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Reeling

by io.github.yomariano
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reeling_evaluate_content_jev

Run a Type-Safe JEV (Judgment & Evaluation Vector) diagnostic on published post metrics. Evaluates 0-3s hook velocity, cognitive concept fit, visual/code density, engagement triggers, and drop-off causes with Zod schema validation, returning actionable content prescriptions for future generations.

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": "reeling_evaluate_content_jev"
  }
}

Input Schema

NameRequiredDescriptionDefault
api_keyNoOptional Reeling API key.
force_freshNoWhether to re-evaluate directly against live post metrics rather than using cached evaluation.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.1/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It does disclose that results come from Zod-validated evaluation and that caching exists (force_fresh toggles live vs cached), plus what is returned (prescriptions). However it omits auth/permission requirements, rate limits, and whether the operation is read-only, leaving meaningful gaps.

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

Conciseness3/5

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

Single sentence, front-loaded with the action, which is good. But it is dense and jargon-heavy, packing the acronym expansion and five evaluation dimensions into one clause, which costs readability.

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

Completeness3/5

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

No output schema exists, and the description usefully previews the return (actionable prescriptions). However, it never explains how the target post is identified — there is no post-ID parameter, only api_key and force_fresh — leaving a significant ambiguity for a tool that claims to evaluate a specific post's metrics.

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 description coverage is 100%, so both api_key and force_fresh are already documented in the schema. The description adds no syntax or semantics beyond that. Baseline 3 applies when the schema does the heavy lifting.

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?

States a specific verb (run a JEV diagnostic) on a specific resource (published post metrics) and enumerates the dimensions evaluated, so an agent can distinguish it from generic siblings like get_analytics or sync_metrics. The 'JEV' acronym is jargon but is expanded inline, keeping the purpose legible.

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?

No when-to-use, when-not-to-use, or prerequisite guidance is given. It never clarifies whether this should follow sync_metrics or complement get_analytics, nor what state a post must be in. Usage must be inferred from 'published post metrics'.

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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