Topaz MCP Server
Server Quality Checklist
Latest release: v0.1.7
- Disambiguation5/5
Each tool targets a clear, distinct action: authentication, creating an upscale task for a specific media type, retrieving task status, and checking pricing. The two upscale tools differ only by media type, which is explicitly stated in their names and descriptions.
Naming Consistency4/5Most tools follow a verb_noun pattern (upscale_image, upscale_video, get_task, check_pricing). The lone 'login' is a minor deviation but still clearly communicates its action, so the overall naming remains predictable.
Tool Count5/5Five tools is well-scoped for a niche server focused on Topaz upscaling through RunAPI. Each tool serves a necessary step in the workflow without unnecessary bloat.
Completeness4/5The core workflow of authenticating, submitting upscale tasks, and polling results is covered. Missing task cancellation or listing is a minor gap but does not break the primary use case.
Average 3.4/5 across 5 of 5 tools scored.
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
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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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 return value (task id, status, output URLs) but omits critical traits like asynchronous execution, polling/callback behavior, pricing implications, or any side effects. It does not disclose whether the task runs synchronously or how the video is processed.
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 a single, efficient sentence that front-loads the core purpose and return information. Every word contributes to understanding the tool's function without unnecessary filler. It earns its place by being concise and factual.
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?
This is a complex asynchronous tool with 7 parameters, no annotations, and no output schema, yet the description only covers basic creation and return. It omits usage context, distinction from upscale_image, and behavioral semantics like polling or webhooks. The description is inadequate for an agent to fully understand the tool's workflow and prerequisites.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 71%, with descriptions for source_video_url, model, callback_url, upscale_factor, and additionalProperties. The description adds no parameter-level details, and the undocumented parameters (wait, timeout_ms, poll_interval_ms) remain unexplained by both schema and description. Since the schema covers most parameters, the baseline is acceptable, but no extra value is provided.
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 action (create a Topaz task) and the domain (upscale video on RunAPI). It distinguishes from the sibling upscale_image by explicitly mentioning video. However, it doesn't explicitly say 'upscales a video file' as the primary purpose, relying on the parenthetical and tool name.
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?
No guidance is provided on when to use this tool versus alternatives like upscale_image. There are no exclusions, prerequisites, or context about whether to choose this over the image variant. The description only states what it does, not when it should be chosen.
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, the description carries the full burden of behavioral disclosure. It mentions returns (task id, status, output URLs) but does not clarify the asynchronous nature of task creation, whether the returned URLs are immediate or after completion, or any error conditions. The presence of wait, poll_interval_ms, and callback_url parameters indicates async behavior, but the description omits this crucial context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence of 16 words, extremely concise and front-loaded. Every word earns its place, but it omits essential behavioral details needed for correct invocation, which prevents a 5. Still, it avoids fluff and is efficiently structured.
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 the schema covering many parameters, the tool has 7 parameters including async-related fields, and no annotations or output schema are provided. The description fails to explain the task lifecycle (e.g., polling or webhooks), the fact that results are not immediate, or what happens on failure. This is insufficient for a complex asynchronous tool.
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 71%, which is moderate but under the 80% threshold for a baseline of 3. The description adds no parameter-level insight, and the schema leaves timeout_ms and poll_interval_ms undocumented. The description's 'Returns a task id, status, and output URLs' vaguely implies outputs but does not clarify how parameters affect behavior.
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 clearly states the tool's function: 'Create a Topaz task on RunAPI (upscale image).' The verb 'create' is specific, and the parenthetical disambiguates the resource as an image upscaling task. This effectively distinguishes the tool from its sibling upscale_video, which is for video upscaling.
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 usage for image upscaling via the phrase 'upscale image,' and the sibling context (upscale_video) suggests when to use this tool versus alternatives. However, there is no explicit 'when to use' or 'when not to use' guidance, nor any mention of alternatives or exclusions.
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 bears full responsibility for behavioral transparency. It only indicates a read operation ('look up pricing') but does not disclose any behavioral traits such as authentication requirements, rate limits, or what happens with invalid inputs.
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 a single concise sentence that is front-loaded and contains no fluff. Every word adds value.
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 two parameters with enums and no output schema, the description provides no information about return values, error handling, or pricing structure. The tool likely requires more context for effective usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with each parameter (model, action) already described in the input schema. The tool description adds no additional meaning beyond the schema, meeting the baseline for this dimension.
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 clearly states the tool looks up RunAPI pricing for the topaz model line. It uses a specific verb-resource combination ('look up pricing') and distinguishes from sibling tools (get_task, upscale_image, upscale_video) which are unrelated to pricing.
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 usage for pricing queries but does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or context for 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.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states what the tool does ('Fetch current status and latest result payload') but does not clarify side effects, error behavior, rate limits, or authentication requirements. The verb 'fetch' implies a read operation, but this is not explicitly stated and adds little behavioral context beyond the purpose.
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 a single sentence of 10 words, concise and front-loaded with the action ('Fetch current status and latest result payload'). Every word contributes to clarity with no redundancy.
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?
The tool is simple and the schema covers parameters well, but the description omits important context such as authentication prerequisites (given a login sibling tool) and potential error scenarios. There is no output schema, so the description's mention of 'latest result payload' provides some return information, but overall completeness is adequate with clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters (task_id, action) clearly described in the input schema. The tool description does not add additional parameter meaning, but the schema suffices, so baseline 3 is appropriate.
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 uses a specific verb ('Fetch') and resource ('current status and latest result payload for a topaz task'), clearly distinguishing it from sibling tools like upscale_image/upscale_video (which likely create tasks) and check_pricing. It unambiguously communicates 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 Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage (fetching task status after creation) but does not explicitly state when to use this tool versus alternatives, nor does it mention prerequisites like authentication. It provides no when-not-to-use guidance, so usage is only implied by the tool's purpose.
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.
Conciseness5/5Is 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.
Completeness4/5Given 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.
Parameters3/5Does 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.
Purpose5/5Does 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 Guidelines3/5Does 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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