chimeralang-mcp 0.2.1

ChimeraLang MCP Server — probabilistic types, consensus gates, and hallucination detection as Claude tools

chimeralang-mcp

Give Claude typed confidence, hallucination detection, and constraint enforcement — as native MCP tools.

ChimeraLang is a programming language built for AI cognition. This MCP server exposes its runtime as 12 tools Claude can call during any conversation — no Anthropic permission needed, works today with Claude Desktop and Claude Code.

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Install

pip install chimeralang-mcp
# or
uvx chimeralang-mcp

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Claude Desktop Setup

Add to your config file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "chimeralang": {
      "command": "uvx",
      "args": ["chimeralang-mcp"]
    }
  }
}

Or with pip-installed version:

{
  "mcpServers": {
    "chimeralang": {
      "command": "python",
      "args": ["-m", "chimeralang_mcp"]
    }
  }
}

Restart Claude Desktop — 12 ChimeraLang tools are now available.

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Tools

Tool	What it does
chimera_run	Execute a .chimera program string
chimera_confident	Assert a value meets >= 0.95 confidence threshold
chimera_explore	Wrap a value as exploratory (hallucination explicitly permitted)
chimera_gate	Collapse multiple candidates via consensus (majority / weighted_vote / highest_confidence)
chimera_detect	Hallucination detection — 5 strategies: range, dictionary, semantic, cross_reference, temporal
chimera_constrain	Full constraint middleware on any tool result
chimera_typecheck	Static type-check a .chimera program
chimera_prove	Execute + Merkle-chain integrity proof
chimera_audit	Session-level call log and confidence summary
chimera_compress	Proportional message-history compression to a token budget
chimera_optimize	Aggressive text extraction (structural + entity + frequency)
chimera_fracture	Full pipeline — optimize docs + compress messages + quality gate

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What problem does this solve?

Claude's tool-use loop has no built-in mechanism for:

• Confidence gating — only proceed if confidence >= threshold
• Typed output contracts — this result must satisfy constraint X before going downstream
• Genuine consensus detection — is multi-path agreement real, or trivially identical?
• Hallucination signals — structured detection, not just "does it sound right"
• Trust propagation — confidence degrades through chained tool calls; nothing tracks it

ChimeraLang fills exactly these gaps as a constraint layer sitting between Claude and its tools.

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Example prompts

Gate a value before a critical action:

"Before you submit that form, use chimera_confident to verify you're >= 0.95 confident the data is correct."

Consensus across reasoning paths:

"Generate 3 different answers, then use chimera_gate with weighted_vote to collapse to the most reliable one."

Hallucination scan on output:

"After you get that search result, run chimera_detect with semantic strategy to check for absolute-certainty markers."

Full constraint pipeline:

"Use chimera_constrain on that tool result with min_confidence 0.85 and detect_strategy semantic."

Integrity proof for audit:

"Run this reasoning with chimera_prove so we have a tamper-evident trace."

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ChimeraLang Quick Reference

// Confident<> — enforces >= 0.95 confidence
val answer: Confident<Text> = confident("Paris", 0.97)

// Explore<> — hallucination explicitly permitted
val hypothesis: Explore<Text> = explore("maybe dark matter is...", 0.4)

// Gate — multi-branch consensus
gate verify(claim: Text) -> Converge<Text>
  branches: 3
  collapse: weighted_vote
  threshold: 0.80
  return claim
end

// Detect — hallucination scan
detect temperature_check
  strategy: "range"
  on: temperature
  valid_range: [-50.0, 60.0]
  action: "flag"
end

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Links

• ChimeraLang core: github.com/fernandogarzaaa/ChimeraLang
• OpenChimera: github.com/fernandogarzaaa/OpenChimera_v1

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License

MIT © Fernando Garza