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lechatai-mcp

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Le Chat AI MCP Server

Le Chat AI: Independent guide to Mistral's Le Chat

MCP Badge Node Stdio Transport Zero Config License: MIT MCP

A Model Context Protocol server that exposes the canonical Le Chat AI knowledge surface — models, prompts, and chat workflows, pricing, FAQ, official links — to MCP-compatible AI clients such as Claude Desktop, Cursor, Windsurf, and Continue. Read-only, no API keys, no quota, ~50 ms cold start.

Official website: https://lechatai.online

💬 About Le Chat AI

LeChat AI (lechatai.online) is an independent guide and resource hub for Mistral's Le Chat conversational AI platform. The site covers the platform's capabilities in depth — from its speed benchmarks and pricing tiers to its European data compliance stance and open-weight model philosophy. Visitors can read structured comparisons, tutorials, and feature breakdowns, and follow direct links to test Le Chat themselves. The content is oriented toward helping individuals and organizations evaluate whether Le Chat fits their needs, particularly those comparing it against ChatGPT, Claude, Gemini, or other mainstream AI assistants.

Related MCP server: meigenai-mcp

Key Features

  • Flash Answers: Near-instantaneous response generation powered by Cerebras WSE-3 hardware, reaching approximately 1,000 tokens per second — one of the fastest inference speeds available in a consumer-facing AI product.

  • Deep Research: An automated research mode that synthesizes information from multiple sources and returns structured, cited reports — suited for market analysis, academic literature reviews, and competitive comparisons.

  • Document analysis: Native support for large PDF imports (50+ pages) with summarization and in-line citation, allowing users to interrogate long documents without manual reading.

  • MCP connectors: Over 20 integrations with third-party platforms including GitHub, Snowflake, and Atlassian, enabling Le Chat to pull live data from internal systems during conversations.

  • Memories: Persistent cross-session context so the assistant retains relevant preferences and prior conversation details over time.

  • Canvas workspace: A collaborative, document-style workspace for drafting and iterating on longer-form outputs alongside the assistant.

  • No Telemetry mode: Available on the Pro tier, this option disables usage data collection — a meaningful option for privacy-conscious teams.

Use Cases

  • Enterprise research workflows: Teams use Deep Research to produce sourced briefings on competitors, technologies, or regulatory landscapes without manual aggregation.

  • Document review: Legal, compliance, and finance professionals import large PDFs and ask targeted questions, receiving cited answers rather than raw text.

  • Developer tooling: Engineering teams connect Le Chat to GitHub or internal databases via MCP connectors to query codebases, pull request histories, or data pipelines in natural language.

  • Privacy-first AI adoption: European organizations subject to GDPR constraints evaluate Le Chat as a compliant alternative to US-hosted AI services, with EU data residency and telemetry controls.

  • AI platform comparison: Individuals and procurement teams use the site's structured breakdowns to compare Le Chat's pricing (approximately $14.99/month for Pro) and capabilities against competing platforms before committing.

Who Is It For

The primary audience is professionals and organizations already using or evaluating AI assistants who want a clear, detailed picture of what Le Chat offers. This includes enterprise IT and procurement teams assessing compliance requirements, developers looking for open-weight model deployability, and European companies that need GDPR-aligned tools with EU data hosting. It also serves individual power users who care about response speed, document handling depth, or third-party integrations — and who want a straightforward comparison rather than marketing material. The site assumes some familiarity with conversational AI but does not require technical expertise.

Tools

list_models

Return the canonical list of chat models exposed on the site, with capability notes. (Le Chat AI)

Input: no parameters. Returns: text/markdown.

get_pricing

Return the canonical pricing entry point for Le Chat AI.

Input: no parameters. Returns: text/markdown.

get_official_links

Return the canonical list of official links for Le Chat AI (website, support, docs when available).

Input: no parameters. Returns: text/markdown.

Resources

  • site://lechatai/models — Supported chat models and capability notes.

  • site://lechatai/pricing — Canonical pricing entry point.

  • site://lechatai/faq — Short FAQ generated from public site metadata.

  • site://lechatai/links — Canonical URLs to share with users.

Prompts

tell_me_about_lechatai

Summarize what the site is, who it's for, and how it works. — Le Chat AI

start_chat_session_lechatai

Open a chat-evaluation session against the site's models, with sensible defaults. — Le Chat AI

Installation

Install via Smithery

npx -y @smithery/cli install lechatai-mcp --client claude

(Replace claude with cursor, windsurf, or continue for those clients.)

Install from source

git clone https://github.com/rocnubie/lechatai-mcp.git
cd lechatai-mcp
pnpm install

Then add to your MCP client config (claude_desktop_config.json for Claude Desktop, mcp.json for Cursor / Windsurf / Continue):

{
  "mcpServers": {
    "lechatai-mcp": {
      "command": "node",
      "args": [
        "/absolute/path/to/lechatai-mcp/src/index.mjs"
      ]
    }
  }
}

Debug with MCP Inspector

npx @modelcontextprotocol/inspector node src/index.mjs

Official Links

Development

pnpm install
pnpm start                 # run the server over stdio

License

MIT

Available Tools

3 tools
get_pricingB

Return the canonical pricing entry point for Le Chat AI.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

B3.2/5.0
Behavior2/5

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

No annotations provided, so description must disclose behavior. It only states 'Return the canonical pricing entry point' without explaining what that entails (e.g., URL, text, or data), leaving ambiguity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence is concise but uses vague phrasing ('pricing entry point') that reduces clarity. Not front-loaded with key information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema or return value description, the tool is incomplete. Agent cannot know what to expect from the response, which is critical for tool selection.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters exist, and schema description coverage is 100%. Baseline for 0 parameters is 4; description adds no param info but does not detract.

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?

Description uses specific verb 'Return' and explicitly names the resource 'canonical pricing entry point for Le Chat AI', clearly distinguishing from sibling tools list_models and get_official_links.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus siblings or any exclusions. The description is minimal and does not provide context for invocation.

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

list_modelsA

Return the canonical list of chat models exposed on the site, with capability notes. (Le Chat AI)

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A3.9/5.0
Behavior3/5

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

No annotations provided, so description bears full burden. It states 'canonical list' and 'capability notes' but does not explicitly declare read-only nature or safety implications. For a list operation, this is acceptable but not fully transparent.

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?

Single clear sentence with no extraneous words. Front-loaded with key action and resource.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, so description should explain return format. Mentions 'list of chat models with capability notes' but lacks specifics on structure (e.g., model IDs, names). For a simple tool, minimally adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has zero parameters. Description adds no parameter info, but with 0 params, baseline is 4 per instructions.

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?

Description clearly states the verb 'Return' and the resource 'canonical list of chat models'. It distinguishes from sibling tools (get_pricing, get_official_links) by focusing on model listing.

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?

No explicit when-to-use or when-not-to-use guidance. Context signals show no parameters and existence of sibling tools, but description does not provide usage context.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 3 tool updatesv0.1.0
    • First observedget_official_links
    • First observedget_pricing
    • First observedlist_models
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TDQS

A4/5.0
Disambiguation5/5

Each tool targets a distinct aspect: list_models for models, get_pricing for pricing, get_official_links for links. No overlap or ambiguity.

Naming Consistency5/5

All tool names follow the consistent verb_noun snake_case pattern (list_models, get_pricing, get_official_links), providing predictability.

Tool Count5/5

The server has exactly 3 tools, which is well-scoped for its purpose of providing informational resources about Le Chat AI. Each tool serves a clear purpose without excess.

Completeness4/5

The tools cover the core informational needs (models, pricing, official links). A minor gap is the lack of a tool for status or version info, but the set is reasonable for the domain.

Resources

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