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pythia-the-oracle

pythia-oracle-mcp

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list_tokens

Retrieve a list of all tracked tokens with status, reliability, uptime, and category information for cross-chain and DeFi assets.

Instructions

List all tokens tracked by Pythia with status and reliability info.

Returns token symbols, categories, data source count, 30-day uptime, and operational status. Covers cross-chain tokens (BTC, SOL, TAO, RENDER, ONDO, etc.) and DeFi tokens.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • Registration of the list_tokens tool via the @mcp.tool() decorator on the FastMCP instance.
    @mcp.tool()
  • Handler function for list_tokens: fetches live feed-status.json data, extracts tokens and stats, formats a table of token symbols, engine IDs, categories, status, 30-day uptime, and data source counts.
    @mcp.tool()
    async def list_tokens() -> str:
        """List all tokens tracked by Pythia with status and reliability info.
    
        Returns token symbols, categories, data source count, 30-day uptime,
        and operational status. Covers cross-chain tokens (BTC, SOL, TAO,
        RENDER, ONDO, etc.) and DeFi tokens.
        """
        data = await _fetch_data()
        tokens = data.get("tokens", [])
        stats = data.get("stats", {})
    
        lines = [f"Pythia Oracle — {stats.get('tokens', len(tokens))} tokens, "
                 f"{stats.get('total_indicators', '?')} indicator feeds\n"]
        lines.append(f"{'Symbol':<8} {'Engine ID':<28} {'Category':<16} {'Status':<6} "
                     f"{'Uptime':>7}  {'Src':>3}")
        lines.append("-" * 78)
        for t in sorted(tokens, key=lambda x: x.get("category", "")):
            status = t.get("status", "?")
            uptime = f"{t['uptime_30d']:.1f}%" if t.get("uptime_30d") is not None else "?"
            lines.append(
                f"{t['symbol']:<8} {t['engine_id']:<28} {t.get('category', '?'):<16} "
                f"{status:<6} {uptime:>7}  {t.get('sources', '?'):>3}"
            )
        lines.append(f"\nData delivered on-chain via Chainlink.")
        lines.append(f"Free trial: PythiaFaucet at {FAUCET_ADDRESS}")
        return "\n".join(lines)
Behavior3/5

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

No annotations are provided, so the description must convey behavioral traits. It reveals that the tool returns token symbols, categories, data source count, 30-day uptime, and operational status, implying a read-only operation. However, it does not disclose authentication requirements, rate limits, or whether the data is cached or real-time. The coverage is adequate but not thorough.

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—two sentences that front-load the main purpose and list key output fields. Every sentence adds value, with no wasted words. This is ideal for an AI agent to quickly understand the tool.

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 the tool's simplicity (no parameters, likely returns a list) and the existence of an output schema, the description adequately covers the return fields with natural language examples. It mentions categories of tokens (cross-chain, DeFi). It could note if the list is exhaustive or any caching behavior, but for a straightforward listing tool, it is mostly complete.

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

Parameters3/5

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

The tool has zero input parameters, and schema coverage is 100% (trivially). Per the rubric, baseline is 3 when coverage is high. The description adds no parameter information because there are none, but it does explain the output fields. This is acceptable but does not exceed the baseline.

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 it lists all tokens tracked by Pythia with status and reliability info, and gives specific examples (BTC, SOL, TAO, etc.). It uses a specific verb ('List') and identifies the resource ('tokens tracked by Pythia'). However, it does not explicitly differentiate from sibling tools like get_token_feeds or get_contracts, which slightly lowers the score.

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?

The description provides no guidance on when to use this tool versus alternative tools. For example, it does not mention that get_feed_value or get_token_feeds might be better for detailed feed information. Without such context, an AI agent may overuse or misuse the tool.

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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