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

No annotations are present, so the description carries full burden. It discloses the lack of a read endpoint, use of a local mirror file, the exact API calls (tRPC create, PATCH update), 0-credit cost, and error behavior (S301 on invalid presetVoiceId). This is far beyond basic and honest about limitations.

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?

Front-loaded with purpose and workflow, but somewhat long with minor redundancy (0-credit mentioned twice, repeated multilingual triggers). Still, every section (limitation, when, next) earns its place; the density is justified by the tool's 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?

Despite no output schema, the description covers return values (entityId from create's response), the mirror file location, integration with flow_generate_image, and error handling. An agent has all necessary information to invoke it 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 coverage is 100%, but the description adds critical context: imageMediaIds are resolved to workflowIds automatically, presetVoiceId is validated pre-submit, action=create vs update semantics, and the empty collectionId workaround. This enriches the schema significantly.

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+resource: 'Create/update CHARACTER entities and bind a preset voice'. It clearly distinguishes from siblings (flow_generate_image/video/status) by focusing on entity management and voice binding, not generation. The multilingual triggers further reinforce the intent.

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?

Explicit 'WHEN' conditions (角色卡 / 建角色 / character entity / bind voice) and an explicit 'NEXT' sequence telling the agent to first call flow_generate_image, then flow_entity. It also clarifies when NOT to rely on server reads (listing from local mirror due to no read endpoint), giving full routing guidance.

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, and it excels. It discloses zero credit usage, no API key/quota, local Chrome CDP session, local output download with returned path, exact aspect/seed honoring, per-image mediaId+seed in output, re-download via flow_status, image-to-image behavior, upscale mode (prompt ignored), and fallback behavior on S1xx errors. This is far beyond what annotations alone would provide, offering deep behavioral insight.

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 long (~600 words) but well-structured with bolded lead-ins (PREFER, WHEN, Image-to-image, UPSCALE, NEXT, Multilingual). It is front-loaded with the most critical info (0 credits, fallback condition). Every section serves a purpose given the tool's multiple modes. It's not overly verbose for its complexity, though it could be trimmed slightly (e.g., the multilingual list is somewhat tangential). Overall, good structure earned a 4.

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 tool with three distinct modes (txt2img, img2img, upscale) and fallback logic, the description is remarkably complete. It covers purpose, usage, exclusions, behavioral details, parameter nuances, output handling, and how to check status via flow_status. With no output schema and no annotations, this description leaves little for the agent to guess. It fully equips the agent to call the tool correctly in all scenarios.

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 90%, so the baseline is 3. The description adds significant value beyond the schema: it names the three models with their aliases (NARWHAL/Nano Banana 2, HARBOR_SEAL/Lite, GEM_PIX_2/Nano Banana Pro with up to 10 refs), clarifies that aspect is honored exactly (schema says 'Maps to nearest' but description says 'exact'), explains images[0] as base image and images[1..10] as references, and details upscale mode prerequisites. It doesn't cover every parameter (e.g., outDir, download), but the schema already does. The added model semantics and mode behavior justify a 4.

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 explicitly states the tool's purpose: 'Generate or edit an AI image via Google Flow' with a specific verb and resource. It differentiates itself from siblings by naming flow_generate_video, flow_status, and flow_entity implicitly (it's the image-specific one) and explicitly mentions the alternative media-gen-mcp generate_image. The opening 'PREFER this tool for AI image generation' makes its role unambiguous.

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 gives clear when-to-use guidance: 'WHEN: user wants photorealistic or illustrated AI images.' It also provides explicit exclusion criteria: 'Only fall back to other image tools... when this tool fails with [flow] S1xx.' It even instructs the agent to check flow_status first and mentions multilingual triggers for intent detection. This is exemplary usage guidance.

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 must carry the full behavioral burden, and it does: it discloses credit costs per model, submit-only async behavior, the mediaId handle return, polling requirements, off-grid duration snapping, aspect ratio constraints, and structured error behavior for invalid inputs. This is unusually transparent.

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 long but information-dense, with the most critical warning (credit cost) front-loaded in red. Each sentence serves a purpose: mode list, submit-only behavior, model key syntax, next steps, multilingual triggers. The structure is logical, though the sheer density might overwhelm some agents; still, it earns its length.

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 an 11-parameter tool with no output schema, the description covers all necessary context: every mode, parameter interactions, credit implications, async flow, polling, language triggers, and even the live catalog via flow_status. It leaves no obvious gap in what an agent needs to invoke it 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 coverage is 100%, so the baseline is 3. The description adds value beyond the schema by explaining the model key semantics (mnemonic+durationSeconds, credit tiers), the exact usage of videoMediaId for each mode, and mutual exclusivity rules (image vs keyframes vs videoMediaId). It does not repeat schema descriptions but complements them with operational context.

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 an explicit verb+resource ('Submit an AI video generation to Google Flow') and distinguishes it from siblings by detailing the video-specific modes (t2v/i2v/r2v/interpolation/extension/upsampler/edit) and the explicit-only routing rule. It clearly differentiates from flow_generate_image and flow_status.

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?

Provides explicit when-to-use ('USE ONLY when the user explicitly asks for Flow / Veo / abra video') and when-not-to-use via the config note. It also tells the agent the exact follow-up sequence (poll with flow_status, re-call with download=true) and lists alternative tools implicitly by covering all video generation paths.

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 zero annotations, the description carries the full burden, and it delivers richly. It discloses the zero-credit cost model, the local CDP-backed architecture (no API keys), IRREVERSIBLE delete semantics, the thumbnail S402 false-positive by design, and live-verified boundaries (image in-flight cancel returns 404; video cancel wire-verified but not live-submitted). This goes well beyond what structured annotations would typically provide.

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 long, but the tool genuinely implements six distinct modes within one schema, so the length is justified. Critical constraints (zero-credit, irreversibility, never-submits-generation) are front-loaded in the first sentence block. Minor deduction: the architecture/CDP explanation and multilingual triggers are secondary details that could sit lower without losing the core guidance.

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 tool with 8 optional params, 6 modes, and no output schema, the description is remarkably complete: it names the backend endpoints invoked (tRPC flow.share.shareMedia, POST /v1/flowMedia:cancelGeneration, POST /v1/flow:batchDeleteAssets), documents the share URL format, and gives expected status transitions (in_progress → retry_after_seconds, MEDIA_GENERATION_STATUS_CANCELED). The lack of an output schema pushes more responsibility onto the description, which it mostly meets; brief mention of the full snapshot fields and status=completed contract covers 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?

Schema coverage is 100%, so the baseline is 3. The description adds genuine value beyond the schema: mutual-exclusivity relationships among mediaId/deleteMediaIds/shareMediaIds/cancelMediaIds, the batch-delete all-or-nothing behavior (S400 on unknown id), the download=false default for fast polling, and the cancel re-check expectation. These cross-parameter semantics are not expressible in the input schema, so the description earns an above-baseline score.

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 resource (Google Flow), a clear verb set (introspect/status/download/delete/share/cancel), and explicitly differentiates from siblings: 'NEVER submits generation — video/image submission goes through flow_generate_video / flow_generate_image.' The multi-mode nature is clearly delineated, and an agent can immediately tell this is the management/polling counterpart to the generation 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?

An explicit WHEN section lists the six trigger conditions (preflight, poll mediaId, fetch asset, check credits, clean up, share, cancel). The NEXT section describes the full generation-to-polling workflow with the sibling tool, and the description states exclusions ('NEVER submits generation'). Routing between this and the generation siblings is unambiguous.

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