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 analyzing and extracting details, but fails to describe key traits such as whether this is a read-only operation, potential side effects, performance considerations (e.g., time-intensive due to script extraction), or error handling. This leaves significant gaps in understanding how the tool behaves.

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

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

The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part earns its place by clearly stating the action and target, making it easy to parse and understand quickly.

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 of analyzing bot protection mechanisms and the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'challenge details' include, how results are returned, or any limitations (e.g., only works with certain flow types). For a tool with no structured behavioral or output information, more context is needed to be fully helpful.

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 input schema fully documents all three parameters. The description adds no additional meaning beyond what's in the schema (e.g., it doesn't explain how 'session_id' or 'flow_index' relate to protection analysis). With high schema coverage, 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 with specific verbs ('analyze', 'extract') and resources ('flow', 'bot protection mechanisms', 'challenge details'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_flow_details' or 'list_flows', which might also analyze or extract information from flows.

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 'get_flow_details' or 'extract_json_fields'. It lacks context on prerequisites (e.g., needing a session or flow index), exclusions, or specific scenarios where this tool is preferred, leaving usage decisions ambiguous.

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 states the tool extracts fields but doesn't describe what happens if JSON content is missing, invalid, or if JSONPath expressions fail. It lacks details on permissions, rate limits, or output format, leaving key behavioral traits unspecified for a tool that manipulates data.

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

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

The description is a single, efficient sentence that front-loads the core purpose ('extract specific fields from JSON content in a flow') and method ('using JSONPath expressions'). There is no wasted text, and it directly communicates the tool's function 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 complexity of a 4-parameter tool with no annotations and no output schema, the description is incomplete. It doesn't explain the return values, error handling, or behavioral nuances like what happens with invalid inputs. For a tool that processes JSON data, more context on output format and limitations is needed to be fully helpful.

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 fully documents all four parameters. The description adds minimal value beyond the schema by mentioning 'JSON content in a flow' and 'JSONPath expressions', which align with parameters like 'content_type' and 'json_paths'. However, it doesn't provide additional syntax, examples, or constraints beyond what's in 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 action ('extract specific fields') and resource ('JSON content in a flow'), specifying the method ('using JSONPath expressions'). It distinguishes from sibling tools like 'get_flow_details' or 'list_flows' by focusing on field extraction rather than retrieval or analysis. However, it doesn't explicitly contrast with 'analyze_protection', which might involve similar data handling.

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 prerequisites (e.g., needing a valid session or flow), exclusions, or compare to siblings like 'analyze_protection' for JSON analysis. Usage is implied only by the action, with no explicit context for 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the tool as listing data, implying a read-only operation, but doesn't cover critical aspects like whether it's safe (e.g., no side effects), performance characteristics (e.g., handling of large datasets), or error conditions. For a tool with no annotation coverage, 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 a single, efficient sentence that directly states the tool's purpose without unnecessary details. It is front-loaded and wastes no words, making it easy for an agent to parse quickly and understand 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?

Given the complexity of handling HTTP flows and the lack of annotations and output schema, the description is incomplete. It doesn't explain the return format (e.g., structure of listed data), potential limitations (e.g., pagination or size constraints), or how parameters like 'include_content' affect behavior. For a tool with 3 parameters and no structured output, more context is needed for effective use.

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, clearly documenting all parameters. The description adds no additional meaning beyond the schema, such as explaining parameter interactions or usage nuances. With high schema coverage, the baseline score is 3, as the description doesn't compensate but also doesn't detract from the schema's information.

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: 'Lists HTTP requests/responses from a mitmproxy capture session, showing method, URL, and status codes.' It specifies the verb ('Lists'), resource ('HTTP requests/responses'), and scope ('from a mitmproxy capture session'), making the function evident. However, it doesn't explicitly differentiate from sibling tools like 'list_flows', 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. It doesn't mention sibling tools like 'list_flows' or specify contexts, prerequisites, or exclusions for usage. This lack of comparative or contextual advice leaves the agent without clear direction for tool 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?

No annotations are provided, so the description carries the full burden. It mentions retrieving 'detailed HTTP request/response data' including headers, content, and metadata, but lacks critical behavioral details such as whether this is a read-only operation, potential rate limits, error handling, or how large JSON is handled ('structure preview'). This leaves significant gaps for an agent.

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

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

The description is a single, efficient sentence that front-loads the key action ('Retrieves detailed HTTP request/response data'). It could be slightly more structured by separating scope details, but it avoids unnecessary fluff and 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?

Given the lack of annotations and output schema, the description is incomplete. It hints at behavioral aspects like handling 'large JSON' but doesn't fully explain return values, error cases, or operational constraints. For a tool with no structured support, more detail is needed to guide an agent 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 100%, so the schema already documents the single parameter 'session_id' adequately. The description adds no additional meaning or context about the parameter beyond what the schema provides, such as format examples or how to obtain a session_id. Baseline 3 is appropriate when schema does the heavy lifting.

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 ('Retrieves') and resource ('detailed HTTP request/response data from specified flows'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_flow_details' or 'analyze_protection', which might have overlapping functionality.

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 guidance is provided on when to use this tool versus alternatives like 'get_flow_details' or 'analyze_protection'. The description mentions retrieving data from 'specified flows' but doesn't clarify prerequisites, such as needing a valid session_id or how this differs from other flow-related tools.

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