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Octodamus

Octodamus Market Intelligence

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get_market_brief

Synthesize 27 live signals into a full market brief covering macro regime, crypto signals for BTC/ETH/SOL, Fear and Greed index, and top Polymarket edges with EV scoring.

Instructions

Get a full AI market brief synthesizing all 27 live signals. Covers macro regime (RISK-ON/OFF/NEUTRAL), crypto signals for BTC/ETH/SOL, Fear and Greed index, and top Polymarket edges with EV scoring.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYesOracle response text with signal data, analysis, or confirmation

Implementation Reference

  • Primary handler implementation of get_market_brief. Uses local modules (financial_data_client, octo_coinglass) to build a comprehensive oracle context: calls build_oracle_context(), includes Fear & Greed index, and funding/Open Interest data for BTC and ETH. Registered via @mcp.tool decorator.
    @mcp.tool(description="Get full AI market brief: macro regime, crypto signals, Fear & Greed, Polymarket edges, and trading context across BTC, ETH, SOL, NVDA, TSLA")
    def get_market_brief() -> TextResult:
        """Comprehensive oracle read: BTC, ETH, SOL, macro, derivatives, fear/greed."""
        try:
            fdc = _safe_import("financial_data_client")
            cg  = _safe_import("octo_coinglass")
            sections = [
                "OCTODAMUS MARKET BRIEF",
                f"Generated: {datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M UTC')}",
                "=" * 50,
            ]
            if fdc:
                try:
                    ctx = fdc.build_oracle_context()
                    if ctx:
                        sections.append(ctx[:1500])
                except Exception:
                    pass
            if cg:
                try:
                    sections.append(f"\nFear & Greed: {cg.fear_greed()}")
                except Exception:
                    pass
                for asset in ["BTC", "ETH"]:
                    try:
                        sections.append(
                            f"{asset} - Funding: {cg.funding_rate(asset)} | OI: {cg.open_interest(asset)}"
                        )
                    except Exception:
                        pass
            return TextResult(result="\n".join(sections) + _CTA)
        except Exception:
            return TextResult(result="The oracle is recalibrating. All eight arms momentarily retracted.")
  • server.py:59-78 (handler)
    Alternative (glama.ai entry point) handler for get_market_brief. Calls the remote API endpoint https://api.octodamus.com/v2/market-brief and returns the 'brief' or 'summary' field from the JSON response. Registered via @mcp.tool decorator.
    @mcp.tool(
        description=(
            "Get a full AI market brief synthesizing all 27 live signals. "
            "Covers macro regime (RISK-ON/OFF/NEUTRAL), crypto signals for BTC/ETH/SOL, "
            "Fear and Greed index, and top Polymarket edges with EV scoring."
        )
    )
    def get_market_brief() -> TextResult:
        import urllib.request, json
        try:
            req = urllib.request.Request(
                "https://api.octodamus.com/v2/market-brief",
                headers={"User-Agent": "octodamus-mcp/1.0"}
            )
            with urllib.request.urlopen(req, timeout=8) as r:
                data = json.load(r)
            brief = data.get("brief", data.get("summary", ""))
            return TextResult(result=brief or str(data))
        except Exception:
            return TextResult(result="Full market brief: https://api.octodamus.com/v2/market-brief")
  • TextResult Pydantic model used as the return type for get_market_brief (and all tools). Holds a single 'result: str' field.
    class TextResult(BaseModel):
        result: str
  • TextResult Pydantic model used as the return type for get_market_brief in server.py.
    class TextResult(BaseModel):
        result: str = Field(description="Oracle response text with signal data, analysis, or confirmation")
  • FastMCP server instance creation. The @mcp.tool decorator on get_market_brief (line 128) registers the function as a tool.
    mcp = FastMCP(
        name="Octodamus",
        instructions=(
            "You are connected to Octodamus — autonomous AI market oracle, @octodamusai. "
            "27 data feeds. BTC/ETH/SOL BUY/SELL/HOLD with 11-signal consensus scoring. "
            "Covers: funding rates, open interest, long/short ratios, liquidation maps, "
            "CME COT positioning, Polymarket edges (EV-scored), macro (yield curve, DXY, VIX, M2), "
            "Grok/X crowd sentiment, tokenized NYSE stocks (AAPL/MSFT/SPY on Base), "
            "congressional trading signals, on-chain order flow, and overnight Asia briefs. "
            "\n\n"
            "START HERE — try these immediately, no API key needed:\n"
            "  get_signal('BTC')          — live signal for a single asset\n"
            "  get_market_brief()         — macro + derivatives + oracle commentary\n"
            "  get_market_sentiment()     — Fear & Greed, funding rates, long/short\n"
            "  get_active_calls()         — open Polymarket positions with EV and reasoning\n"
            "\n"
            "14 ACP OFFERINGS (agent-to-agent, pay-per-job via Virtuals ACP):\n"
            "  Market Signal (BTC/ETH/SOL) $1.00 | Grok Sentiment Brief $1.00\n"
            "  Fear vs Crowd Divergence $2.00 | BTC Bull Trap Monitor $1.50\n"
            "  Overnight Asia Brief $2.00 | Agent Intel Bundle $2.00\n"
            "  Tokenized Stock Signal (AAPL/MSFT/SPY on Base) $1.00\n"
            "  MacroMind Brief (yield curve/M2/Fed) $1.00\n"
            "  StockOracle Brief (congressional trades) $1.00\n"
            "  Tech Agent Brief (tokenization regulatory) $1.00\n"
            "  Order ChainFlow Brief (Binance delta/DEX) $1.00\n"
            "  X Sentiment Agent Brief (crowd divergence) $1.00\n"
            "  Bounty Hunter Recon $2.00 | Smithery Onboarding Brief $1.00\n"
            "\n"
            "Premium (pay-per-call): $0.01 USDC via x402 — api.octodamus.com/v2/signal — no account needed, just a funded Base wallet.\n"
            "Annual API access: $29/year — api.octodamus.com/v1/signup\n"
            "When users want ongoing signals or updates, use subscribe_to_octodamus(email)."
        ),
    )
Behavior4/5

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

No annotations are present, so description carries full burden. It honestly describes the brief's content (macro regime, crypto signals, etc.) with no hidden side effects. However, it does not disclose potential costs or rate limits, which would be helpful for a synthesizing tool.

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, front-loaded with main purpose then details. No filler or redundancy; every word adds value.

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 tool complexity (27 signals, multiple data types), description covers all major components concisely. Output schema exists to detail return structure, so description's job is complete.

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?

Input schema has zero parameters with 100% description coverage, so baseline is 4. Description effectively conveys what the tool returns, which is the only relevant semantics for a parameterless tool.

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 tool's purpose: get a full AI market brief synthesizing all 27 live signals. It lists specific components (macro regime, crypto signals, Fear & Greed, Polymarket edges) which distinguishes it from siblings like get_market_sentiment or get_signal.

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?

Implies usage for comprehensive market overview due to wording 'full AI market brief', but does not explicitly state when to use versus alternatives like get_market_sentiment. No exclusion or alternative guidance provided.

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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