imagine-mcp
Server Quality Checklist
Latest release: v1.11.0
- Disambiguation5/5
Each tool maps to a clearly distinct function: help, understanding media, generating media, configuration, and opening the relay credential form. The only closely related pair is config/config__open_relay, but the descriptions make their boundary explicit.
Naming Consistency4/5Most tool names are simple lowercase imperatives (help, understand, generate), which is predictable and readable. config and config__open_relay break the bare-verb pattern slightly, and the double-underscore namespace is a minor deviation from an otherwise consistent style.
Tool Count5/5Five tools is well-scoped for a focused multimodal generation/understanding server. The set covers core media operations, documentation, and configuration without redundant or excessive entries.
Completeness4/5The essential workflows—understanding images/videos and generating images/videos—are covered, plus configuration and credential setup. The async video generation flow relies on calling generate again with a job_id rather than a dedicated status/cancel tool, which is a minor gap but not a blocker.
Average 3.7/5 across 5 of 5 tools scored. Lowest: 2.6/5.
See the Tool Scores section below for per-tool breakdowns.
- 32 of 33 community issues answered or closed in the last 6 months
- 177 commits in the last 12 weeks
- Last stable release on
- 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
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate destructiveHint=true and readOnlyHint=false. The description lists actions including 'cache_clear' (destructive) and mentions deprecated aliases. No contradiction, but adds limited context beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single dense sentence with parenthetical lists, making it hard to parse. It combines multiple action categories and deprecated aliases without clear structure, reducing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 3 parameters, no schema descriptions, and full behavioral burden on the description, it fails to cover return values, action semantics, or parameter usage. The output schema exists but is unmentioned.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain parameters (key, value, action). The agent gets no meaning beyond the schema types and required fields.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool handles server config and credential setup, listing specific actions. It distinguishes from sibling config__open_relay by noting that tool opens the credential form. However, the broad scope and dense list reduce clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description only offers one usage guideline: to use config__open_relay for opening the credential form. It does not explain when to use specific actions or how to choose among them, lacking guidance for the agent.
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?
Discloses async behavior for video, which is not captured by annotations; annotations already indicate write operation (readOnlyHint=false), consistent with generation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with no filler; first covers purpose, second covers critical async flow.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having output schema, the description lacks information on most parameters, return values, and image generation behavior, making it incomplete for a complex tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage and 10 parameters, description only mentions 'text prompt' and 'job_id' implicitly, leaving all other parameters undocumented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool generates an image or video from a text prompt, distinguishing it from sibling tools like config or help.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly mentions video is async and provides polling instructions, but does not specify when to use image vs video or provide alternatives.
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?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the description does not need to repeat those. The description adds no significant behavioral detail beyond 'full documentation', but it also does not contradict the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely compact, front-loaded with the core purpose, and every word adds information. No filler or unnecessary repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter help tool with strong annotations and an output schema, the description is mostly sufficient. The only notable gap is that the sibling config__open_relay is not reflected in the topic list, which could leave an agent unsure whether that tool has separate documentation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description's topic list provides the only meaningful guidance for the topic parameter. It enumerates valid values, which is valuable, though it does not explicitly mention the default value or explain each topic's content.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies this as a documentation/help tool and specifies the available topics: understand, generate, and config. It is unambiguous and distinguishable from the sibling tools, though it lacks an explicit action verb like 'returns' or 'displays'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The topic list implies that the tool should be used to retrieve documentation on those topics, but it never explicitly states when to prefer help over calling the sibling tools directly. Usage context is implied rather than 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?
Annotations already indicate readOnly, idempotent, non-destructive. Description adds provider-specific behavioral context (media type support), which is valuable beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, directly stating core function and provider distinction. No redundant information; front-loaded with the action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Output schema exists, so return values are covered. However, the description lacks parameter documentation, which is a significant gap given 6 parameters and 0% schema coverage. Overall adequate but incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain any parameters (e.g., media_urls, prompt, tier, model). The agent must rely solely on parameter names, which is insufficient for correct usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool understands images/videos with a prompt, and distinguishes provider capabilities (Gemini supports mixed media, others image-only). Verb+resource+scope is specific, and no sibling tool performs this function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Description provides context on provider differences (mixed vs. image-only), helping select provider. However, it does not explicitly state when to use this tool vs. alternatives or when not to use it.
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 provided, the description carries the full behavioral burden. It transparently discloses the key side effect of launching the user's browser 'when possible' and lists what will be returned. It could add more about failure conditions or permissions, but for a zero-parameter tool the disclosure is solid.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no wasted words. The primary action is front-loaded, the side effect is stated, and the return contents are summarized. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters and an output schema exists to document return values, the description provides all necessary context: what the tool does, what side effect it may have, and what it returns. Nothing essential is missing for an agent to select and invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so there is no parameter schema to elaborate on. The description appropriately focuses on the operation and its outputs, meeting the baseline for a parameterless tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Get'), a specific resource ('relay configuration URL for imagine-mcp'), and a clear side effect ('opening it in the user's browser when possible'). It is easy to distinguish from the generic sibling 'config' because it names the exact operation and output.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool: when the agent needs the relay configuration URL and wants to attempt opening it in the browser. However, it does not explicitly mention when not to use it or compare it with the sibling 'config' tool, leaving some inference to the agent.
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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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.
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