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Carbon Cashmere MCP Server

Carbon Cashmere MCP Server

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by CarbonCashmere
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TypeScript
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get_v1_mantis_forecast_archive_challenge

Retrieve archived AI forecasts with realized outcomes for retrospective benchmarking. Get historical 5-bucket distributions, cross-sectional scores, and directional features for supported challenges.

Instructions

Matured AI forecast archive: historical model outputs with realized outcomes (5-bucket distributions, cross-sectional scores, directional features) as a verifiable benchmark dataset. Strictly retrospective (24h maturity gate) — not current signals. Informational research data — not investment advice. Price: $0.10. Category: bittensor-mantis.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "challenge": "<challenge>"
    },
    "name": "get_v1_mantis_forecast_archive_challenge"
  }
}

Input Schema

NameRequiredDescriptionDefault
challengeYesPath parameter challenge (e.g. BTC, ETH, SOL, or other supported identifier)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.2

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are provided, so the description carries full burden. It discloses that the data is informational, not investment advice, and mentions a price. It also specifies the retrospective nature. However, it does not cover error behavior, rate limits, or authentication requirements, which are typical gaps.

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 a single paragraph of three sentences, covering the tool's purpose, content, constraints, disclaimers, and price. Every sentence adds value with no redundancy or wasted words.

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?

For a simple tool with one parameter and no output schema, the description explains what the output contains (distributions, scores, features) and categorizes it as a benchmark dataset. It lacks a full list of supported 'challenge' values, but examples are given. Overall, it is nearly complete for the tool's complexity.

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?

Schema coverage is 100% with one parameter 'challenge'. The description gives examples (BTC, ETH, SOL) but adds no semantics beyond the schema. Baseline of 3 is appropriate as the schema already defines the parameter adequately.

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 provides historical AI forecast outputs with realized outcomes, specifying content (5-bucket distributions, scores, features) and that it is a benchmark dataset. However, it does not explicitly differentiate from the sibling tool 'get_v1_mantis_forecast_scorecard', which may have a similar purpose.

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?

The description mentions the tool is strictly retrospective (24h maturity gate) and not current signals, implying when to use it. It does not explicitly state when not to use it or mention alternatives, but the context is clear enough for basic usage decisions.

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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