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Seonix

by io.github.pahaa97
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Score an existing article

score_article
Read-onlyIdempotent

Re-run the SEO + readability scoring on an article already in Seonix (no changes saved) and list its problems.

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": {
      "article_id": "<article_id>"
    },
    "name": "score_article"
  }
}

Input Schema

NameRequiredDescriptionDefault
article_idYesArticle id from list_articles.
project_idNoProject id or slug.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so safety is covered; the description still adds value by stating '(no changes saved)' and noting the call returns a list of problems. It does not discuss cost/latency of re-scoring, but for a read-only scoring call this is solid added context.

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?

A single sentence front-loads the core action and folds the side-effect guarantee ('no changes saved') into a parenthetical. Nothing is wasted and no clarification is needed.

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?

With rich annotations, fully documented params, and no output schema, the description covers both what triggers the call and roughly what comes back ('list its problems'). It could say more about the shape of the scoring output, but it is complete enough for an agent to call correctly.

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 article_id and project_id both documented in the schema. The description adds no parameter-level detail (e.g., that project_id is optional or how it scopes the lookup), so the schema is doing all the work — the baseline 3 is appropriate.

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 names a specific verb+resource (re-run SEO/readability scoring on an article already in Seonix) plus the output (list its problems), so the action is unambiguous. It implies separation from analyze_seo by scoping to an article 'already in Seonix', but never names the sibling, so it stops short of full differentiation.

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 phrase 'already in Seonix' signals the tool operates on stored articles, which implicitly tells an agent when to reach for it rather than a content-analysis tool. However, there is no explicit when/when-not guidance and no mention of analyze_seo or get_article as the alternative path.

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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