ViewMax MCP

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

With no annotations provided, the description carries the full behavioral burden and does so thoroughly. It discloses that the call is asynchronous, that rendering takes two to four minutes, that credits are charged and automatically refunded on failure, and that the agent should tell the user rendering is in progress rather than going silent.

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 dense but every sentence earns its place: async behavior first, then polling guidance, billing, model options, and default behavior. Bulleted model details are scannable, and the most important operational warning—do not poll faster than 15 seconds—is front-loaded.

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 async generation tool with seven parameters and no output schema, the description is unusually complete. It covers the response shape (job id), follow-up call, timing, billing, model selection, and parameter constraints, leaving an agent with everything needed to invoke and monitor the operation 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 100%, so the baseline is 3, but the description adds real value beyond the schema by explaining per-model capabilities, durations, resolutions, audio support, pricing differences, and the default model behavior. It also gives practical guidance like describing subject, action, camera move, and lighting, which goes beyond the schema's own wording.

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 opens with a specific verb and resource: 'Render a video from a text prompt using ViewMax Studio.' It also clearly distinguishes this tool from its sibling get_video by explaining that it returns a job id and that the clip is not ready yet, so an agent can immediately tell which tool to call first.

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 explicit follow-up instructions: call get_video with the returned id, wait about 15 seconds between checks, and do not poll faster. It also tells the agent when to use list_video_models for exact limits/prices and says to leave model unset for the default, leaving little ambiguity about workflow.

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 implies a safe read-only balance check ('has left', 'checking before booking'), ties the balance to the API key holder, and adds useful policy context that unpaid renders are refused. It does not explicitly say 'read-only' or 'does not consume credits,' but the wording strongly conveys that.

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, both purposeful: the first states the tool's result, the second gives a concrete, decision-relevant use case. There is no fluff or redundant 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?

For a zero-parameter balance getter, the description is nearly complete: it identifies the resource, the owner, and when to call it. It does not spell out the exact return format or error behavior for an invalid API key, but that is a minor gap given the simplicity of the 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?

There are zero parameters, so there is no parameter documentation burden. The description adds no parameter information, but none is needed; baseline 4 for zero-parameter tools is appropriate.

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 exactly what the tool returns: how many ViewMax Studio credits the API key holder has left. It clearly scopes to the API key holder and the ViewMax Studio credit resource, and it is unmistakably distinct from the video-generation siblings (generate_video, get_video, list_video_models).

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 explicit trigger: worth checking before booking an expensive render, plus the consequence that an unpaid render is refused outright. It does not name alternatives or exclusions, but the sibling tools are unrelated, so no exclusion guidance is really needed.

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 full burden. It discloses status values, polling delay, public URL on completion, and credit refund on failure—covering key side effects and expected behavior. Thorough and accurate.

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 tight and front-loaded with the core purpose. Every sentence adds value—status handling, timing, and outcome—without redundancy.

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 single-parameter tool with no output schema, the description fully covers the operational loop: how to interpret statuses, when to poll, what result to return, and what happens on failure. Nothing essential is missing.

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 already documents the parameter fully (job id returned by generate_video). The description adds no extra parameter meaning beyond the schema's coverage, so baseline 3 is appropriate.

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 ('look up'), resource ('a render started by generate_video'), and clarifies its role among siblings. It clearly distinguishes from generation, credit balance, and model listing 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?

Provides explicit polling instructions: wait 15 seconds while pending/processing, check again, and what to do on completed (hand URL) and failed (refunded). This gives clear when-to-use and how to act without needing external hints.

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 that the tool returns a list of all video models with their limits and credit prices, and that it is a read-only informational operation. It doesn't explicitly state that it makes no changes, but the verb 'list' and the content described make the read-only nature clear. It could add a note about no side effects, but the description is quite 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?

Two sentences, front-loaded with the core purpose and the exact data returned, followed by concrete use cases. Every sentence earns its place; no filler or repetition of the title.

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 with no output schema, the description is nearly complete. It tells the agent what data is returned and when to use it. The only minor gap is that it doesn't specify the response format (e.g., array of objects), but with no output schema, the description's enumeration of fields is sufficient for an agent to know what to expect.

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 zero parameters, so the schema provides no parameter documentation. The description compensates by explaining what the tool returns, which is the relevant semantic content. Since there are no parameters to document, the description fully covers the semantic space.

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 ('list') and resource ('video models'), and enumerates the exact attributes covered: lengths, resolutions, frame shapes, audio rendering, duration, and credit prices. This clearly distinguishes it from siblings like generate_video and get_credit_balance.

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 explicitly states when to use the tool: 'Use it to choose a model on price or capability, or to quote a cost before rendering.' This gives clear context and implies it should be used before generate_video, and it is not for checking balance (get_credit_balance) or retrieving a video (get_video).

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