@runapi.ai/happyhorse-mcp

Official

by runapi-ai

Overview Schema Related Servers Score Discussions

TypeScript

Remote

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.8
Disambiguation5/5
Each tool has a clearly distinct purpose: authentication, three video generation modes, task status retrieval, and pricing lookup. No two tools overlap in functionality.
Naming Consistency4/5
The naming pattern is mostly verb_noun (e.g., get_task, check_pricing) but the video generation tools break this with image_to_video and text_to_video, while edit_video fits the verb pattern. The mix is still readable and predictable in context.
Tool Count5/5
With 6 tools, the server is well-scoped for video generation and task management. Each tool covers a necessary part of the workflow without unnecessary bloat.
Completeness4/5
The surface covers login, task creation, status retrieval, and pricing. A task listing or cancellation tool would be a minor addition, but the primary use cases are fully supported.
Average 3.3/5 across 6 of 6 tools scored. Lowest: 2.7/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 12 commits in the last 12 weeks
- Last stable release on July 31, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description is the sole source of behavioral information. It discloses that the tool returns a task id, status, and output URLs, but fails to mention async behavior, auth requirements, rate limits, or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, concise and front-loaded with the primary action. However, it could be better structured by separating purpose and return information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (11 parameters, no output schema, no annotations), the description is severely incomplete. It lacks instructions on required inputs, authentication (likely needed given login sibling), and detailed return format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is low (18%), and the tool description adds nothing about parameters. It does not explain the meaning or usage of any of the 11 parameters, leaving the agent without necessary context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool creates a HappyHorse task for editing video on RunAPI, using a specific verb (Create) and resource (HappyHorse task). It distinguishes from sibling tools like text_to_video by explicitly mentioning 'edit video'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives (e.g., text_to_video). There is no mention of prerequisites, when not to use it, or comparison with siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions return values but does not disclose async behavior, waiting/polling semantics (despite a 'wait' parameter and timeout options), potential side effects, or prerequisites. This leaves significant behavioral ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence, front-loaded with the key action and result. It is efficient with words, though it sacrifices depth. This is not overly verbose, so it scores well on conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 10 parameters, no output schema, and no annotations, this description is insufficient. It does not explain the task lifecycle, parameter usage, or how to configure the request. It provides only a minimal overview, which is inadequate for a tool of this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 20% (2 of 10 parameters described), and the tool description adds no parameter information. It fails to compensate for the low schema coverage, leaving users without guidance on parameters like prompt, seed, duration_seconds, etc.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the action: 'Create a HappyHorse task on RunAPI (image to video).' It specifies the verb, resource, and scope, and differentiates from sibling text_to_video by emphasizing 'image to video.' It also notes the return value (task id, status, output URLs).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The context is implied through 'image to video,' suggesting when to use it, but there is no explicit guidance on alternatives or when not to use. It does not mention relative to text_to_video or other sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description must disclose behavioral traits. It mentions 'Returns a task id, status, and output URLs,' which gives some clue about the asynchronous nature, but it doesn't explain side effects, required auth, costs, or how long the task takes. Thus, it's minimally informative.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The entire description is one 14-word sentence that front-loads the verb and resource. It's efficient and every word contributes to the core message.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (11 parameters, 0 required, no output schema, no annotations), this high-level description is insufficient. It doesn't explain the task lifecycle, how to specify inputs, what the output URLs represent, or how this relates to get_task. The agent would be left guessing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 11 parameters with only 18% description coverage, and the tool description does not explain any parameters. It doesn't clarify which model to choose, what prompt format is expected, or what the options mean. The description adds no meaning beyond the bare field names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Create' and identifies the resource as a 'HappyHorse task on RunAPI' with the scope 'text to video,' which clearly distinguishes it from siblings like image_to_video and edit_video. It also states the return type (task id, status, output URLs).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'text to video' clearly implies the tool is for generating video from text prompts, which provides clear context relative to image_to_video. However, it doesn't explicitly mention alternatives or exclusions, so it falls slightly short of full guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description bears full burden. It indicates a read operation but does not disclose potential side effects, authentication requirements, or behavior for missing tasks. Minimal behavioral detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with no extraneous information. Front-loads the key action and resource. Efficient and to the point.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema provided, but description mentions 'status and latest result payload' without detailing their structure. Could be more complete for agent decision-making, but acceptable for a simple fetch tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so parameters are already described. Description adds no additional semantic context beyond what schema provides. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the verb 'fetch' and the resource 'current status and latest result payload for a happyhorse task'. It distinguishes from sibling tools like edit_video and text_to_video which are about creating/editing tasks, not fetching status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives. Does not mention prerequisites, context, or when not to use it. Simply describes the function without usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It discloses the browser-based flow and file saving, but does not mention potential side effects (e.g., overwriting existing config), required user interaction, or any retry/rate limit behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that conveys the key information without extraneous words. Every element contributes to understanding the tool's purpose and mechanism.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple authentication tool with one optional parameter and no output schema, the description is largely complete. It could mention the need for user interaction (browser popup) but otherwise covers the essential details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single parameter 'force', which is well-described in the schema. The tool description adds no further clarification about the parameter beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the action ('Authenticate RunAPI'), the method ('browser PKCE login flow'), and the outcome ('saving the API key to ~/.config/runapi/config.json'). It distinguishes itself from sibling tools which are unrelated video/pricing operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies login is for authentication but does not explicitly state when to use it (e.g., at session start) or when not to use it. The 'force' parameter hints at re-authentication scenarios, but no alternative tools or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden of behavioral disclosure. It does not explicitly state that the operation is read-only, nor does it mention response format or error behavior. While 'look up' suggests a safe read, the description lacks deeper context that could be expected from a tool with no annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that gets straight to the point. It is front-loaded with the verb and resource, and every word earns its place. No redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple pricing lookup with two optional parameters and no output schema, the description is largely sufficient. It could be improved by indicating the type of response (e.g., a list of prices), but it is not critical given the tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters (model and action) clearly described in the schema. The description adds no additional parameter information, so it meets the baseline for high schema coverage without extra value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('look up') and resource ('RunAPI pricing') with a clear scope ('happyhorse model line'). It unambiguously distinguishes this tool from sibling generation/editing tools like edit_video and image_to_video.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies usage for retrieving pricing information, providing enough context for when to use it. It does not explicitly state exclusions or mention alternatives, but the purpose is self-evident given the sibling tool set.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![happyhorse-mcp MCP server](https://glama.ai/mcp/servers/runapi-ai/happyhorse-mcp/badges/card.svg)](https://glama.ai/mcp/servers/runapi-ai/happyhorse-mcp)

Score Badge

Copy to your README.md:

[![happyhorse-mcp MCP server](https://glama.ai/mcp/servers/runapi-ai/happyhorse-mcp/badges/score.svg)](https://glama.ai/mcp/servers/runapi-ai/happyhorse-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/runapi-ai/happyhorse-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server