dora-mcp

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

Annotations already cover the safety profile (readOnlyHint=true, destructiveHint=false, openWorldHint=false), and the description adds genuinely useful behavior beyond that: the possible terminal states (pending|done|failed), the result_url returned on completion, and the polling latency/duration expectations. Missing only error/retry behavior on a 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?

Three tight sentences, front-loaded with purpose, then return values, then the polling constraint. Every sentence carries distinct information and nothing is padded.

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 correctly compensates by enumerating return values (status states plus result_url). For a single-param polling tool this is nearly complete; the only unresolved piece is the provenance of job_id and how to handle a 'failed' job.

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 0% and job_id is the sole parameter, yet the description never explains where job_id comes from (presumably the generate_image/generate_video response). The context makes this inferable, but the definition does not add the meaning the schema lacks.

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 ('check the status of a generation job') and immediately scopes it as a polling/status-check operation, which clearly separates it from the generate_image, generate_video, and list_models siblings. An agent can identify the right tool without opening any schema.

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 concrete usage guidance for polling cadence ('wait at least 30 seconds between polls') and expected duration ('1-5 minutes typically'), which is exactly what an agent needs to avoid hammering the endpoint. It does not, however, state when not to use it or what alternative exists for retrieving results outside this job flow.

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?

Annotations already declare non-read-only, non-destructive, non-open-world. The description adds valuable unsurfaced context: approximate coin costs per model, that reference photos are supported, the 7-input cap, and the async workflow (returns job_id, poll check_job). It does not state failure modes or whether images persist, but the added operational context is substantial.

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 the default action, then layered decisions (model, resolution, aspect ratio, references, polling). Every sentence carries a decision the agent must make; no filler.

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 supplies the return-value workflow (job_id then poll check_job), the default model/resolution, and the reference-image model constraints — enough for an agent to invoke and follow through correctly on a 6-parameter generation 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?

Schema coverage is only 50%, so the description must compensate, and it does: it gives cost/quality tradeoffs for the model enum values, states the default resolution and why not to raise it, and explains the aspect_ratio enum values in plain terms (9:16 vertical, 16:9 horizontal, 1:1 square). It adds meaning beyond the raw enums, though a few enum values (google_nano_banana_edit, gpt4o_image, gpt_image_2) are never characterized.

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?

Opens with a specific verb+resource ('Generate an image') and immediately differentiates itself from the sibling generate_video by scope. The model-selection preamble makes the tool's role unambiguous without opening the schema.

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-to-use rules for each model (nano_banana_2 default, z_image only for cheapest, nano_banana_pro only for face preservation/max quality), a resolution rule (never 2K/4K unless asked), a mandated clarifying question for aspect ratio, and guidance on reference_image_urls for multi-character shots.

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?

Annotations only carry the generic readOnly/destructive/openWorld flags; the description adds the substantive behavior an agent needs: cost scaling across resolution and audio, the ~11 coins/s minimum billing for motion control, the required pairing of image_url with motion_video_url, and the async contract (returns job_id, poll check_job until status=done). That is real information beyond structured fields.

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?

Dense and front-loaded — defaults first, then the question protocol, then the special cases — with almost every sentence carrying operational payload. It runs long as a single block and partly duplicates the schema's aspect-ratio and duration enumerations, which keeps it out of the top tier.

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 9-parameter, no-output-schema, multi-model generation tool, the description covers the missing pieces: defaults, cost tradeoffs, the ask-first decision rule, model-specific parameter requirements, and the polling workflow after job_id is returned. Nothing needed to invoke it correctly is absent.

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?

With schema coverage at 56%, the description carries much of the load: it names the default model and its 480p/8s/no-audio configuration, explains what each reference parameter is for (character anchors, multi-person, style), and tells the agent to set duration to the driving clip length. A few parameters (aspect_ratio values, duration set) are still only fully specified in the schema, so it is a 4 rather than a 5.

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 clear verb+resource ("Generate a video") and immediately reveals it is a multi-model dispatcher with defaults, which separates it from generate_image and check_job. The opening sentence is mostly a restatement of the tool name, so it just misses a 5, but the follow-on specifics make the purpose 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?

Explicitly prescribes agent behavior: ask the user for aspect ratio/duration/audio/resolution before calling, skip the questions when details are already given or the user asked for "the cheapest." It also routes a specific sub-case (character consistency, motion control) to the right parameters and model, leaving nothing 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?

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so safety is covered. The description adds genuinely new behavior: results are ordered cheapest-first, each entry carries a per-call coin cost, and the default model choice per media type. It does not describe result volume or whether the list is exhaustive, which is a minor 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?

Three dense sentences, front-loaded with what the tool returns, then when to call it, then the default/escalation policy. No filler and no repetition of the title or annotations.

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, read-only list tool with no output schema, the description covers everything needed: what is listed, how it is ordered, what each entry contains (coin cost), and how to use the result to pick a model. Safety is already carried by annotations.

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 zero parameters, so the schema baseline is 4. The description still contributes model-level semantics (the per-media-type defaults) that shape how the caller should act on the returned list.

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 ('List Dora generation models') plus two distinguishing traits: cheapest-first ordering and per-call coin cost. An agent can tell this apart from generate_image/generate_video/check_job at a glance.

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 names three triggering conditions (user asks what models exist, cost matters, verifying a model name) and gives an escalation rule with named defaults (nano_banana_2 for images, seedance_1_5_pro for videos) plus the condition for stepping up. Nothing is left to inference.

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