CNAPS.ai MCP Server

report_issue

Report a tool failure or confusion to the cnaps.ai team. Use this when you encounter persistent errors, unclear documentation, or missing features. Reports are stored and analyzed to improve the platform.

Input Schema

TableJSON Schema

Name	Required	Description
`category`	No	Optional; defaults to 'other'. Failure category: 'validation' (input rejected), 'permission' (access denied), 'backend_error' (server-side failure), 'timeout' (operation too slow), 'unclear_docs' (confusing tool description), 'missing_feature' (tool can't do what's needed), 'other'.
`severity`	No	Optional; defaults to 'degraded'. Impact level: 'blocking' (task impossible), 'degraded' (workaround exists), 'minor' (inconvenience only).
`tool_name`	Yes	The name of the tool that failed (e.g., 'create_flow', 'upload_file').
`description`	No	Additional context: reproduction steps, file types involved, model names, flow structure, anything that helps understand the issue (max 4000 chars).
`retry_count`	No	Number of times the same tool was retried (0 = first attempt).
`steps_taken`	No	JSON array of tool names called before the failure, in order (e.g., '["list_models", "create_flow"]').
`user_intent`	No	What the user was trying to achieve (e.g., 'upscale an image to 4K resolution'). Optional, but it is the field that makes a report actionable.
`error_message`	Yes	The full error message received from the tool.
`suggested_fix`	No	Your suggestion for how this tool or the surrounding process could be improved (max 1000 chars).
`attempted_input`	No	The exact parameters sent to the failing tool call, as a JSON string (max 4000 chars).
`workaround_found`	No	Set to 1 if a workaround was found, 0 otherwise.
`workaround_description`	No	How the issue was worked around, if applicable (max 1000 chars).

TDQS

A4/5.0

Behavior3/5

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

Annotations do not provide safety hints (all false), so the description carries the burden. It discloses that 'Reports are stored and analyzed to improve the platform,' which informs the user about data persistence. It does not mention potential non-deterministic behavior or side effects beyond storage, but for a reporting tool this is adequate.

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?

Two sentences, front-loaded with the primary action and usage context. Every word earns its place without redundancy or filler. Structure is clean and scannable.

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 12-parameter tool with no output schema, the description covers the core purpose and usage triggers. The thorough schema handles parameter details. It doesn't explain return values or submission confirmation, but for a 'fire-and-forget' report tool, this is sufficient.

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%: all 12 parameters have descriptions including defaults and examples. The tool description adds no parameter-specific guidance, but the schema already does the heavy lifting, 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 action: 'Report a tool failure or confusion to the cnaps.ai team.' It names the specific resource (tool issues) and recipient, and it distinguishes from the sibling tool 'diagnose_error' by emphasizing reporting rather than diagnosing. 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 Guidelines4/5

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

Explicitly provides usage triggers: 'Use this when you encounter persistent errors, unclear documentation, or missing features.' This gives clear guidance on when to invoke the tool. However, it does not mention exclusions or alternatives (e.g., when not to use it), so it's not a full replacement for decision-making.

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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TDQS

A4/5.0

Disambiguation5/5

Each tool targets a distinct resource and action, with clear separation between flow lifecycle, execution, model exploration, community, and account tools. Even similar-sounding tools like create_flow, preview_flow, and suggest_flow have clearly different purposes (actually creating, dry-running, and recommending models). Descriptions prevent misselection.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern in snake_case (e.g., create_flow, list_flows, run_batch, cancel_flow). No mixed conventions or vague verbs like 'process' or 'handle'. The naming is uniform and predictable.

Tool Count3/5

At 33 tools, this is a large surface, but each tool addresses a distinct feature of the cnaps.ai platform, from flow CRUD and execution to community features and notifications. Still, it exceeds the typical well-scoped range and feels heavy, making it a borderline case between appropriate and too many.

Completeness3/5

The core flow lifecycle (create, read, update, delete, restore, duplicate) and execution (run, batch, cancel) are covered, but structural editing of flow graphs is missing—update_flow only changes parameters, not topology. Additionally, there is no run history, batch list/cancel, or community post update/delete, leaving notable gaps for a platform API.

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