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

With no annotations, the description carries the full burden. It discloses the real live path against /nodes.json and /settings.json, the fallback to clearly labeled simulated data, and the return format. This provides meaningful behavioral context beyond the bare schema, though it doesn't cover all edge cases like authentication failure 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 well-structured: a single-sentence purpose, a brief behavioral note, a return format section, and two examples. Every sentence contributes valuable information without redundancy, 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?

For a read-only status tool, the description explains the live data source, fallback behavior, and return envelope. The specification of the return format compensates for the absence of an output schema in the provided input. It does not enumerate all possible data fields inside 'domain', but that may be acceptable for this tool's purpose.

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 80% (4 of 5 parameters have descriptions). The description adds example invocations that demonstrate the host, port, username, and password parameters, but does not elaborate on the ctx parameter, which lacks a schema description. It provides marginal value beyond the schema's own parameter 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 begins with a specific verb+resource: 'Retrieve connected-node telemetry and settings from an Overte Domain Server.' This clearly distinguishes it from sibling tools like overte_script_inject, which have notably different 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 implies usage by stating it retrieves telemetry/settings and mentions a fallback to simulated data when a domain-server is unreachable. While it doesn't explicitly name alternatives, the context is clear and there are no conflicting sibling tools. It lacks explicit when-not-to-use guidance, so it falls short of a 5.

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 burden of behavioral disclosure. It clearly discloses the live-versus-simulated behavior and the exact return format, which is critical for the agent to interpret results correctly. It stops short of discussing permissions or reversibility, but for a spawn operation this is adequate.

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 with a clear opening statement, a note on live/simulated mode, a return format section, and examples. Every section earns its place without unnecessary fluff, making it easy to scan 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 tool has 8 parameters and no annotations, the description provides essential context: purpose, simulation behavior, and return shape. The examples and return format compensate for a lack of behavior details like coordinate units or persistence. It is not exhaustive but is complete enough for the agent to invoke 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?

Schema description coverage is 88%, so the baseline is 3. The description adds value through examples that illustrate parameter combinations, such as using 'entity_type="Model"' with 'model_url', and 'position' as a coordinate array. This goes beyond simply restating schema fields, though it does not clarify the undocumented 'ctx' parameter.

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 'Spawn a virtual object or 3D GLB model in-world at the specified coordinates,' using a specific verb and resource. This distinguishes it from sibling tools like overte_script_inject, overte_sampling_assist, and overte_domain_status, which serve entirely different 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 practical usage context by noting that it is live when the bridge is connected and otherwise returns a simulated confirmation. It also gives examples of typical calls. However, it does not explicitly mention alternatives or when not to use it, though the sibling context makes the differentiation 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?

With no annotations, the description carries the full burden. It discloses the sampling mechanism (ctx.sample), the fallback behavior ('returns structured error otherwise'), and the return format with an example. This provides useful behavioral context beyond the empty 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 with a brief summary, example, and return format spec. Every sentence adds value and it is appropriately concise for a tool of this complexity.

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 output schema is present and the description additionally explains the error behavior and provides a concrete example. The tool is fully contextualized for an agent, covering the key aspects of sampling usage and fallback.

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 only 50%: 'goal' is documented, but 'ctx' is not. The description mentions ctx.sample but does not explain what the ctx parameter is or how it should be used, failing to compensate for the schema gap.

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 uses a specific verb ('Get') and resource ('multi-step Overte operation guidance') to define what the tool does. It clearly distinguishes itself from siblings like overte_entity_spawn or overte_script_inject by positioning itself as a planning/guidance tool rather than an execution 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 states it is for 'complex goals' and 'multi-step' operations, providing clear context on when to use it. It does not explicitly mention when not to use it or name alternatives, but the purpose is clear enough to guide 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. It discloses a key behavioral trait: the tool returns a clearly labeled simulated confirmation when disconnected, meaning it won't actually inject the script. It also specifies the return format, adding useful transparency beyond the bare existence of the tool.

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: purpose statement, connectivity caveat, return format, and examples. It is concise without being terse, and each section earns its place. Slightly longer than necessary due to the return format and examples, but still efficiently organized.

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, lack of annotations, and presence of an output schema, the description is quite complete. It explains the tool's purpose, the crucial live/simulation behavior, the return format, and provides instant-usage examples. It does not cover prerequisites or side effects, but these are not obviously critical for this 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?

The schema already describes 3 of 4 parameters (75% coverage), providing a baseline of 3. The description adds value through examples showing how to pass entity_id, script_url, and script_data, but it leaves the 'ctx' parameter completely unexplained, so it doesn't fully compensate for the 25% schema gap.

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 ('Inject a JavaScript script') and the target ('in-world entity'), along with the intended purpose ('to govern behavior'). This distinguishes it from sibling tools like overte_entity_spawn, which deals with entity creation.

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 important usage context by specifying that the tool is live only when the Overte bridge WebSocket client is connected, and otherwise returns a simulated confirmation. However, it does not explicitly discuss alternatives or when not to use the tool, so it falls short of a 5.

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