CNAPS.ai MCP Server

diagnose_error

Read-onlyIdempotent

Analyze a failed flow run and suggest fixes. Provide the flow_id of a failed flow. Returns root cause analysis, the specific failing node, a concrete fix suggestion (e.g. insert a Resize node, change a parameter), and confidence level. Use this whenever get_flow(view="status") shows a failed node.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`flow_id`	Yes	The unique identifier of the failed flow to diagnose.
`error_context`	No	Additional context from the user about the failure. Example: "it was working before with smaller images"

TDQS

A4.3/5.0

Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds return-value details but no additional behavioral context such as side effects, permissions, or failure modes, so it stays at baseline given annotation coverage.

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 three sentences, front-loaded with the core action, then lists return values, then states the usage trigger. Every sentence earns its place with no filler.

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

Completeness5/5

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

With no output schema, the description explicitly enumerates the return fields: root cause analysis, failing node, fix suggestion, and confidence level. Combined with the usage trigger and param guidance, the description is complete for an agent to invoke the tool correctly.

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 parameters are already well-documented. The description adds 'failed flow' context for flow_id, but this overlaps with the schema's 'failed flow to diagnose.' No significant new parameter semantics are provided.

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 ('Analyze') with a clear resource ('failed flow run') and outcome ('suggest fixes'). It distinguishes itself from siblings by targeting failed flows, explicitly referencing get_flow(view="status") for detection.

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

Usage Guidelines5/5

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

Explicit usage trigger is provided: 'Use this whenever get_flow(view="status") shows a failed node.' This clearly tells the agent when to select this tool over alternatives like optimize_flow or preview_flow.

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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