Skip to main content
Glama
Sign In
tool-mcp-server

tool-mcp-server

by maple-awa
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

tool_text_stats

Analyze any text to get character, word, line, sentence, paragraph, and byte counts. Provides instant text metrics for content assessment.

Instructions

[text] Text Stats

Parameters:

  • text (string): Text

Outputs:

  • characters (number): Characters

  • words (number): Words

  • lines (number): Lines

  • sentences (number): Sentences

  • paragraphs (number): Paragraphs

  • bytes (number): Bytes

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": "tool_text_stats"
  }
}

Input Schema

NameRequiredDescriptionDefault
textNoText
bytesNoBytes
linesNoLines
wordsNoWords
sentencesNoSentences
charactersNoCharacters
paragraphsNoParagraphs

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries the full burden for behavioral disclosure. It clarifies the input/output relationship (text in, stats out) but does not explain counting definitions, edge cases (e.g., empty text), encoding for bytes, or locale sensitivity. This is useful but incomplete context beyond the schema.

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 well-organized with 'Parameters' and 'Outputs' sections. It earns its place, though the opening line '[text] Text Stats' is slightly redundant and the repeated single-word descriptions (e.g., 'Characters') add minimal value.

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?

The tool is relatively simple, and the description covers the main input and outputs. However, it lacks details on how counts are defined, how bytes are measured, and what edge cases behave (e.g., empty or whitespace-only input). The confusing schema is partially mitigated, but not fully resolved.

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 input schema lists all seven metrics as properties, which would be confusing to an agent. The description explicitly states that only 'text' is a parameter and the rest are outputs, adding crucial disambiguation. However, it does not describe text format constraints or size limits.

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 clearly identifies the tool as computing text statistics, listing six specific output metrics (characters, words, lines, sentences, paragraphs, bytes). While it lacks an explicit verb like 'computes' and does not compare against sibling tools, the output list makes the purpose unambiguous.

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 provides no guidance on when to use this tool versus alternatives. It does not state use cases, limitations, or exclusions, making the agent infer usage solely from the name and output list. No alternative tools are mentioned.

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