Scrapybara MCP

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

No annotations are provided, so the description carries full burden. It mentions the agent can control the instance with mouse/keyboard and bash commands, which hints at behavioral traits like interactivity and command execution. However, it doesn't disclose critical details such as whether this is a read/write operation, potential side effects (e.g., data changes), authentication needs, rate limits, or error handling. For a tool with no annotations, this leaves significant gaps 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 with two sentences that efficiently state the tool's purpose and capabilities. It's front-loaded with the main action and avoids unnecessary details. However, it could be slightly more structured by explicitly separating the agent's roles or adding brief context, but overall it's well-sized and wastes no 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 of controlling an instance with an agent (implying interactive or automated tasks), no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., success status, extracted data), potential errors, or behavioral nuances. For a tool with 3 parameters and significant implied functionality, more context is needed to make it fully usable by 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?

Schema description coverage is 100%, so the schema already documents all parameters (instance_id, prompt, schema). The description adds minimal semantic value beyond the schema: it implies 'prompt' is for action instructions and 'instance_id' targets a specific instance, but doesn't elaborate on format or constraints. With high schema coverage, the baseline is 3, and the description doesn't significantly enhance parameter understanding.

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: 'Take action on a Scrapybara instance through an agent' with specific verbs like 'control' and actions like 'mouse/keyboard and bash commands'. It distinguishes from siblings like 'bash' (which might be standalone) and 'get_instances' (which is read-only), though it doesn't explicitly name alternatives. The purpose is specific but could be more precise about the agent's role.

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 by mentioning controlling an instance with an agent, suggesting it's for interactive or automated tasks on Scrapybara. However, it lacks explicit guidance on when to use this vs. siblings like 'bash' (e.g., for direct commands) or 'start_instance' (for setup). No exclusions or prerequisites are stated, leaving usage context inferred rather than clearly defined.

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 states the action but doesn't reveal critical traits like whether this is a read-only or destructive operation, potential security implications, rate limits, or error handling. For a tool that runs arbitrary commands, this lack of transparency 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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and wastes no space, making it easy to parse 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 running arbitrary bash commands and the absence of annotations and output schema, the description is incomplete. It doesn't cover behavioral aspects like safety, permissions, or output format, which are crucial for such a tool. This leaves the agent with insufficient context for reliable 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 both parameters ('instance_id' and 'command'). The description adds no additional meaning beyond what the schema provides, such as command syntax examples or instance state requirements. Baseline 3 is appropriate since the 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 action ('Run') and target ('bash command in a Scrapybara instance'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'act' or 'start_instance', which might also involve instance operations, leaving some ambiguity about when this specific tool is appropriate versus others.

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 'act' or 'start_instance'. It lacks context about prerequisites, such as whether the instance must be running, or exclusions, such as not using it for non-bash commands. This leaves the agent without clear usage instructions.

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 states what the tool does but lacks behavioral details such as whether it requires authentication, how it handles errors, or what the return format looks like. This is a significant gap for a tool with zero annotation coverage.

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 with no wasted words. It's front-loaded and directly states the tool's purpose without unnecessary elaboration.

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 is incomplete. It lacks details on behavioral traits, return values, or error handling, which are crucial for a tool that likely interacts with system resources like instances.

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 tool has 0 parameters, and schema description coverage is 100%, so no parameter information is needed. The description doesn't add param details, but that's appropriate here, meeting the baseline for zero 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 verb ('Get') and resource ('all running Scrapybara instances'), making the purpose unambiguous. It doesn't explicitly differentiate from siblings like 'start_instance' or 'stop_instance', but the action is distinct enough to imply 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?

No guidance is provided on when to use this tool versus alternatives like 'start_instance' or 'stop_instance'. The description implies usage for listing instances but doesn't specify prerequisites, timing, or exclusions.

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 key behavioral traits: the tool creates a desktop sandbox for web access or code execution, and it generates a stream URL for real-time viewing. However, it lacks details on permissions, 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 front-loaded with the core action, followed by usage context and a critical post-action step. Every sentence adds essential information with zero waste, making it highly efficient.

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 (starting an instance with streaming) and lack of annotations or output schema, the description is mostly complete. It covers purpose, usage, and output behavior, but could benefit from details on instance specifications or error cases.

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 0 parameters with 100% coverage, so no parameter documentation is needed. The description adds value by explaining the tool's purpose and output behavior, justifying a baseline score above the minimum.

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 ('Start') and resource ('a Scrapybara Ubuntu instance'), and distinguishes it from siblings like 'stop_instance' and 'get_instances' by focusing on initiation rather than termination or querying.

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 ('Use it as a desktop sandbox to access the web or run code') and provides a clear post-action directive ('Always present the stream URL to the user afterwards'), offering strong guidance without mentioning 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 states the action ('Stop') but lacks details on effects (e.g., whether data is preserved, permissions required, or if the operation is reversible). This is a significant gap for a mutation tool with zero annotation coverage.

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 with zero waste, clearly front-loading the core action. It's appropriately sized for a simple tool with one parameter, earning its place without unnecessary elaboration.

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 as a mutation operation with no annotations and no output schema, the description is incomplete. It lacks information on behavioral outcomes, error conditions, or return values, which is inadequate for guiding an agent in safe and 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?

Schema description coverage is 100%, with the single parameter 'instance_id' documented in the schema. The description adds no additional parameter details beyond what the schema provides, so it meets the baseline for high schema coverage without compensating 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 action ('Stop') and target resource ('a running Scrapybara instance'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'start_instance' or 'get_instances' beyond the obvious verb difference, missing explicit comparison.

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. It doesn't mention prerequisites (e.g., the instance must be running), exclusions, or comparisons to siblings like 'start_instance' or 'act', leaving usage context implied but unspecified.

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