CNAPS.ai MCP Server

search_community

Read-onlyIdempotent

Search community-shared AI flows on cnaps.ai. Find flows by keyword, input/output modality, or tags. Use this to discover existing flows before creating new ones. Returns post IDs that can be forked with fork_community_flow.

Input Schema

TableJSON Schema

Name	Required	Description
`page`	No	Page number, 1-5 (default: 1). Each page is 20 results; past page 5, narrow the query or filters instead.
`sort`	No	Sort order: 'popular' (default), 'recent', 'most_liked', 'most_forked'.
`tags`	No	Filter by tags (AND logic). JSON array or comma-separated, e.g. '["upscale","4k"]' or 'upscale,4k'.
`query`	No	Search keyword (e.g., 'super resolution', 'background removal', 'deblur'). Searches titles, content, and tags.
`input_type`	No	Filter by input modality: image, text, video, or sound. A comma-separated list matches flows whose inputs are exactly that set — `image,video` means both, not either.
`output_type`	No	Filter by output modality: image, text, video, or sound. A comma-separated list matches flows whose outputs are exactly that set — `image,video` means both, not either.

TDQS

A4.3/5.0

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, covering the safety profile. The description adds valuable behavioral context by stating it returns post IDs and that these can be forked, which is not directly in the annotations or schema. It does not describe pagination or rate limits, but these are in the schema.

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 concise sentences: first states the tool's function, second details search criteria, third gives usage guidance and return value. It is front-loaded and contains no fluff.

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?

The description is complete for a read-only search tool with strong annotations and full schema coverage. It explains the purpose, when to use it, and what it returns (post IDs), which is important since there is no output schema. No critical context is missing.

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%, with each parameter having a clear description. The description merely summarizes the search dimensions (keyword, modality, tags) without adding extra meaning beyond what the schema provides. Baseline of 3 is appropriate since the schema does the heavy lifting.

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's purpose: 'Search community-shared AI flows on cnaps.ai.' It specifies the search dimensions (keyword, modality, tags) and distinguishes itself from siblings like list_flows by focusing on community content. The mention of returning post IDs for forking with fork_community_flow adds clarity.

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?

The description explicitly provides a when-to-use scenario: 'Use this to discover existing flows before creating new ones.' It also mentions how to use the results (fork with fork_community_flow). However, it does not explicitly state when_not to use it or name alternative search tools, though the community focus differentiates it.

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.

Resources

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