Scout Intel MCP

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 mentions 'parallel' execution and includes a parameter example, but doesn't disclose critical behavioral traits such as error handling (e.g., if one query fails), rate limits, authentication needs, or what the output looks like. For a batch tool with no annotation coverage, this leaves significant gaps in understanding its 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?

The description is appropriately sized and front-loaded. The first sentence states the core purpose, the second adds use cases, and the third provides a crucial parameter example. Every sentence earns its place with 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 that there is an output schema (context signals indicate 'Has output schema: true'), the description doesn't need to explain return values. However, for a batch tool with no annotations and 2 parameters, it should do more to cover behavioral aspects like error handling or performance implications. The description is adequate but has clear gaps in completeness for this complexity level.

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 meaningful context beyond the input schema. While schema description coverage is 100%, the description provides a concrete example of the query structure ('{"tool": "company|market|competitors|trends|product", "params": {...}}'), which clarifies the allowed tool types and param format. This enhances understanding of the 'queries' parameter, though it doesn't add much for 'max_parallel'.

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: 'Run multiple scout queries in parallel.' It specifies the verb ('run') and resource ('scout queries'), and mentions use cases ('competitive landscape analysis and bulk research'). However, it doesn't explicitly differentiate from its siblings (scout_company, scout_competitors, etc.) beyond implying this is a batch version of those individual 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 implies when to use this tool ('Perfect for competitive landscape analysis and bulk research') and suggests it's for parallel execution of multiple queries. However, it doesn't explicitly state when NOT to use it or provide clear alternatives (e.g., using individual scout tools for single queries). The context is somewhat clear but lacks explicit exclusions or named 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 provided, the description carries the full burden of behavioral disclosure. It mentions the return format (industry, funding, etc.), which is helpful, but lacks critical details like whether this is a read-only operation, requires authentication, has rate limits, or how it handles errors. For a tool that likely queries external data sources, this is a significant gap.

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 and front-loaded, with two sentences that efficiently convey the core functionality and return values. Every word earns its place, and there's no redundant or verbose language.

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), the description covers the basic purpose and return format adequately. Since an output schema exists, the description doesn't need to detail return values further. However, it lacks behavioral context (e.g., data sources, limitations), which slightly reduces completeness for a tool that likely involves external queries.

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 schema already fully documents both parameters (name and domain). The description adds no additional parameter semantics beyond what's in the schema, such as format examples or edge cases. The baseline score of 3 reflects adequate but minimal value added by the 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's purpose with a specific verb ('Get') and resource ('structured intelligence on any company'), making it immediately understandable. However, it doesn't explicitly differentiate this from sibling tools like 'scout_person' or 'scout_product', which likely provide intelligence on different entity types rather than distinguishing within company intelligence.

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 like 'scout_batch', 'scout_competitors', or 'scout_market'. It doesn't mention prerequisites, exclusions, or comparative use cases, leaving the agent to infer usage from tool names alone.

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 of behavioral disclosure. It mentions the return format ('list of competitors with positioning, pricing, strengths, weaknesses'), which adds some context beyond the input schema. However, it lacks critical details such as data sources, accuracy limitations, rate limits, or authentication requirements, leaving significant gaps in understanding the tool's behavior and constraints.

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 core purpose stated in the first sentence and return values in the second. Both sentences earn their place by providing essential information without redundancy. However, it could be slightly more structured by explicitly separating purpose from output details, but overall it's 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?

Given the tool's complexity (competitor analysis) and the presence of an output schema (which likely covers return values), the description is minimally adequate. It states the purpose and output format, but without annotations, it misses behavioral context like data reliability or usage limits. For a tool with no annotations and moderate complexity, it should do more to compensate, but the output schema helps mitigate some 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 description coverage is 100%, meaning the input schema fully documents both parameters (company_or_product and max). The description doesn't add any parameter-specific details beyond what's in the schema, such as formatting nuances or examples. Since the schema handles the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

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 and analyze competitors for any company or product.' It specifies the verb ('Find and analyze'), resource ('competitors'), and scope ('any company or product'), which is specific and actionable. However, it doesn't explicitly differentiate from sibling tools like scout_company or scout_product, which likely have related but distinct 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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools (e.g., scout_batch, scout_market, scout_trends) or clarify scenarios where this tool is preferred over others. Without such context, users must infer usage from tool names alone, which is insufficient for effective selection.

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 of behavioral disclosure. It mentions the return values (market size, growth rate, etc.) but doesn't cover critical aspects like data sources, accuracy, rate limits, authentication needs, or whether this is a read-only operation. For a research tool with zero annotation coverage, this leaves significant gaps in understanding its 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?

The description is brief and front-loaded with the core purpose in the first sentence, followed by a clear list of return values. There's no wasted text, but it could be slightly more structured (e.g., separating purpose from returns with a colon or bullet points).

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 (researching markets), the presence of an output schema (which handles return values), and 100% schema coverage, the description is minimally adequate. However, it lacks context about data freshness, scope limitations, or how it differs from siblings, which would be helpful for an agent to use it 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?

The schema description coverage is 100%, so the input schema already fully documents the 'query' and 'depth' parameters. The description adds no additional parameter semantics beyond what's in the schema, such as examples of effective queries or implications of the 'depth' setting. This meets the baseline for high schema 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?

The description clearly states the tool's purpose as 'Research any market or industry' with a specific verb ('Research') and resource ('market or industry'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'scout_trends' or 'scout_competitors' which might have overlapping functionality, preventing a perfect score.

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 like 'scout_trends' or 'scout_competitors' from the sibling list. It also lacks context about prerequisites, limitations, or typical use cases, leaving the agent to infer usage based on the tool name alone.

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 of behavioral disclosure. It mentions 'PRO tier only,' which hints at access restrictions, but does not cover other important traits such as rate limits, authentication needs, data freshness, or error handling. For a tool that fetches intelligence data, this is a significant gap in 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 concise and front-loaded, starting with the core purpose. The sentences are efficient, with no wasted words, and it includes key details like the PRO tier restriction and return values. However, the list of return items could be slightly more structured for better readability.

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, the description covers the purpose, access restriction, and return values. With an output schema present, it does not need to explain return values in detail, and the schema handles parameters well. The main gap is the lack of behavioral context, but overall, it provides a reasonably complete overview 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 schema description coverage is 100%, with clear descriptions for both parameters ('name' and 'company'). The description adds minimal value beyond the schema by implying the tool focuses on 'public figures,' but does not provide additional context like format examples or constraints beyond what's in the schema. This meets the baseline for high schema 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?

The description clearly states the tool's purpose: 'Get intelligence on a public figure.' It specifies the verb ('Get') and resource ('intelligence on a public figure'), making it understandable. However, it does not explicitly differentiate from sibling tools like 'scout_company' or 'scout_batch,' which reduces clarity about when to choose this specific 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?

The description provides some usage context by stating 'PRO tier only,' indicating a prerequisite or restriction. However, it lacks explicit guidance on when to use this tool versus alternatives like 'scout_company' or 'scout_batch,' and does not mention any exclusions or specific scenarios for its use, leaving room for 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. While it mentions what information is returned (category, pricing, ratings, etc.), it doesn't address important behavioral aspects like rate limits, authentication requirements, data freshness, or potential costs. The description is functional but lacks operational 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 efficiently structured in two sentences: one stating the purpose and one listing return values. It's appropriately sized for a single-parameter tool, though the second sentence could be more elegantly integrated rather than appearing as a bulleted list in prose form.

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 that an output schema exists (which should document the return structure), the description doesn't need to explain return values in detail. However, for a tool with no annotations and multiple similar siblings, the description should provide more context about when to use it and what distinguishes it from alternatives to be truly 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 input schema has 100% description coverage, with the single parameter 'name' clearly documented in the schema itself. The description doesn't add any additional parameter semantics beyond what the schema already provides, so it meets the baseline expectation when schema coverage is complete.

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 intelligence') and resource ('any product'), making it immediately understandable. However, it doesn't explicitly differentiate this from sibling tools like 'scout_company' or 'scout_competitors', which likely provide different types of intelligence.

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. With multiple 'scout_' tools available (scout_batch, scout_company, scout_competitors, etc.), there's no indication of what distinguishes this product intelligence tool from alternatives that might also provide product-related information.

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. While it mentions what the tool returns (sentiment score, trending direction, etc.), it doesn't describe important behavioral aspects like rate limits, authentication requirements, data sources, accuracy limitations, or whether this is a read-only operation. For a tool with no annotation coverage, this leaves significant gaps in understanding its operational characteristics.

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 concise with two sentences that each serve distinct purposes: the first states the core functionality, the second specifies the return values. It's front-loaded with the main purpose. While efficient, the second sentence could be slightly more integrated with the first for better flow.

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 that an output schema exists (context signals indicate 'Has output schema: true'), the description doesn't need to explain return values in detail. The description covers the basic purpose and output types adequately for a tool with good schema coverage. However, the lack of behavioral context and usage guidance relative to siblings prevents a perfect score.

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 100%, so the schema already fully documents both parameters (topic and timeframe). The description doesn't add any parameter-specific information beyond what's in the schema. According to scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the 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's purpose with specific verbs ('track trends and sentiment') and identifies the resource ('on any topic'). It distinguishes itself from potential siblings by focusing on general topic analysis rather than specific entity types like companies or products. However, it doesn't explicitly differentiate from all sibling tools by name.

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 the six sibling tools (scout_batch, scout_company, etc.). It doesn't mention alternatives, prerequisites, or exclusions. The agent must infer usage context solely from the tool name and description without explicit comparison to related tools.

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