CNAPS.ai MCP Server

list_models

Read-onlyIdempotent

List available AI models on cnaps.ai with optional filters. Returns model names, types, input/output port types, and accessibility. Use filters to narrow results instead of browsing all models.

Input Schema

TableJSON Schema

Name	Required	Description
`tag`	No	Filter by tag (e.g., 'deblur', 'inpainting', 'super-resolution'). Case-insensitive exact match.
`category`	No	Filter by main category (e.g., 'AI Models', 'Tools', 'External Models'). Leave empty to list all.
`input_type`	No	Filter by input port type: image, text, video, sound, transcript, segments, file:font, or a list:<type> (e.g. list:image). "text" and "string" both match text ports.
`output_type`	No	Filter by output port type: image, text, video, sound, transcript, segments, or a list:<type> (e.g. list:video). "text" and "string" both match text ports.
`sub_category`	No	Filter by sub-category (e.g., 'Image Upscaling', 'Object Detection'). Case-insensitive partial match against the model's sub_category field; a value that matches nothing returns the list of valid sub-categories.

TDQS

A4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds concrete output details ('Returns model names, types, input/output port types, and accessibility') that go beyond the annotations, and it reveals the behavior of listing with filters. There is no contradiction, and the added context justifies a 4 given the 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 short sentences that front-load the primary action ('List available AI models on cnaps.ai'), then provide return details and a usage tip. Every sentence earns its place; no redundant or filler content.

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?

As a simple list tool with no output schema, the description covers the purpose, filter behavior, and return fields. It could additionally mention pagination or whether the list is exhaustive, but the description is reasonably complete for the tool's complexity given rich annotations and full schema coverage.

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?

The input schema has 100% description coverage, with each of the 5 parameters (tag, category, input_type, output_type, sub_category) fully explained. The description only refers to 'optional filters' and 'filters' generically, without adding parameter-specific meaning beyond the schema. Baseline 3 is appropriate.

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 opens with 'List available AI models on cnaps.ai' – a specific verb + resource + scope. It also states what is returned ('model names, types, input/output port types, and accessibility') and mentions optional filters. This clearly differentiates from siblings like get_model (singular) and list_flows (different resource).

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

Usage Guidelines3/5

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

The description advises 'Use filters to narrow results instead of browsing all models,' which gives usage context for the tool's filter parameters. However, it does not explicitly contrast with alternatives such as find_compatible_models or get_model, nor does it state when not to use this tool. Thus, usage guidance is present but implied rather than explicit.

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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