Skip to main content
Glama
Octodamus

Octodamus Market Intelligence

Official
by Octodamus
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

ask_oracle

Ask for a probability estimate on any yes/no market question. Returns probability, key factors, and oracle reasoning for crypto, macro, and Polymarket questions.

Instructions

Ask the Octodamus oracle for a probability estimate on any yes/no market question. Returns an estimated probability, key factors for each side, and oracle reasoning. Works for crypto, macro, and Polymarket-style questions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionYesA yes/no market question, e.g. Will BTC hit 100k by end of 2026?

Output Schema

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

Implementation Reference

  • server.py:190-196 (registration)
    Registration of the 'ask_oracle' tool via the @mcp.tool decorator with its description.
    @mcp.tool(
    description=(
        "Ask the Octodamus oracle for a probability estimate on any yes/no market question. "
        "Returns an estimated probability, key factors for each side, "
        "and oracle reasoning. Works for crypto, macro, and Polymarket-style questions."
    )
)
  • server.py:197-204 (handler)
    Handler function 'ask_oracle' that takes a yes/no market question and returns a TextResult directing the user to the Octodamus API for live probability estimates.
    def ask_oracle(
    question: Annotated[str, Field(description="A yes/no market question, e.g. Will BTC hit 100k by end of 2026?")],
) -> TextResult:
    return TextResult(result=(
        f"Oracle analysis for: {question}\n\n"
        f"Submit to live oracle: https://api.octodamus.com\n"
        f"For real-time probability estimates, use the Octodamus API with your question as a parameter."
    ))
  • server.py:19-20 (schema)
    Schema definition for TextResult, the return type used by ask_oracle.
    class TextResult(BaseModel):
    result: str = Field(description="Oracle response text with signal data, analysis, or confirmation")

Tool Definition Quality

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description must disclose behavior. It states the tool returns an estimated probability, key factors, and reasoning, which is sufficient for a read-only query tool. It does not disclose rate limits or auth, but those are not critical for this simple 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?

The description is two sentences long, each sentence adding essential information: the action and return values in the first, the domains in the second. No wasted words.

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?

With an output schema present (not shown), the description complements it by listing key return fields. For a single-parameter tool, the description fully informs about usage and return values. No gaps remain.

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 one parameter 'question' with 100% description coverage. The description adds value by specifying the expected format ('a yes/no market question') and providing an example, which aids correct usage beyond the schema.

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?

The description clearly states it asks an oracle for a probability estimate on yes/no market questions, lists return fields (estimated probability, key factors, reasoning), and specifies applicable domains (crypto, macro, Polymarket). It is distinct from sibling tools like get_market_brief or get_market_sentiment.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context: it works for yes/no market questions and gives example domains. However, it does not explicitly state when to avoid this tool or compare it to siblings, though the sibling names imply different scopes.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Octodamus/octodamus-core'

If you have feedback or need assistance with the MCP directory API, please join our Discord server