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chat

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Route prompts to external AI models with conversation memory, file context, images, and multi-turn threads.

Instructions

Multi-model AI gateway. Routes prompts to external AI models (Gemini, OpenAI, Anthropic, DeepSeek, Moonshot, xAI, OpenRouter, custom endpoints) with conversation memory. Supports file context embedding, images, and multi-turn threads via continuation_id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYesCurrently in auto model selection mode. If no model is provided, you may use the `listmodels` tool to review options and select an appropriate match. The server validates model availability and returns errors for unknown models. Top models: gemini-3.8-flash (score 100, 1.0M ctx, thinking, code-gen); gemini-2.5-pro (score 100, 1.0M ctx, thinking, code-gen); gemini-3.1-pro-preview (score 100, 1.0M ctx, thinking, code-gen); gemini-2.5-flash (score 81, 1.0M ctx, thinking).
imagesNoImage paths (absolute) or base64 strings for optional visual context.
promptYesYour question or task for the external model. Prefer passing code and large content via absolute_file_paths rather than inlining it here.
temperatureNoOptional sampling temperature. If omitted, the model's own default is used (recommended; some reasoning models reject or degrade on a fabricated value). Range is provider-dependent (commonly 0–2); values are clamped per model.
thinking_modeNoOptional reasoning depth: minimal, low, medium, high, or max. Omit to use the provider default.
continuation_idNoUnique thread continuation ID for multi-turn conversations. Works across different tools. Reuse the last continuation_id you were given to preserve full conversation context, files, and history across turns. Threads are held in memory and expire after inactivity.
absolute_file_pathsNoFull, absolute file paths to relevant code in order to share with the external model. Accepts both files and directories (directories are expanded recursively). Content is read and embedded into the prompt context.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.8.0
    • changedInput schema / properties / model / description
      Previous value: -"Currently in auto model selection mode. If no model is provided, you may use the `listmodels` tool to review options and select an appropriate match. The server validates model availability and returns errors for unknown models. Top models: gemini-2.5-pro (score 100, 1.0M ctx, thinking, code-gen); gemini-3.1-pro-preview (score 100, 1.0M ctx, thinking, code-gen); gemini-2.5-flash (score 81, 1.0M ctx, thinking); gemini-2.0-flash (score 66, 1.0M ctx); gemini-2.0-flash-lite (score 56, 1.0M ctx)."New value: +"Currently in auto model selection mode. If no model is provided, you may use the `listmodels` tool to review options and select an appropriate match. The server validates model availability and returns errors for unknown models. Top models: gemini-3.8-flash (score 100, 1.0M ctx, thinking, code-gen); gemini-2.5-pro (score 100, 1.0M ctx, thinking, code-gen); gemini-3.1-pro-preview (score 100, 1.0M ctx, thinking, code-gen); gemini-2.5-flash (score 81, 1.0M ctx, thinking)."
    • changedInput schema / properties / thinking_mode / description
      Previous value: -"Reasoning depth: minimal, low, medium, high, or max."New value: +"Optional reasoning depth: minimal, low, medium, high, or max. Omit to use the provider default."
  2. First observedv0.5.0

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, so the safety profile is covered; the description adds real behavioral context beyond that, namely that it is a multi-provider gateway with conversation memory, file-context embedding, image support, and multi-turn threads. It omits any mention of cost, rate limits, or per-provider failure behavior, so it falls short of a 5.

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 tight sentences, front-loaded with the core identity ('Multi-model AI gateway') followed by routing scope and the capabilities that matter for invocation. No sentence is wasted or redundant with the schema.

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?

For a 7-parameter, multi-provider routing tool with no output schema, the description covers the essential mental model: what it routes to, that memory/threads exist, and that files and images can be attached. It leaves unaddressed what happens on provider failure or how responses are shaped, but the readOnly annotation and rich schema compensate.

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 every parameter is already documented in detail, which sets the baseline at 3. The description adds only high-level framing ('file context embedding, images, and multi-turn threads via continuation_id') without new syntax or format detail beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource ('Routes prompts to external AI models') and enumerates the providers, so an agent immediately knows this is a completion/routing tool rather than a listing tool. It does not explicitly name the siblings (listmodels, dump_threads) to contrast itself against them, which keeps it at a 4 rather than a 5.

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?

Usage is implied by 'Multi-model AI gateway' and the routing sentence, but there is no explicit statement of when to use chat versus listmodels (which the schema, not the description, suggests for model discovery) or when a thread should be continued versus started fresh. The agent must infer the workflow.

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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