Ideogram V3 MCP Server

Server Quality Checklist

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Latest release: v0.1.7
Disambiguation5/5
Each tool serves a clearly distinct purpose: authentication, pricing lookup, task status retrieval, and four different image generation modes (text-to-image, edit, reframe, remix). There is no functional overlap, so an agent can easily select the correct tool.
Naming Consistency4/5
Most tool names follow a consistent verb_noun pattern (e.g., get_task, edit_image, check_pricing), but 'login' is a bare verb and 'text_to_image' is a compound descriptor rather than a verb+object structure. These minor deviations are still readable and predictable.
Tool Count5/5
Seven tools is well within the ideal range for a domain-specific server, covering authentication, pricing, all major generation modes, and task status checking without unnecessary redundancy or bloat.
Completeness4/5
The core image generation workflows are well represented, including text-to-image, editing, reframing, and remixing, plus status retrieval. Minor gaps exist, such as no task cancellation or listing, but these are not critical for the primary use case.
Average 3/5 across 7 of 7 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 11 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.
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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?
No annotations are provided, so the description carries the full burden. It mentions returning a task id, status, and output URLs, which implies an asynchronous task model, but it doesn't disclose that the tool can poll (via 'wait') or other behavioral aspects like error handling or side effects. This is only slightly more informative than typical bare descriptions.
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 appropriately concise and front-loaded: the first sentence identifies the core action and the second mentions return values. It avoids unnecessary words, though its brevity sacrifices detail that might be valuable.
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?
The tool has 14 parameters, no output schema, and no annotations, making it a complex tool. The description only covers the basic purpose and return values, leaving out critical information about parameter usage, defaults, polling behavior, and how to interpret the response. It is far from complete for an AI agent to invoke correctly.
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 14% across 14 parameters, and the description provides zero information about any parameters. With such low coverage, the description must compensate by explaining parameter roles or relationships, but it does nothing, leaving the agent to infer everything from parameter names and types.
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 clearly states it creates an Ideogram V3 task for editing an image, providing a specific verb and resource. It mentions 'edit image' which distinguishes it somewhat from siblings like text_to_image, but it doesn't explicitly compare to reframe_image or remix_image.
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?
There is no guidance on when to use this tool versus alternatives. Sibling tools include reframe_image and remix_image, but the description offers no context for choosing one over the other, nor does it mention prerequisites or typical use cases.
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?
With no annotations provided, the description carries full responsibility for behavioral disclosure. It mentions returning a task id, status, and output URLs, which hints at asynchronous behavior, but it does not explain the async nature, potential side effects (e.g., credit usage), or whether the operation is destructive or requires authentication.
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 efficient sentence that packs in the action, resource, and return type. It is front-loaded with the key verb and object, and there is no redundancy or filler.
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 (18 parameters, no annotations, no output schema), the description is severely under-specified. It covers the basic purpose and return type but omits critical context such as asynchronous behavior, parameter semantics, and usage alternatives, making it far from complete.
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 11%, and the description adds no parameter explanations beyond the tool's basic purpose. The phrase 'remix image' hints at source_image_url, but none of the 18 parameters are clarified, leaving most of them undocumented.
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 clearly states a specific action ('Create a Ideogram V3 task') and names the resource ('RunAPI'), with the parenthetical '(remix image)' clarifying the operation type. However, it does not explicitly distinguish this tool from siblings like edit_image or reframe_image, which also involve image manipulation.
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. The description does not mention exclusions, prerequisites, or comparator to sibling tools like text_to_image or reframe_image, leaving the agent without usage context.
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?
The description only states that it returns a task id, status, and output URLs, but does not disclose that this creates an asynchronous task that may require polling (despite the wait parameter) or that it may incur costs. With no annotations, the side effects are 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 sentence with no filler, making it concise. However, it is under-specified, and the minor grammar issue ('a Ideogram') slightly detracts from professionalism.
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 11 parameters, no annotations, and no output schema, this description is too sparse. It does not explain the relationship to get_task for polling, the meaning of style/aspect_ratio/rendering_speed, or the expected response structure, making it insufficient for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has only 18% parameter description coverage, and the description adds no parameter details. The required source_image_url is not described in either the schema or the description, leaving a critical parameter ambiguous.
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 verb 'Create' with a specific resource ('Ideogram V3 task on RunAPI') and clarifies the operation with '(reframe image)'. This clearly distinguishes it from sibling tools like edit_image, remix_image, and text_to_image.
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 such as remix_image or text_to_image. Prerequisites like authentication or the asynchronous task workflow are not mentioned.
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 the full burden. It discloses that the tool returns a task id, status, and output URLs, but it omits behavioral details such as whether the task is asynchronous, whether the 'wait' parameter controls polling, authentication requirements, or potential side effects. This is minimal disclosure.
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 sentences, front-loaded with the core action, and contains no redundant information. Every word adds value.
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?
With 14 parameters, no output schema, and no annotations, the description provides only a basic purpose. It does not explain how to construct a valid request, what the response includes beyond IDs and URLs, or the effects of optional parameters. This is critically incomplete for a tool with 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 14% (2 of 14 parameters have descriptions). The description adds no parameter-level guidance, failing to explain the meaning or importance of parameters like prompt, aspect_ratio, or style. It does not compensate for the low schema 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 tool's function: 'Create a Ideogram V3 task on RunAPI (text to image)' and immediately notes the return value: 'Returns a task id, status, and output URLs.' This distinguishes it from sibling tools like edit_image or remix_image, which imply different 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 usage for text-to-image generation (as opposed to editing/reframing), but it does not explicitly state when to use this tool versus alternatives, nor does it list any exclusions or prerequisites. No guidance is given about scenarios where another sibling would be more appropriate.
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 the full burden of disclosing behavioral traits. However, the description only states the basic function with no information about rate limits, authentication requirements, data freshness, or any side effects. For a read-only pricing lookup tool, this is minimal but acceptable, yet it fails to provide any added behavioral context beyond the trivial.
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 clearly conveys the tool's purpose. There is no extraneous information, and it is well-structured for immediate understanding. Every word is necessary.
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 low complexity of the tool (two optional parameters, no nested objects, no output schema), the description is minimally adequate. However, it does not explain what the output looks like or any additional context (e.g., pricing format, units). For a simple lookup tool, this may be sufficient, but it is not fully complete.
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 already provides descriptions for both parameters (model and action), including enums and default behaviors. The description adds no additional meaning beyond what the schema offers. Since schema coverage is 100%, the baseline is 3, and the description does not elevate it.
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 clearly states the tool's purpose: 'Look up RunAPI pricing for the ideogram-v3 model line.' It specifies the verb (look up) and the resource (pricing for a specific model line). Although it does not explicitly distinguish itself from sibling tools, the siblings are action-oriented (edit_image, text_to_image, etc.), so the purpose is sufficiently clear.
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. The description only states what the tool does, with no mention of when it is appropriate or when to avoid it. Given the siblings are all action tools, the context implies usage for pricing queries, but explicit guidance is lacking.
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?
With no annotations provided, the description carries the transparency burden. The verb 'fetch' strongly implies a read-only, non-destructive operation, which provides some safety signal. However, it omits details about authentication requirements, error behavior on missing tasks, or whether the operation is idempotent, leaving gaps.
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, front-loaded sentence that immediately states the action and key detail. There is no wasted wording, and it is appropriately short for a simple two-parameter get operation.
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?
There is no output schema, so the description should explain what the response contains, but it only says 'current status and latest result payload' without elaborating on the format or how statuses progress. It also fails to mention the asynchronous polling use case, making the description incomplete for an agent to fully understand the tool's role in a workflow.
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%, so the input schema already fully documents both 'task_id' and 'action'. The description adds no additional parameter semantics beyond stating the task type, which is not needed. The baseline of 3 is 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 clearly states the tool fetches the current status and latest result payload for an ideogram-v3 task, which is a specific verb+resource pairing. While it doesn't explicitly differentiate from sibling tools, the sibling names are all action-creation endpoints, so the purpose is unambiguous.
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 does not provide any guidance on when to use this tool versus alternatives. It doesn't mention that it should be used after creating a task or for polling, nor does it reference any sibling tools as the creation counterparts. Usage context is implied but not stated.
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 full burden and does well: it discloses that a browser is opened (user interaction), the PKCE flow is used, and an API key is written to a specific file. It does not mention edge cases like headless environments or overwriting behavior, but the core behavioral traits are clearly stated.
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 primary action ('Authenticate') and efficiently packs the mechanism and side effect without redundancy. Every word adds value.
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 login tool with one optional parameter and no output schema, the description covers the essential workflow and side effects. It does not explain return values, but that is a minor gap given the tool's primary purpose is to modify local configuration. The parameter behavior is fully specified in the schema.
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% since the only parameter 'force' has a full description in the schema. The tool description adds no additional parameter-level meaning beyond what the schema already provides, so the baseline 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 uses a specific verb ('Authenticate') with a clear resource ('RunAPI') and mechanism ('browser PKCE login flow'), distinctly separating it from sibling tools that handle image operations or pricing. It also specifies the side effect (saving API key to a config path), leaving no ambiguity about the tool's function.
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 implies clear usage: call when authentication is required, as it is the only auth tool among siblings. It does not explicitly state when not to use it or compare with alternatives, but the context makes the intended use obvious. The parameter 'force' in the schema adds usage nuance for re-authentication, though the description itself does not elaborate.
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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