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analyze_encoding_detector

Read-only

Encoding Detector — Detect the character encoding of a text or HTML file. Use when accented letters, apostrophes or quotation marks arrive as garbled symbols: it identifies which character set the file was saved in (UTF-8, Windows-1252, ISO-8859-1, UTF-16 and so on). It REPORTS the encoding only — it does not convert or rewrite the file. [category: analyze]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fileYesInput file (TXT, HTML, CSV, XML)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already mark readOnlyHint=true, and the description reinforces this by explicitly stating the tool only reports and does not convert or rewrite the file. It also adds useful behavioral context about what kinds of files and encoding issues it addresses. The description aligns with and slightly extends the annotation-provided safety profile.

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 compact and front-loaded, starting with a clear one-line summary followed by a practical use case and an explicit scope limitation. Every sentence earns its place, and the category tag is unobtrusively appended.

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

Completeness5/5

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

For a simple, single-parameter, read-only detector tool, the description covers the trigger scenario, supported encodings, input file types, and the boundary of what it does not do. No output schema exists, but the description's 'reports the encoding only' sufficiently conveys the outcome. Nothing critical is 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 'file' is already fully documented in the schema with type, format, and accepted extensions (TXT, HTML, CSV, XML), so schema coverage is 100%. The description adds no new parameter-specific semantics beyond listing example encodings, which is marginal value. Baseline 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 identifies the specific action 'Detect the character encoding' and the resource 'text or HTML file.' It also distinguishes itself from conversion tools by stating 'It REPORTS the encoding only — it does not convert or rewrite the file,' making its purpose unmistakable among the large sibling set.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides an explicit trigger condition: 'Use when accented letters, apostrophes or quotation marks arrive as garbled symbols.' It also clarifies what it does not do, so an agent knows not to use it for conversion or rewriting. However, it does not name specific alternative tools or state when not to use it beyond the conversion exclusion.

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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TDQS

B3.2/5.0
Disambiguation2/5

Multiple tool pairs are near-identical: octopus_mkdir/octopus_make_folder and octopus_move/octopus_move_file are literal duplicates, analyze_hash/generate_hash both compute hashes, convert_word_to_pdf overlaps convert_document, and photo_compress/photo_compress_to_size plus pdf_thumbnails/pdf_to_images have fuzzy boundaries. The descriptions are detailed and cross-reference each other helpfully, but at 144 tools an agent will regularly misselect.

Naming Consistency3/5

The dominant {category}_{verb}_{object} snake_case pattern (pdf_*, photo_*, convert_*, analyze_*, media_*) is largely consistent and predictable. However, outliers like chatwithyourpdf and describe_image break the category-prefix convention, and the octopus namespace mixes bare verbs (read, write, mkdir) with verb_noun forms (make_folder, move_file, search_meta) inconsistently.

Tool Count2/5

144 tools is an extreme count for any MCP server. The broad scope (PDF, photo, video, audio, conversion, analysis, generation, file storage, web, e-sign) justifies some volume, but the count is inflated by batch and inspect variants (pdf_to_excel + batch + inspect), duplicate tools, and overlapping converters. An agent faces an overwhelming selection surface.

Completeness4/5

Per-domain coverage is remarkably deep: PDF spans merge/split/compress/protect/unlock/metadata/OCR/watermark and bidirectional conversion; photo covers editing, format conversion, face handling, OCR, and collage; file storage has full CRUD plus search. Minor gaps exist (no audio transcription, no video metadata editing, no deletion of PDF pages is actually covered via pdf_delete_pages) but the surface has no dead ends for its declared domains.

Resources