error behavior, providing all necessary context for an AI agent to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate fully. It adds significant meaning beyond the schema by explaining the slug parameter as a 'URL-safe brand identifier' with examples ('pacvue', 'hubspot', 'snowflake') and linking it to the search_brands tool for discovery.
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 specific action ('Get the full intelligence profile') and resource ('for a brand by its URL slug'), distinguishing it from sibling tools like search_brands (which discovers slugs) and compare_brands/find_alternatives/get_landscape (which involve multiple brands or broader 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?
It explicitly states when to use this tool ('by its URL slug') and when to use an alternative ('Use search_brands to discover slugs if unsure'), providing clear guidance on prerequisites and distinguishing it from sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_brand_graphAInspect
Get the knowledge graph for a brand — entity type, parent/child hierarchy, competitive edges, integrations, acquisitions, and structured capabilities.
Args:
slug: Brand slug (e.g. "chatgpt", "aws", "salesforce").
Returns:
Dict with entity_type, parent company, child products, relationship
edges (competes_with, integrates_with, acquired_by, invested_in,
powered_by, product_of, subsidiary_of, built_on), and capabilities.| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the tool's behavior by detailing the return structure (dict with specific fields), which helps understand output format. However, it doesn't mention error handling, rate limits, authentication needs, or whether the data is static/cached vs. real-time.
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 efficiently structured: first sentence states the purpose, followed by bullet-like sections for Args and Returns. Every sentence adds value—no fluff or repetition—and it's front-loaded with the core functionality.
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 annotations, no output schema, and 1 parameter, the description is quite complete: it explains the purpose, parameter semantics, and return structure. However, it lacks details on behavioral aspects like error cases or data freshness, leaving minor gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage (no param descriptions), but the description fully compensates by explaining the 'slug' parameter with a clear definition ('Brand slug') and concrete examples ('chatgpt', 'aws', 'salesforce'), adding essential meaning beyond the 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?
The description clearly states the verb 'Get' and the resource 'knowledge graph for a brand', specifying the exact data returned (entity type, hierarchy, competitive edges, integrations, acquisitions, capabilities). It distinguishes from siblings like 'get_brand' (likely simpler data) and 'compare_brands' (comparative 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 implies usage when detailed brand relationship data is needed, but doesn't explicitly state when to use this vs. alternatives like 'get_brand' (for basic info) or 'get_landscape' (for broader context). No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_landscapeAInspect
Get all brands in a vertical, ranked by market position.
Args:
vertical: Vertical name or slug (e.g. "Cybersecurity", "cybersecurity",
"marketing-technology", "HR & People Tech").
limit: Max brands to return (default 50, max 100).
Returns:
Dict with "vertical" (canonical name), "brand_count", and "brands" list ranked
by tier then confidence. Each entry has brand, slug, tier, rank, subcategory, signal, tags.| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| vertical | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the ranking behavior (by tier then confidence) and output structure, but does not mention potential limitations like rate limits, authentication needs, or error handling, leaving gaps in behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by well-structured sections for Args and Returns. Every sentence adds value—no wasted words—making it efficient 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?
Given no annotations and no output schema, the description does well by explaining parameters and return values in detail. However, it lacks information on error cases or side effects, which could be important for a tool with ranking logic.
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%, so the description must compensate. It fully explains both parameters: 'vertical' with examples (e.g., 'Cybersecurity') and 'limit' with default and max values, adding essential meaning beyond the 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?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('all brands in a vertical'), and distinguishes it from siblings by specifying it returns ranked brands by market position, unlike 'get_brand' (single brand) or 'search_brands' (search-based).
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 implies usage for retrieving ranked brands in a vertical, but does not explicitly state when to use it versus alternatives like 'compare_brands' or 'find_alternatives'. It provides clear context for vertical-based retrieval but lacks explicit exclusions or comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_brandsAInspect
Search for brands by name, description, or keyword across the brand database.
Args:
query: Search term — company name, product category, technology, or description keyword.
vertical: Optional vertical filter (e.g. "Cybersecurity", "Marketing Technology",
"Financial Services"). Use the exact canonical name or a close slug.
Returns:
List of matching brands (up to 20) with brand name, slug, vertical, tier, signal summary,
and source URL. Use get_brand(slug) to fetch full details on any result.| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| vertical | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
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 behavioral traits: it returns a list of up to 20 matching brands with specific fields (name, slug, vertical, tier, signal summary, source URL) and mentions pagination/limitation. It also notes that get_brand(slug) is needed for full details, adding useful context about the tool's scope and limitations.
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 well-structured and appropriately sized. It front-loads the purpose, then details args and returns in separate sections. Every sentence adds value: the first states the tool's function, the args section clarifies parameters, and the returns section explains output and next steps, 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?
Given the tool's moderate complexity (2 parameters, no annotations, but has output schema), the description is complete enough. It explains the purpose, parameter semantics, and output details (including limitations like up to 20 results and the need for get_brand for full details). The output schema existence means the description doesn't need to fully document return values, but it still provides helpful 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?
The schema description coverage is 0%, so the description must compensate. It adds significant meaning beyond the input schema: it explains that 'query' can be a company name, product category, technology, or description keyword, and 'vertical' requires an exact canonical name or close slug with examples (e.g., 'Cybersecurity'). This provides clear semantics and usage guidance for both 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 searches for brands by name, description, or keyword across the brand database. It specifies the verb (search) and resource (brands) with scope (across database), but doesn't explicitly differentiate from sibling tools like find_alternatives or get_landscape, which might also involve brand searches.
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 implies usage for searching brands and mentions using get_brand(slug) for full details, providing some context. However, it doesn't explicitly state when to use this tool versus alternatives like find_alternatives or compare_brands, nor does it mention any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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