@runapi.ai/gemini-omni-mcp

Server Quality Checklist

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Latest release: v0.2.1
Disambiguation5/5
Each tool targets a distinct operation: authentication, video generation, audio generation, character creation, task retrieval, and pricing. The similar descriptions for create_audio and create_character are clarified by their names, leaving no real ambiguity.
Naming Consistency3/5
Most tools follow a verb_noun pattern (create_audio, create_character, get_task, check_pricing), but 'login' is a bare verb and 'text_to_video' is an action-object phrase without a leading verb. This mix is readable but not fully consistent.
Tool Count5/5
Six tools is a well-scoped set for a Gemini Omni integration, covering authentication, content generation, task status, and pricing. Each tool has a clear purpose and the count is appropriate for the server's domain.
Completeness4/5
The server covers core generation types (video, audio, character) and task status, but lacks operations like listing or canceling tasks. The missing lifecycle operations are minor and workable for most workflows.
Average 3.4/5 across 6 of 6 tools scored. Lowest: 2.8/5.
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.
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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 carries the full burden of behavioral disclosure. It mentions the operation is synchronous and returns a result, but omits critical details such as side effects (is it a write/creation? are resources persisted?), authentication requirements, possible failure modes, or whether the operation is idempotent. This is insufficient for a create operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short (two sentences) but not optimally front-loaded. The first sentence 'Run a synchronous Gemini Omni operation on RunAPI (create character)' is indirect; a more direct phrasing like 'Creates a character using Gemini Omni on RunAPI' would be clearer. The second sentence about the return value is useful, but the structure feels slightly awkward.
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?
The tool has 5 parameters, no output schema, and the description does not explain what the operation result actually contains (e.g., a task ID, character data, or error details). There is also no mention of how this tool fits into a workflow (e.g., whether it depends on create_audio to provide audio_ids). Given the complexity and lack of output schema, the description is incomplete.
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?
The input schema has 100% description coverage for all 5 parameters, so the schema already explains the meaning of fields like descriptions and reference_image_url. The description adds no additional parameter semantics beyond what the schema provides, making a baseline score of 3 appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies the tool as creating a character via a Gemini Omni operation on RunAPI, which is a specific verb+resource combination. It distinguishes from siblings like text_to_video and create_audio by focusing on character creation, though the phrase 'synchronous Gemini Omni operation' adds jargon without fully clarifying the action.
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 such as create_audio or text_to_video. No exclusions, prerequisites, or contextual triggers are mentioned, leaving the agent to infer usage from the tool name alone.
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 must carry the full burden of behavioral disclosure. It only mentions the return values (task id, status, output URLs) but does not explain asynchronous behavior, polling semantics, or that output URLs may not be immediately available. The default `wait=true` parameter is not mentioned, making the behavior unclear.
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 with no filler or redundancy. It is front-loaded with the core action. However, it is perhaps too brief for a tool of this complexity, though this is more a completeness issue than a conciseness one.
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?
For a tool with 14 parameters, no output schema, and no annotations, this description is severely under-specified. It fails to explain the task lifecycle, the wait/poll behavior, webhook options, or how to interpret the returned status. The description only scratches the surface of what the tool does.
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 description provides no parameter information whatsoever. With schema description coverage at only 43%, many parameters (seed, audio_ids, video_list, character_ids, duration_seconds, poll_interval_ms, etc.) remain undocumented in both the schema and the description. The tool description does nothing to compensate for this low coverage.
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 the action ('Create') and the resource ('Gemini Omni task') with an explicit parenthetical 'text to video' that distinguishes it from sibling tools like create_audio and create_character. It is specific and immediately understandable.
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 use for text-to-video generation but offers no explicit guidance on when to choose this over create_audio or create_character. No alternatives or exclusion criteria are mentioned, leaving usage context implicit rather than explicit.
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?
The description discloses that the operation is synchronous and returns the operation result, which adds behavioral context beyond the schema. However, it does not explain side effects, permissions, or error behavior, leaving a moderate gap given the absence of 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 two short sentences, front-loading the action and following with return behavior. It contains no redundant or filler content.
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?
With no output schema, the description's mention of returning the operation result is useful but underspecified. It does not clarify the result structure or common use cases, leaving the description only partially complete for a 5-parameter 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?
The input schema has 100% parameter description coverage, so the schema already defines each parameter. The description adds no parameter-specific meaning, making the baseline score of 3 appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies a synchronous Gemini Omni operation, with the parenthetical 'create audio' clarifying the tool's purpose. This distinguishes it from siblings like text_to_video and create_character, though the generic verb 'run' and lack of explicit 'preset' wording slightly reduce specificity.
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 is provided on when to use this tool versus alternatives. Sibling tools exist (create_character, text_to_video), but the description does not mention conditions, exclusions, or selection criteria.
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 provided, so the description carries the full burden. It discloses a read operation (fetch) but does not mention any side effects, auth requirements, or error conditions. Adequate for a simple fetch but lacks depth.
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, front-loaded with the action and resource, no unnecessary words. Highly concise and efficient.
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?
Given the simplicity (2 params, no output schema), the description is sufficient but could be improved by hinting at the return format or any important limitations. Not incomplete, but not rich.
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%, and the description does not add any parameter-specific details beyond what the schema already provides. Baseline 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?
The description clearly states the tool fetches current status and latest result payload for a gemini-omni task, using a specific verb and resource. It implicitly distinguishes from siblings like text_to_video which creates tasks.
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 explicit guidance on when to use this tool versus alternatives, such as checking task status versus creating a new task. The agent must infer usage from 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 carries the full burden. It implies a read-only operation via 'look up' but does not explicitly state that it has no side effects, whether authentication is required, or what exactly is returned. The description adds minimal behavioral context beyond the tool name.
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 is front-loaded with the action and resource. Every word contributes meaning, with no redundant or filler content.
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 lookup tool with only two parameters and no output schema, the description is adequate. It clearly identifies the resource and scope. However, it does not describe the return format or any caveats, leaving a small gap for an otherwise simple 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 description coverage is 100%, with both parameters ('model' and 'action') fully documented. The description adds no additional parameter details beyond what the schema already provides, so the 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?
The description clearly states the tool's function: 'Look up RunAPI pricing for the gemini-omni model line.' It uses a specific verb ('look up') and resource ('RunAPI pricing'), and distinguishes itself from sibling tools like create_audio and text_to_video by focusing on pricing rather than generation.
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 use for pricing lookups, but it does not explicitly state when to use this tool vs alternatives or provide exclusions. Sibling tool names suggest they serve different purposes, but no direct guidance is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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.
Conciseness5/5
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.
Completeness4/5
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.
Parameters3/5
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.
Purpose5/5
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.
Usage Guidelines3/5
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.

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