@runapi.ai/happyhorse-mcp

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

With no annotations, the description carries the full burden. 'Look up' strongly implies a safe, non-mutating read, which is useful, but nothing is said about auth requirements, rate limits, or what pricing figures represent (per-call, per-second, currency). Adequate but thin for a tool with zero annotation coverage.

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 no filler or redundant restatement of the tool name. Nothing could be trimmed without losing information.

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 tool is simple (two optional params, complete schema, no output schema), so the description is close to sufficient, but it omits any usage context and gives no hint of what the pricing response expresses. It is the minimum viable for calling the tool, not a complete briefing.

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 'model' and 'action' parameters are already fully documented with defaults and an enum. The description adds only the 'happyhorse model line' framing, which is marginally useful but also slightly narrower than the schema, which accepts an arbitrary model slug.

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?

Clear verb+resource+scope: 'Look up RunAPI pricing' names both the action and the subject, and 'happyhorse model line' narrows the domain. It does not, however, differentiate itself from the sibling tools (edit_video, image_to_video, text_to_video), whose names also appear as the 'action' enum values in this tool's schema, leaving an ambiguity an agent must resolve itself.

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 mention of prerequisites, and no reference to any alternative tool. The likely workflow (check cost before invoking an endpoint) is left entirely 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 provided, the description carries the full burden, and it does disclose useful behavioral context: this is an asynchronous task creation that returns a task id, status, and output URLs. However, it says nothing about authentication (a sibling 'login' tool implies auth is needed), cost, or what happens to the source video, which leaves meaningful gaps for a mutation endpoint.

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 tight sentences with the action front-loaded and no filler; the parenthetical '(edit video)' is mildly redundant with the tool name but aids scannability. Nothing else is wasted.

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, single-required-argument async video tool with no annotations and no output schema, the description covers the return shape but omits key operational context: the required source_video_url, the synchronous vs. polling (wait/timeout_ms) behavior, and auth prerequisites. It is minimally adequate but not complete.

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 82%, well above the baseline threshold, so the schema already documents most parameters (wait=poll until terminal, audio_setting enum-known values, output_resolution values). The description adds no parameter semantics at all, so a 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+resource: it creates a HappyHorse task that edits video, and notes the RunAPI platform. It does not explicitly differentiate itself from siblings like image_to_video or text_to_video (e.g., by noting it takes an existing source video), so it falls short of a 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 exclusions, and no mention of alternatives such as image_to_video or text_to_video. The agent must infer from the tool name that this is the variant for editing an existing video rather than generating one from scratch.

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, and it only partially does so. It implies a safe read and identifies the return content (status plus latest payload), but says nothing about auth requirements, rate limits, behavior for unknown/completed/failed task ids, or whether repeated calls are safe.

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 tight sentence with the key return content front-loaded and zero filler. Nothing is padded and nothing essential is buried.

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 tool is simple (2 required params, no nesting), but with no annotations and no output schema the description should explain the returned status/result shape and error cases for stale or invalid ids. It gestures at the payload but does not complete the picture an agent needs to poll reliably.

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 both task_id and the action enum are already documented in the schema, including the note that action is the async endpoint used at creation. The description adds no syntax, format, or matching-constraint detail beyond that, so the 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?

States a specific verb ("Fetch") and resource (task status and latest result payload), which is unambiguous against siblings like edit_video or check_pricing. However, it never names or contrasts with a sibling tool, so it stops short of the 5-level differentiation.

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 no when-to-use guidance: it doesn't say this is for polling an asynchronously created task, when to call it repeatedly, or when not to call it. The polling context must be inferred from "current status."

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. It reveals that a task is created and what comes back (task id, status, output URLs), which is useful, but says nothing about async/polling behavior despite a 'wait' parameter defaulting to true, nor about auth, cost, rate limits, or failure modes.

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 compact sentences with the action front-loaded and the return contract immediately after; no filler. It is efficient, though it spends its second sentence on return values while leaving usage and behavioral context unaddressed.

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 10 parameters, no annotations, and no output schema, the description does its duty of describing the return shape, which is genuinely helpful. However, for an async task-creation tool it omits polling/termination semantics and model selection context that an agent needs to invoke it confidently.

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 80%, so the baseline is 3. The description adds no parameter-level meaning at all — nothing about seed, model slug choice, duration_seconds, output_resolution, or how wait/timeout_ms/poll_interval_ms interact.

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 (Create) and resource (a HappyHorse task on RunAPI) and names the modality ('image to video'), which implicitly separates it from text_to_video and edit_video. It stops short of explicitly naming those siblings or stating what artifact it operates on, so it lands below the 5 bar.

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 or when-not-to-use guidance, no mention of prerequisites (e.g. an existing image URL), and no routing to the sibling tools (text_to_video, edit_video, get_task) that a caller could easily confuse this with. The modality label only vaguely implies usage.

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 behavioral burden. It discloses the interactive browser flow and the file write side effect (config.json). However, it does not mention that it may overwrite existing credentials or that it could block waiting for user input, though these are implied.

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 a single, well-structured sentence that front-loads the action ('Authenticate RunAPI') and provides necessary details without extraneous information.

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 login tool with one optional parameter and no output schema, the description covers the core purpose and side effect. It lacks an explicit statement that this is a prerequisite for other tools, but that is implied.

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% (the only parameter 'force' has a description). The tool description adds no additional meaning about parameters 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 clearly states the tool's purpose with a specific verb ('Authenticate'), target resource ('RunAPI'), method ('browser PKCE login flow'), and side effect (saving to config.json). It is distinct from sibling tools, none of which relate to authentication.

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 (to authenticate RunAPI) but does not explicitly say when to run it (e.g., before other RunAPI tools) or when to use the 'force' parameter. Since there are no alternative auth tools among siblings, 'vs alternatives' is not applicable.

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 burden, and it discloses very little: it omits that this is an asynchronous task creation, that cost may be incurred, whether auth is required, and how the 'wait' default (true) changes behavior. It adds only the shape of the return (task id, status, output URLs), which is the one genuinely useful behavioral hint.

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 compact sentences with the purpose front-loaded and the return contract second. Nothing is padded, though it is thin rather than efficient-with-substance.

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 11 parameters, zero required, no annotations, and no output schema, the description is only marginally sufficient. It does cover the return shape (task id, status, output URLs), which partially compensates for the missing output schema, but the async/cost/auth behavior of a task-creation tool is left entirely unstated.

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 82%, so the schema already documents most parameters (including enum-like known values for aspect_ratio and output_resolution). The description adds no parameter-level meaning, which is acceptable at this coverage level but earns no uplift.

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 concrete verb and resource ('Create a HappyHorse task on RunAPI') and the modality qualifier '(text to video)' distinguishes it from image_to_video and edit_video. It stops short of explicitly naming or contrasting those siblings, but the resource+modality pairing is 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?

There is no guidance on when to choose this over image_to_video or edit_video, and no mention of prerequisites such as authentication or pricing checks despite check_pricing and login being siblings. The agent must infer selection purely from the modality parenthetical.

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