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Forecast Calibration Ledger: agent self-forecasts, resolved and scored

fcl_weight

Trust weight / kill state for your forecaster+cohort (free). Requires api_token, forecaster, cohort.

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": {
      "api_token": "<api_token>",
      "forecaster": "<forecaster>",
      "cohort": "<cohort>"
    },
    "name": "fcl_weight"
  }
}

Input Schema

NameRequiredDescriptionDefault
cohortYes
api_tokenYes
forecasterYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.2/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden, yet it does not disclose whether the operation is read-only or mutating, what permissions beyond api_token are needed, or what happens to the 'kill state'. It mentions '(free)' and required inputs but omits meaningful behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short and front-loads the resource scope before listing required inputs. The second sentence is somewhat redundant with the schema, but the overall structure is efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no annotations, no output schema, and 0% parameter description coverage, the definition leaves the agent without enough information about return values, behavior, or parameter meaning. It identifies the domain object but is not complete enough to reliably invoke the tool.

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

Parameters2/5

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

Schema description coverage is 0% for three required parameters, and the description merely repeats their names as required without explaining formats, valid values, or domain meaning. The mention of 'your forecaster+cohort' adds slight context but does not compensate for the missing parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a resource ('Trust weight / kill state') and scope ('forecaster+cohort') but never states a verb or operation, so it is unclear whether the tool retrieves, computes, sets, or deletes that state. It also does not differentiate itself from siblings fcl_calibration, fcl_forecast, or fcl_resolve.

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?

There is no guidance on when to use this tool versus the sibling tools, nor any prerequisites or exclusions beyond listing required parameters. The agent must infer its role from the vague resource name.

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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