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

No annotations are provided, so the description carries the full behavioral burden, and it discloses almost nothing beyond the action itself. It does not say whether cancellation is irreversible, whether the job's partial output is retained or discarded, what happens if the job already finished, or what the tool returns. For a destructive lifecycle operation 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?

A single front-loaded sentence with zero filler; the action and its target are stated immediately.

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 one-parameter tool with a fully documented schema and no output schema, the definition is minimally viable. It is still missing the behavioral facts an agent needs before invoking a cancellation (irreversibility, effect on job output, behavior on already-completed jobs).

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% and the single job_id parameter is documented in the schema ('The job ID to cancel.'), so the baseline of 3 applies. The description adds no format, sourcing, or lookup guidance (e.g. obtaining the ID from list_active_jobs) beyond the 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 states a specific verb and resource ('Cancel a running background subagent job'), which an agent can distinguish from the read-oriented siblings check_agent_job and list_active_jobs by verb alone. It stops short of explicitly naming a sibling or contrasting scope, so it lands at 4 rather than 5.

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?

There is no when-to-use guidance, no prerequisites (e.g. the job must still be running), and no mention of alternatives or follow-up tools such as check_agent_job. The word 'running' hints at a precondition but leaves it to inference.

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 behavioral burden as a single terse sentence. It never states that the operation is read-only, whether it blocks or polls, whether log output is truncated or streamed, or how it behaves for a still-running versus failed job.

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?

One sentence, zero filler, and the object of the operation is front-loaded before the enumerated return facets. Nothing here fails to earn 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?

With no output schema and no annotations, the description usefully compensates by naming what comes back (status, logs, duration, output). It still omits polling/streaming behavior and terminal state handling, which an agent monitoring a job would want.

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% for the single job_id parameter, and the schema description even cites dispatch_gemini_agent as the source and gives an example format. The prose adds no meaning beyond the schema, so the baseline of 3 applies.

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 pairs a specific verb ('Check') with a specific resource ('a dispatched agent job') and enumerates the returned facets (status, logs, duration, output), which is clearer than the bare name. It implicitly distinguishes itself from dispatch_gemini_agent by noting the job is already dispatched, but it does not explicitly contrast itself with list_active_jobs or cancel_agent_job.

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?

There is no guidance on when to use this tool versus list_active_jobs (which also surfaces jobs) or cancel_agent_job. The dependency on a prior dispatch is only hinted at through 'dispatched', leaving the workflow condition to inference.

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, and it does disclose the single most important trait: this is asynchronous and returns a job_id immediately. It says nothing about failure modes (do errors surface at dispatch or later via check_agent_job), concurrency/rate limits, or the dependence on an active Antigravity IDE session.

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?

Two sentences, front-loaded with the action and finished with the async guarantee. Nothing is redundant and nothing needs to be skimmed past.

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 dispatch tool with no output schema, the description is adequate but thin: it establishes that a job_id comes back, yet does not say what else the response holds or that results must be retrieved later via check_agent_job. The rich input schema covers the request side, so the remaining gap is on the response/lifecycle side.

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 baseline is 3; the schema already documents task, model (with the list_available_models routing hint), context_files, workspace_path and system_instruction in detail. The description adds no parameter-level meaning beyond what the schema provides.

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?

States a specific verb and resource: dispatch a coding/analysis/research task to a background subagent, plus the async outcome (returns job_id). It is clearly distinguishable from check_agent_job, list_active_jobs and cancel_agent_job in practice, but it never names those siblings to make the distinction explicit.

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 task classes it accepts (coding, analysis, research) plus the 'returns immediately so the orchestrator is never blocked' note give clear context for when to reach for it over a blocking call. There are no exclusions, however: nothing says what it should not be used for, and the follow-up path (poll with check_agent_job) is left implicit.

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 usefully discloses that execution is synchronous and that the tool returns an answer, but it omits limits, permissions, latency/cost implications, and error behavior for a large-context model call.

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 two sentences, front-loads the core capability, and includes the synchronous-return behavior without wasted wording.

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?

It covers the basic purpose and synchronous return behavior, which is helpful given no annotations and no output schema. However, it leaves out practical constraints such as file count limits, output format expectations, and how workspace_path inference works.

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 coverage is 100%, so the schema already documents query, context_files, and workspace_path. The description reinforces multi-file scanning and question answering but adds no additional meaning for workspace_path or file-path constraints.

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 states a specific action: scan multiple repository files with a large-context model and answer architectural or code questions. This clearly distinguishes it from workspace/job-management siblings, though it does not explicitly name or contrast with dispatch_gemini_agent.

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 gives an implied usage context by saying to use it for multi-file scanning and architectural/code questions. However, it does not specify when to choose this over dispatch_gemini_agent or when this tool is inappropriate.

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 disclosure burden. 'Inspect' implies a read-only, non-mutating operation and the returned fields are named, but there is no statement about side effects, permissions/auth requirements, or behavior when no workspace matches.

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?

Two tightly packed sentences with no filler. The core capability is front-loaded, and the usage guidance follows immediately.

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?

With no output schema, the description compensates by enumerating the returned fields, which is sufficient for a simple read-only tool. The only gap is the unaddressed behavior of the optional target_workspace filter (match failure semantics).

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 coverage is 100%, so the single optional target_workspace parameter is already documented in the schema. The description never mentions the filtering parameter or what happens on a non-match, so it adds no meaning beyond structured data; baseline 3 applies.

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?

A specific verb ('Inspect') plus a specific resource ('live Antigravity IDE instances'), followed by the exact fields returned (workspace path, PID, port, connection status). It is clearly distinguishable from siblings like dispatch_gemini_agent or list_active_jobs, though it does not name them explicitly.

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?

Explicitly states when to call it: 'before dispatching file-modifying tasks to ensure the agent targets the correct workspace.' This gives clear context, but no exclusions or named alternatives (e.g., when to skip it in favor of list_active_jobs).

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 present, the description carries the full behavioral burden and discloses almost nothing: no ordering (most recent first?), no pagination/limit behavior, no scope of jobs, and no indication of what a job entry contains. The only real signal is that all statuses are returned, which contradicts the tool name's implication of actives-only.

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?

One compact sentence with no filler, front-loaded with the verb and resource. It is efficient, though the status enumeration carries most of the weight and could be paired with one clause on scope.

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 simple single-parameter list tool with no output schema, the definition covers purpose but omits ordering, scope, and result shape, leaving the agent to guess what a returned job record looks like. Adequate but clearly thin.

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 single 'limit' parameter is fully described in the schema ('Maximum number of recent jobs to return'), so the schema does the work. The description adds no ordering or default-value context beyond it, which is the baseline expectation at 100% 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 gives a clear verb ('List') and resource ('subagent jobs') and usefully enumerates the statuses returned, which corrects the misleading 'active' in the name. It does not, however, distinguish itself from sibling check_agent_job or explain scope (own jobs vs all jobs).

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?

There is no guidance on when to use this rather than check_agent_job (single job status) or the dispatch tools. The reader must infer that this is the enumeration counterpart, but nothing in the text says so.

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 behavioral burden. It usefully signals that results are session-scoped and dynamic ('live', 'currently available'), but says nothing about what the listing returns (names vs. full metadata), whether it is cached, or any failure mode when no session is connected. Adequate but incomplete for a tool with zero annotation coverage and no output schema.

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?

Two sentences, zero waste, with the resource and scope front-loaded and the usage condition following. Every clause 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?

For a zero-parameter list tool, purpose and the routing rule to dispatch_gemini_agent are covered. The only gap is the return shape, which matters slightly more here because no output schema exists to describe it.

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 takes no parameters, so there is nothing for the description to disambiguate and the schema is trivially complete. Baseline 4 applies for a zero-parameter tool.

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?

States a specific verb (List) and resource (live models available from the connected Antigravity IDE session), and scopes it to the current session rather than a static catalogue. It also names the downstream consumer, dispatch_gemini_agent, so an agent can place it among its siblings.

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?

Gives an explicit call condition: invoke before dispatch_gemini_agent when the exact, currently-live model name is unknown. That is a concrete when-to-use trigger tied to a named sibling, leaving little to inference.

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