Skip to main content
Glama
Sign In
Everyday DevTools MCP

Everyday DevTools MCP

by Deependra12349
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

text_stats

Count characters, words, lines, and sentences in any text to assess length and structure before editing, publishing, or validating content.

Instructions

Count characters, words, lines and sentences in text.

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": {
      "text": "<text>"
    },
    "name": "text_stats"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It does not disclose whether character counts include whitespace, how sentences are delimited, how empty or non-ASCII text is handled, or what the response shape looks like.

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?

One front-loaded sentence with zero filler; the countable units are listed immediately after the verb.

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?

This is a low-complexity, single-parameter compute tool, so the bar is modest, but with no output schema the description should state what the returned object contains (e.g., named count fields) and clarify counting rules. That gap leaves the agent guessing at return values.

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 0%, but the single parameter is named 'text' and the description implies it is the string to be measured. It adds no detail on accepted format, encoding, or size limits, so it does little beyond what the parameter name already conveys.

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 (count) and enumerates the exact metrics produced (characters, words, lines, sentences) against a text input. It is unambiguous and cannot be confused with any sibling utility such as case_convert or slugify, though it never explicitly states that distinction.

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?

There is no when-to-use guidance, no prerequisites, and no mention of alternatives. The agent must infer usage from the one-line purpose statement alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools