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Medium Blog MCP Server

Medium Blog MCP Server

by Navneet1710
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Run Quality Analysis

run_quality_analysis

Run automatic quality checks on a final draft, evaluating readability scores, citation tracking, image placement, structure, and SEO fundamentals.

Instructions

Run automatic quality checks on final draft

Checks:

  1. Readability scores (Flesch-Kincaid)

  2. Citation tracking

  3. Image placement validation

  4. Structure analysis

  5. SEO basics

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": {
      "session_id": "<session_id>"
    },
    "name": "run_quality_analysis"
  }
}

Input Schema

NameRequiredDescriptionDefault
session_idYesActive session ID

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior2/5

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

The description lists the checks but does not disclose the output format, whether the tool is read-only, or if it saves results. Since annotations are absent, the description carries full responsibility for behavioral transparency, and it falls short.

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 highly concise, using a single sentence and a bullet-point list. Every word adds value, and the structure is clear and scannable.

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?

The description lacks information about the return value, success/failure criteria, or how the results are used in the broader workflow. Given the tool's role as a quality gate, this context is important but missing.

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?

The single parameter session_id is described in the schema with 'Active session ID', which provides adequate meaning. The tool description itself adds no further detail, but since schema coverage is 100%, a baseline of 3 is appropriate.

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?

The description clearly states the action ('Run automatic quality checks'), the object ('final draft'), and enumerates the specific checks performed. It is unambiguous and distinguishes the tool's purpose from others.

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 does not specify when this tool should be invoked relative to other tools, nor does it mention any prerequisites or conditions. It simply states what it does without guiding the agent on choosing it over alternatives.

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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