CCAPI

Call a text model

chat_completion

Read-only

Send a conversation to any text model available through CCAPI (Claude, GPT, Gemini, DeepSeek, GLM, MiniMax, Kimi, Qwen…) and get the reply. Useful for consulting a second model for a different perspective, running a cheap model over bulk work, or reaching a model the current client does not have configured. Responses are non-streaming.

Input Schema

TableJSON Schema

Name	Required	Description
`stop`	No	Up to 4 sequences that stop generation.
`model`	Yes	Exact model name, e.g. claude-sonnet-5 or deepseek-v4-pro. Call list_models with category 'text' if unsure.
`top_p`	No	Nucleus sampling probability mass, 0-1. Omit for the model default.
`messages`	Yes	Conversation so far, oldest first. Must contain at least one message.
`max_tokens`	No	Maximum tokens to generate. Omit for the model default.
`temperature`	No	Sampling temperature, typically 0-2. Omit for the model default.

Output Schema

TableJSON Schema

Name	Required	Description
`model`	Yes	Model that actually served the request.
`usage`	Yes	Token accounting for this call.
`content`	Yes	The model's reply text.
`finish_reason`	No	Why generation stopped, e.g. stop or length.

TDQS

A4.3/5.0

Behavior4/5

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

The annotations already declare readOnlyHint=true, so the description's job is lighter. It adds the non-streaming behavior and the fact that it works with any text model, providing context beyond the annotations without contradiction.

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 total: the first states the core action and scope, the second lists use cases and a behavioral note. No filler, front-loaded with the verb, and every sentence serves a purpose.

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?

Given the readOnlyHint annotation, full parameter schema, and an output schema, the description provides sufficient context. The non-streaming note and model family list add useful detail beyond what structured data provides.

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% coverage with detailed descriptions for all 6 parameters. The tool description does not add parameter-specific guidance beyond referring to 'conversation' and 'model', so the baseline of 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 action ('Send a conversation... and get the reply') and the resource ('any text model available through CCAPI'), with a specific list of model families. It distinguishes itself from siblings like list_models and get_balance by focusing on the core chat completion functionality.

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?

It explicitly lists three concrete use cases (second opinion, bulk work, reaching unconfigured models) and notes that responses are non-streaming. However, it does not explicitly state when not to use the tool or how it differs from alternatives, though the use cases largely imply this.

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

A4.4/5.0

Disambiguation5/5

Each tool maps to a distinct media type or lifecycle step: text, image, music, video, task status, balance, and model discovery. The adjacent tools like edit_image vs generate_image and generate_lyrics vs generate_music are explicitly cross-referenced in their descriptions, so an agent can reliably pick the right one.

Naming Consistency4/5

All but one tool follows a clear imperative verb_noun pattern: edit_image, generate_music, get_task, list_models, etc. chat_completion is the lone structural outlier, using a noun phrase instead of a verb-led action name, which is a minor but noticeable inconsistency.

Tool Count5/5

11 tools is well-scoped for a multimodal API: one generation tool per media type, plus task polling, model listing, and balance checking. There is no redundancy or bloat, and each tool earns its place in the set.

Completeness4/5

The surface covers the full asynchronous workflow—submit generation, poll status, retrieve results—plus model discovery and quota awareness, so there are no dead ends. It lacks conveniences like cancelling a queued task or listing historical tasks, but those are minor gaps rather than blockers for the stated purpose.

Resources

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