Qwen 2 MCP Server

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden, and it does not discharge it. It never says whether pricing is per-token, per-image, or per-request, what currency or unit applies, whether the values are live or cached, or whether authentication is required — all material for a pricing lookup.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with no padding, and the verb-resource pairing arrives immediately. It is arguably too terse given the gaps elsewhere, but there is no wasted text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no annotations and no output schema, the description must cover the response shape, and it does not say what the pricing result contains. For a tool whose entire value is the returned pricing unit, omitting that leaves the definition inadequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so both the 'model' slug and the 'action' enum are already documented in the schema, making 3 the baseline. The description adds essentially nothing about parameter behavior and arguably narrows the model scope to qwen-2 in a way the open slug parameter does not imply.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('look up') and resource ('RunAPI pricing') with a stated scope ('qwen-2 model line'). It is not confused with any sibling (login, edit_image, text_to_image, get_task), but the scope wording is slightly at odds with the schema, which accepts an arbitrary model slug.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No statement of when to call this versus alternatives, and no prerequisites. An agent can infer it should be checked before invoking edit_image or text_to_image, but the description never says so, and the siblings give no pricing counterpart to route against.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description bears the full burden. It does add useful context by disclosing the asynchronous task model and the return shape (task id, status, output URLs), but it omits auth requirements, cost/pricing implications, rate limits, and what happens if the task times out.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tight sentences with the core action front-loaded and the return values stated second. No filler, though the parenthetical '(edit image)' is slightly awkwardly placed.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an 11-parameter task-creation tool with no output schema, the description covers the basic return shape but leaves the async lifecycle (polling vs callback_url, timeout behavior) and operational constraints unexplained. Adequate but with clear gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 82%, above the high-coverage threshold, so the schema already documents parameters like wait, model, aspect_ratio, and output_format. The description adds no parameter meaning beyond that baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific action and resource: creating a Qwen 2 RunAPI task that edits an image. It is clear what the tool does, but it does not distinguish itself from sibling text_to_image or explain why one would pick image editing over generation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use guidance, no prerequisites, and no mention of alternatives such as text_to_image or get_task for retrieving results. The only hint of workflow is the 'wait' parameter defaulting to polling, which lives in the schema, not the description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden. It usefully frames the operation as a read returning 'current status' and 'latest result payload', hinting at polling semantics, but says nothing about whether the operation is safe/idempotent, what status values exist, or what the payload looks like while a task is still pending.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with zero filler; the core action and payload are stated immediately.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema exists, so the description must carry some of the return-value burden, and it partially does by naming status and result payload. It still omits lifecycle detail (pending vs. complete states, empty payload behavior) needed to use this status tool correctly in a polling loop.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with both parameters well described, so the baseline is 3. The description adds essentially no parameter meaning beyond the schema - it never clarifies that action must match the endpoint used at creation time, which is the one non-obvious semantic an agent might get wrong.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (Fetch) and resource (current status and latest result payload for a qwen-2 task), which is more informative than a bare 'get task'. It implicitly contrasts with the sibling creation tools edit_image/text_to_image, but never names them, so an agent must infer the create-then-poll relationship.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'for a qwen-2 task' implies this is the polling companion to edit_image/text_to_image, so the use context is inferable. However, there is no explicit statement of when to call it, how often to poll, or what to do instead once the task reaches a terminal state.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

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.

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.

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.

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.

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.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It does disclose the async task model by stating that a task id, status, and output URLs are returned, which is real behavioral value. However, it omits auth requirements, cost, and the fact that the default wait=true blocks until completion, leaving meaningful gaps for a creation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tight sentences with no filler, and the core action is front-loaded before the return contract. Every clause earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 10-parameter tool with no output schema, the description usefully names the return values (task id, status, output URLs), which stands in for the missing output schema. It falls short on polling/wait semantics and authentication, but is otherwise sufficient to call the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 80%, so the schema already documents nearly all 10 parameters. The description adds nothing about seed, aspect_ratio, model, wait, timeout_ms, or callback_url, so it neither compensates for the remaining gap nor enriches the documented ones. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Create a Qwen 2 task on RunAPI') and clarifies the modality with '(text to image)', which separates it from the sibling edit_image. It stops short of explicitly contrasting the two, so the distinction is inferable rather than stated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use guidance and no alternatives are named. The mention of a returned task id hints that get_task is the follow-up for polling, but the agent must infer this; nothing tells it when text_to_image is preferable to edit_image.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.