Sahadeva

Measured hit rates of chart-named past windows

get_rule_hit_rates

Read-onlyIdempotent

Aggregate results from people who chose to share their answers to Sahadeva's chart-named past windows: confirmed, denied and unsure counts and hit rate overall, per topic and per timing-fusion factor, each beside the ordinary control months (lift). No individual answers. Rates appear only once a cell has 20 decided answers.

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_rule_hit_rates"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
`schemaVersion`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 1, 2026

TDQS

A3.6/5.0

Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive, closed-world behavior. The description adds meaningful behavioral context beyond annotations: no individual answers are exposed, and rates are surfaced only when a cell has 20 decided answers, which is important privacy and reliability information.

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 compact and front-loads the core operation and aggregation dimensions. It is somewhat dense, but every clause adds useful scope, privacy, or reliability information.

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 zero-parameter schema, existing output schema, and rich annotations, the description supplies the key missing context: what is aggregated, the dimensions reported, privacy constraints, and the minimum-cell threshold. Additional when-to-use guidance would improve completeness, but the core is covered.

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 tool takes zero parameters, so there are no parameter semantics to document. The baseline for zero-parameter tools is 4, and nothing in the description creates confusion about inputs.

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 states a specific resource and operation: aggregating user-shared answers to chart-named past windows into confirmed/denied/unsure counts and hit rates. It clearly differentiates this measured-outcome data from rule-finding or validation siblings, though it does not name a specific alternative tool.

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 explains what the tool returns but gives no explicit when-to-use guidance, prerequisites, or comparison to alternatives such as get_validation_report or run_longitudinal_validation. Usage is only implied by the nature of the data.

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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