CNAPS.ai MCP Server

find_compatible_models

Read-onlyIdempotent

Find models that can connect to a given model in a flow. Use 'downstream' to find models whose inputs match this model's outputs (what can come AFTER). Use 'upstream' to find models whose outputs match this model's inputs (what can come BEFORE). Returns compatible models sorted by number of matching ports. Use this BEFORE creating a flow to ensure models can be wired together.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`model_id`	Yes	The model to find compatible partners for.
`direction`	No	'downstream' (default): models that can come AFTER this model. 'upstream': models that can come BEFORE.

TDQS

A4.3/5.0

Behavior4/5

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

The description adds useful behavioral context beyond annotations, such as output sorted by number of matching ports and the meaning of downstream/upstream. Annotations already declare read-only and idempotent, so the bar is lower; this context is sufficient but not overly rich.

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?

Three sentences, all informative and front-loaded with purpose. No wasted words, each sentence contributes to usage or behavior understanding.

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?

For a simple read-only tool with 100% schema coverage and full annotations, the description provides complete guidance: when to use, how direction works, and what output to expect. No output schema exists, so return behavior is appropriately described.

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 coverage is 100% for both parameters, and the description repeats the direction definitions already present in the schema without adding new syntax or edge cases. 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 clearly states the tool finds models that can connect in a flow, using a specific verb and resource. It distinguishes itself from siblings like list_models by focusing on compatibility and flow wiring.

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 advises using the tool BEFORE creating a flow and explains downstream/upstream meaning. However, it does not explicitly mention alternative tools or when not to use, so it misses the 'alternatives' criterion for a 5.

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

All CNAPS.ai MCP Server tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources

Need Help?