chimeralang-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@chimeralang-mcpCheck for hallucinations in: 'The Earth is flat.'"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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 44 tools Claude can call during any conversation. No Anthropic permission needed, works today with Claude Desktop and Claude Code.
Install
pip install chimeralang-mcp
# or
uvx chimeralang-mcpDevelopment quick checks
pytest -q
python -m pip checkpytest is configured via pyproject.toml to include project root on PYTHONPATH.
For token counting internals, these optional environment variables are supported:
CHIMERA_TOKEN_CACHE_MAX_ENTRIES(default2048) — bounds in-memory token count cache size.CHIMERA_TOKEN_FALLBACK_LOG_INTERVAL_S(default60) — throttles repeated fallback warning logs.
Related MCP server: Orchestrator MCP Server
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 a pip-installed version:
{
"mcpServers": {
"chimeralang": {
"command": "python",
"args": ["-m", "chimeralang_mcp"]
}
}
}Restart Claude Desktop. 44 ChimeraLang tools are now available.
Tools
The core language/runtime tools are executable and deterministic. Several higher-level reasoning, safety, and cognition helpers are lightweight local heuristics intended for planning, triage, and guardrails rather than authoritative verification.
Most stateful tools accept an optional namespace and persist data to ~/.chimeralang_mcp (or CHIMERA_MCP_DATA_DIR) so agents can carry memory, world state, traces, and cost history across sessions. The package also ships an embedded material pack plus offline CLI commands for sync, build, status, and licenses.
Core Language
Tool | What it does |
| Execute a |
| Static type-check a |
| Execute plus generate a Merkle-chain integrity proof |
Confidence and Safety
Tool | What it does |
| Assert a value meets the |
| Wrap a value as exploratory and explicitly allow uncertainty |
| Collapse multiple candidates via consensus |
| Full constraint middleware on any tool result |
| Hallucination and MCP security detection across range, dictionary, semantic, cross-reference, temporal, and confidence-threshold strategies |
| Validate content against a safety policy and flag prompt-injection, tool-poisoning, token-theft, and oversharing patterns |
| Evaluate an action against ethical principles |
Reasoning and Cognition
Tool | What it does |
| Decompose a high-level goal into ordered sub-goals |
| Build and query a causal graph |
| Heuristic multi-perspective deliberation with Jaccard similarity and divergence scoring |
| Multi-agent consensus voting with contradiction detection |
| Reflect on reasoning quality and compute calibration metrics |
| Maintain a persistent self-model of agent capabilities |
| Embodied reasoning simulation |
| Social reasoning and perspective modelling |
| Run fitness-ranked candidate selection across generations |
| Record adaptation events and retrieve meta-learning stats |
| Map concepts across source and target domains |
Knowledge and Memory
Tool | What it does |
| Persistent namespace-scoped world model (key to value with confidence) |
| Persistent namespace-scoped knowledge base (add, search, list) |
| Persistent namespace-scoped memory store (store, recall by importance) |
Provenance and Verification
Tool | What it does |
| Extract atomic claims from text or an envelope with claim typing plus hedge and abstention tags |
| Verify claims against evidence with FEVER-style supported, contradicted, or insufficient-evidence verdicts |
| Merge multiple result envelopes into one aggregated provenance object |
| Apply reusable constraint profiles like |
| Inspect persisted result envelopes and trace history, including material-pack metadata |
| Inspect bundled material packs, licenses, build status, and pinned source manifests |
Token Budget, Cost, and Workflow
The token-efficiency stack now defaults to a deterministic, quantum-inspired compressor: it scores text spans by salience amplitude, boosts shared entities through entanglement, suppresses redundant spans through interference, and then "measures" the best units under a token budget. chimera_optimize, chimera_compress, chimera_fracture, and chimera_csm all accept algorithm="classic|quantum" and optional focus text so compression can stay task-aware instead of blindly deleting tokens.
Tool | What it does |
| Query-aware text compression with legacy rewrite fallback |
| Aggressive salience extraction for large text or code blobs |
| Full pipeline: optimize docs plus compress messages plus budget gate |
| Rank messages by importance for lossy compression decisions, with optional task focus |
| Report current token usage against a budget |
| Deterministic cost estimate for any supported model |
| Record before and after compression events to the tracker |
| Namespace-level cost intelligence summary |
| Context Session Manager: optimize prompt, compress history, and propose a token budget |
| Lock and track an approved per-turn output budget |
| Recommend a task-relevant subset of the tool inventory |
| Execute multiple Chimera tools in a single MCP call |
| LLM-free extractive summarizer for long documents |
Meta and Audit
Tool | What it does |
| Session-level call log, confidence summary, and persistent audit stats |
What problem does this solve?
Claude's tool-use loop has no built-in mechanism for:
Confidence gating - only proceed if confidence is above a threshold
Typed output contracts - a result must satisfy a constraint before going downstream
Genuine consensus detection - determine whether multi-path agreement is substantive
Hallucination signals - structured detection rather than pure intuition
Trust propagation - confidence and provenance should survive chained tool calls
Evidence-backed verification - explicit claims checked against supplied evidence
Persistent reasoning state - memory, world state, and traces carried across sessions
Cost intelligence - token tracking and compression throughout long sessions
ChimeraLang provides a practical constraint layer between Claude and its tools. The language runtime is the strongest guarantee surface. The higher-level cognition and safety helpers are best treated as lightweight first-pass checks unless you pair them with stronger external evidence.
Example prompts
Gate a value before a critical action:
"Before you submit that form, use chimera_confident to verify you're at least 0.95 confident the data is correct."
Consensus across reasoning paths:
"Generate 3 different answers, then use chimera_quantum_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."
Evidence-backed fact checking:
"Extract claims with chimera_claims, verify them against these sources with chimera_verify, then apply chimera_policy using strict_factual."
Trace and provenance inspection:
"Merge the envelopes from those two tool calls with chimera_provenance_merge, then inspect the latest trace with chimera_trace."
Material-pack inspection and build flow:
"List the bundled packs with chimera_materials, then run chimeralang-mcp build locally if you want generated JSON runtime and eval pack artifacts on disk."
ChimeraLang Quick Reference
Variable Declaration
Both val and let are supported:
val answer = Confident("Paris", 0.97)
let hypothesis = Explore("maybe dark matter is...", 0.4)Probabilistic Types
emit Confident("verified fact", 0.97)
emit Explore("hypothesis", 0.60)Assertions
assert Confident(0.78) > Confident(0.45)Gate Declaration
gate verify(claim: Text) -> Converge<Text>
branches: 3
collapse: weighted_vote
threshold: 0.80
return claim
endLogical Operators
Both keyword and symbolic forms are supported:
if a > 0.5 and b > 0.5
emit Confident("both pass", 0.9)
end
if a > 0.5 && b > 0.5
emit Confident("both pass", 0.9)
endHallucination Detection
detect temperature_check
strategy: "range"
on: temperature
valid_range: [-50.0, 60.0]
action: "flag"
endIf / Else
if confidence > 0.80
emit Confident("high confidence result", 0.9)
else
emit Explore("low confidence - needs review", 0.5)
endFor Loop
for item in items
emit Explore(item, 0.6)
endMatch
match verdict
| "pass" => emit Confident("approved", 0.95)
| "fail" => emit Explore("rejected", 0.70)
| _ => emit Explore("unknown", 0.50)
endChangelog
0.6.0
Add a deterministic quantum-inspired token compression engine with salience amplitude, entanglement boosts, redundancy interference, and budgeted measurement
Make
chimera_optimize,chimera_compress,chimera_fracture, andchimera_csmquery-aware through optionalfocusinput plusclassic|quantumalgorithm selectionUpgrade
chimera_fractureto return the actual optimized documents and compressed message artifacts instead of only aggregate statsFix
TokenBudgetManagersingleton behavior so token-count caches are reused across tool calls
0.5.0
Add a material-pack subsystem with pinned source manifests, pack builders, a loader, and offline
sync/build/status/licensesCLI commandsAdd
chimera_materialsand wire pack metadata into claims, verification, policy, detect, safety, trace, and audit flowsUpgrade claim extraction with pack-driven claim typing plus hedge and abstention tagging
Upgrade verification to FEVER-style verdicts with evidence matches, taint-aware attack flags, and pack-version reporting
Add MCP security policies, OWASP MCP Top 10 mappings, CI material builds, and an HTTP transport path for conformance workflows
0.4.0
Add a unified result envelope model with confidence, provenance, transform history, claims, constraints, warnings, and metadata
Persist namespace-scoped knowledge, memory, world model, self model, meta-learning history, traces, and cost tracking to disk
Add
chimera_claims,chimera_verify,chimera_provenance_merge,chimera_policy, andchimera_traceExpose evidence-backed verification and reusable policy application directly through MCP tools
Add regression coverage for namespace persistence, claim extraction, verification, provenance merge, policy enforcement, and trace inspection
0.3.2
Preserve structured JSON values through
chimera_confident,chimera_explore, andchimera_constraininstead of stringifying themFix multimodal message token estimation fallback so
chimera_cost_estimate,chimera_budget, andchimera_csmdo not undercount text-array contentMake
chimera_fracturestop dropping history once the budget is satisfied instead of over-pruning to the minimum message floorFix
chimera_mode fullto report the real live tool inventory and align README documentation with the current 38-tool surface
0.2.7
Fix
UnboundLocalErrorinchimera_cost_trackcaused bylogvariable shadowing the module-level logger in thechimera_audithandlerAdd
letas a keyword alias forvalin variable declarationsAdd
&&and||as lexer tokens (aliases forandandor)Expand tool count to 33 in the README
0.2.5
Initial AGI component suite: causal reasoning, deliberation engine, quantum vote, safety layer, ethical reasoner
Knowledge base, world model, session memory
Cost tracking, budget management, dashboard
Self-model and metacognition tools
Links
ChimeraLang core: github.com/fernandogarzaaa/ChimeraLang
OpenChimera: github.com/fernandogarzaaa/OpenChimera_v1
License
MIT (c) Fernando Garza
Materials CLI
The wheel includes a compact curated core material pack. Larger corpora stay external and are only touched through manual CLI commands:
chimeralang-mcp status
chimeralang-mcp licenses
chimeralang-mcp sync
chimeralang-mcp buildstatusreports bundled pack counts and whether generated artifacts already exist inCHIMERA_MCP_DATA_DIR/materialslicensesprints the machine-readable source and bundle-policy reportsyncfetches current upstream metadata snapshots for the pinned GitHub and Hugging Face sourcesbuildwrites normalized JSON manifests plus runtime and eval pack files to disk
The runtime MCP tools never fetch from the network. They only consume the bundled core pack and any already-generated local artifacts.
Maintenance
Resources
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Looking for Admin?
If you are the server author, to access and configure the admin panel.
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