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pythia-the-oracle

pythia-oracle-mcp

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get_pricing

Retrieve live pricing tiers and free trial information from the Pythia data feed to evaluate subscription costs.

Instructions

Get Pythia pricing tiers and free trial info. Prices are live from the data feed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The main handler/implementation of the 'get_pricing' tool. It fetches live pricing data from feed-status.json, extracts tier fees (discovery, analysis, speed, complete), and returns a formatted string with tier pricing and free trial info.
    @mcp.tool()
    async def get_pricing() -> str:
        """Get Pythia pricing tiers and free trial info. Prices are live from the data feed."""
        data = await _fetch_data()
        fees = _get_tier_fees(data)
    
        d = fees.get("discovery", "?")
        a = fees.get("analysis", "?")
        s = fees.get("speed", "?")
        c = fees.get("complete", "?")
    
        return f"""Pythia Oracle — Pricing Tiers
    
      DISCOVERY — {d} LINK
        Any single indicator (EMA, RSI, Bollinger, Volatility)
        Returns: uint256
        Best for: one-off queries, specific signals
    
      ANALYSIS — {a} LINK
        All 1-hour, 1-day, and 1-week indicators bundled
        Returns: uint256[]
        Best for: protocols needing multi-timeframe view
    
      SPEED — {s} LINK
        All 5-minute indicators bundled
        Returns: uint256[]
        Best for: real-time trading, active rebalancing
    
      COMPLETE — {c} LINK
        Every indicator for a token (all timeframes)
        Returns: uint256[]
        Best for: comprehensive analysis
    
      FREE TRIAL — PythiaFaucet
        Address: {FAUCET_ADDRESS}
        No LINK needed. 5 requests/day/address. Real data."""
  • Helper function _get_tier_fees() used by get_pricing to extract tier fees from the live feed-status.json data.
    def _get_tier_fees(data: dict) -> dict[str, float]:
        """Extract tier fees from live feed-status.json data.
    
        Raises RuntimeError if tiers section is missing.
        """
        tiers = data.get("tiers")
        if not tiers:
            raise RuntimeError(
                "feed-status.json is missing the tiers section. "
                "This is a structural error in the live data — check the data engine."
            )
        return {t["id"]: t["fee"] for t in tiers if "id" in t and "fee" in t}
  • Registration of the 'get_pricing' tool via the @mcp.tool() decorator on the get_pricing function.
    @mcp.tool()
    async def get_pricing() -> str:
  • Schema/type definition: the tool takes no arguments and returns a string.
    async def get_pricing() -> str:
Behavior3/5

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

With no annotations, the description bears full responsibility for behavioral disclosure. It adds that 'Prices are live from the data feed', indicating real-time data, but does not mention side effects, authentication needs, or other behavioral traits. For a simple read tool, this is adequate but not detailed.

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?

The description is extremely concise with two sentences, no unnecessary words, and front-loads the purpose. Every sentence adds value without redundancy.

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?

Given no parameters and the presence of an output schema, the description covers the essential purpose. For a simple retrieval tool, it is largely complete, though it could add minor details like whether authentication is required.

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?

The input schema has zero parameters, so the baseline is 4. The description adds meaning by specifying what information is retrieved (pricing tiers, free trial info), which is sufficient given the absence of parameters.

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 clearly states the tool retrieves 'Pythia pricing tiers and free trial info' with a specific verb 'Get'. It identifies the resource but does not explicitly differentiate from sibling tools, though the resource is specific enough to distinguish.

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 is provided on when to use this tool versus alternatives. The description only explains what the tool does without context for appropriate usage scenarios or exclusions.

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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