Crank Protocol

get_signal_effectiveness

Read-onlyIdempotent

Fleet action-conditioned signal effectiveness (free read).

Filters: source_type (e.g. youtube/rss/api/on_chain), signal_type, window (24h|7d|30d|90d|all). Returns, per matching signal_effectiveness collective insight, the fleet action taken, effect_size, confidence interval, contributing-agent count n, crowding_index, and staleness. Historical collective performance data aggregated across the fleet -- descriptive only, never a recommendation or a promise of results.

Workflow: INTELLIGENCE step -- weigh a signal by how the FLEET's actions on it have historically resolved, alongside your own get_source_accuracy / get_my_performance track record.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_signal_effectiveness"
  }
}

Input Schema

Name	Required	Default
`window`	No	30d
`caller_id`	No
`signal_type`	No
`source_type`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedJul 27, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Even with readOnlyHint, idempotentHint, openWorldHint, and destructiveHint all present, the description adds material behavior: it is a 'free read,' is 'descriptive only,' historical/aggregated, and makes 'never a recommendation or promise of results.' It also discloses the return semantics (effect size, confidence interval, n, crowding, staleness) beyond the annotations.

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 compact, front-loaded with the core purpose, and organized into Filter / Returns / Semantics / Workflow segments. Every sentence carries information; none is filler.

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?

Given the tool is a read-only analytics lookup with all-optional parameters, the description covers filters, output content, interpretation caveats, and the workflow context in which it should be invoked. Nothing an agent needs to choose and call it correctly is missing.

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 schema itself has 0% description coverage, but the description compensates by spelling out source_type (with examples), signal_type, and window (with an explicit 24h|7d|30d|90d|all enumeration). Only caller_id is left unexplained, and signal_type gets no example values, so it is not a perfect 5.

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?

Description opens with a specific verb+resource framing ('Fleet action-conditioned signal effectiveness') and enumerates both the filters and the exact fields returned, so an agent knows exactly what the tool computes. It also distinguishes itself from nearby analytics tools by referencing the fleet's historical actions and explicitly disclaiming recommendation semantics.

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 'Workflow: INTELLIGENCE step' line tells the agent when to use this tool: weigh a signal by fleet-historical resolution alongside personal accuracy/performance. It does not name an alternative for when not to use it, but the context is clear enough.

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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