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JSON2TOON MCP Server

by muhammedehab35
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Python
Remote

JSON2TOON v2.0 🚀

License: MIT Python 3.10+ MCP Compatible Code Coverage

Advanced Token-Optimized Object Notation - The most powerful JSON compression system for AI context management.

JSON2TOON is a next-generation MCP server that revolutionizes JSON compression with AI-powered pattern detection, achieving 75-85% token reduction while maintaining perfect data integrity.

✨ Key Features

🎯 4 Compression Levels

  • MINIMAL (30-40% savings): Lightning-fast key abbreviations

  • STANDARD (40-60% savings): Balanced performance + compression

  • AGGRESSIVE (60-75% savings): Advanced pattern optimization

  • EXTREME (75-85% savings): Maximum compression with zlib

🤖 AI-Powered Pattern Detection

  • 17+ Pattern Types: API responses, databases, time series, graphs, trees, and more

  • Smart Strategy Selection: Automatic optimization based on data structure

  • Confidence Scoring: Each pattern comes with accuracy metrics

  • Compression Potential: Estimates savings before conversion

🔧 12 Advanced MCP Tools

  1. convert_to_toon - Multi-level JSON compression

  2. convert_to_json - Lossless decompression

  3. analyze_patterns - Deep pattern analysis with AI

  4. get_optimal_strategy - AI-recommended compression plan

  5. calculate_metrics - Detailed compression statistics

  6. batch_convert - High-performance batch processing

  7. smart_optimize - Auto-detect and apply best compression

  8. compare_levels - Side-by-side level comparison

  9. validate_toon - Format validation + round-trip testing

  10. suggest_abbreviations - Custom abbreviation generation

  11. estimate_savings - Pre-conversion savings estimation

  12. get_server_stats - Real-time performance metrics

💡 Advanced Capabilities

  • 150+ Key Abbreviations (vs 68 in TOON v1.0)

  • String Dictionary: De-duplication for repeated values

  • Partial Schema Compression: Works with inconsistent data

  • Value Pattern Compression: Optimizes timestamps, UUIDs, URLs, emails

  • Reference System: Eliminates duplicate structures

  • zlib Integration: Optional extreme compression

Related MCP server: everything-slim

📊 Performance Benchmarks

Data Type

Compression

Speed

Round-Trip

API Responses

50-65%

0.3ms/KB

✅ Perfect

Database Results

60-70%

0.3ms/KB

✅ Perfect

Time Series

65-75%

0.5ms/KB

✅ Perfect

User Profiles

45-55%

0.3ms/KB

✅ Perfect

Config Files

40-55%

0.1ms/KB

✅ Perfect

🚀 Quick Start

Installation

# Clone repository
git clone https://github.com/muhammedehab35/JSON2TOON-MCP.git
cd json2toon

# Install with pip
pip install -e .

# Or use Docker
docker-compose up -d

MCP Configuration

Add to your Claude Desktop config (~/.config/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "json2toon": {
      "command": "python",
      "args": ["-m", "src.mcp_server"],
      "cwd": "/path/to/json2toon"
    }
  }
}

Docker Configuration:

{
  "mcpServers": {
    "json2toon": {
      "command": "docker",
      "args": ["run", "-i", "json2toon:2.0.0"]
    }
  }
}

💻 Usage Examples

Basic Conversion

from src.advanced_converter import convert_json_to_toon, convert_toon_to_json, CompressionLevel

# Simple conversion with STANDARD level
data = {
    "id": 12345,
    "name": "John Doe",
    "email": "john@example.com",
    "created_at": "2025-01-01T00:00:00Z"
}

# Convert to TOON
toon = convert_json_to_toon(data, level=CompressionLevel.STANDARD)
print(f"Compressed: {toon}")

# Convert back to JSON
original = convert_toon_to_json(toon)
print(f"Restored: {original}")

Advanced Pattern Analysis

from src.pattern_analyzer import AdvancedPatternAnalyzer

analyzer = AdvancedPatternAnalyzer()

# Analyze your data
patterns = analyzer.analyze(large_json_data)

# Get compression strategy
strategy = analyzer.get_compression_strategy(large_json_data)

print(f"Detected {len(patterns)} patterns")
print(f"Expected savings: {strategy.expected_savings * 100:.1f}%")
print(f"Recommended level: {strategy.recommended_level}")
print(f"Reasoning: {strategy.reasoning}")

Smart Optimization

from src.optimizer import SmartOptimizer

optimizer = SmartOptimizer()

# Automatic optimization with profile
result = optimizer.optimize(data, profile="balanced")
# Profiles: "speed", "balanced", "size"

print(f"Used profile: {result['profile_used']}")
print(f"Selected level: {result['level_selected']}")
print(f"Savings: {result['metrics']['savings_percent']:.1f}%")

Batch Processing

from src.advanced_converter import AdvancedTOONConverter, CompressionLevel

converter = AdvancedTOONConverter(level=CompressionLevel.AGGRESSIVE)

# Process multiple items
items = [
    {"id": i, "data": f"Item {i}"}
    for i in range(1000)
]

for item in items:
    toon = converter.json_to_toon(item)
    # Process compressed data

🔬 MCP Tools Examples

In Claude Code

1. Convert with Custom Level

Use the convert_to_toon tool with:
- json_data: <your JSON>
- level: 3 (AGGRESSIVE)

2. Analyze Patterns

Use the analyze_patterns tool to detect:
- Pattern types
- Compression potential
- Optimization recommendations

3. Compare All Levels

Use the compare_levels tool to see:
- Side-by-side comparison
- Savings per level
- Best recommendation

4. Smart Auto-Optimize

Use the smart_optimize tool with:
- json_data: <your JSON>
- profile: "size" (for maximum compression)

📖 Format Specification

TOON v2.0 Structure

{
  "_toon": "2.0",           // Version identifier
  "_lvl": 2,                // Compression level used
  "d": {...},               // Compressed data
  "_refs": {...},           // Optional: structure references
  "_dict": {...}            // Optional: string dictionary
}

Key Abbreviations (Sample)

Original

TOON

Original

TOON

Original

TOON

id

i

email

eml

status

s

name

n

phone

ph

created_at

ca

type

t

address

addr

updated_at

ua

value

v

username

unm

timestamp

ts

150+ abbreviations covering common API, database, and application fields.

Value Optimizations

  • null → ~

  • true → T, false → F

  • Timestamps: $ts:2025-01-01T00:00:00Z

  • UUIDs: $uid:550e8400-e29b-41d4-a716-446655440000

  • String refs: @s0, @s1 (from dictionary)

Schema Compression

Before:

[
  {"id": 1, "name": "Alice", "email": "alice@test.com"},
  {"id": 2, "name": "Bob", "email": "bob@test.com"},
  {"id": 3, "name": "Carol", "email": "carol@test.com"}
]

After (TOON):

{
  "_sch": ["i", "n", "eml"],
  "_dat": [
    [1, "Alice", "alice@test.com"],
    [2, "Bob", "bob@test.com"],
    [3, "Carol", "carol@test.com"]
  ]
}

Savings: ~55-60% for arrays with consistent schemas

🧪 Testing

# Run all tests
pytest tests/ -v

# With coverage
pytest tests/ --cov=src --cov-report=html

# Specific test file
pytest tests/test_converter.py -v

# Run tests in Docker
docker-compose run json2toon-server pytest tests/ -v

Test Coverage

  • ✅ Converter: 100+ test cases covering all compression levels

  • ✅ Pattern Analyzer: 30+ tests for all 17 pattern types

  • ✅ Round-trip: Perfect data integrity verification

  • ✅ Edge cases: Unicode, large numbers, special characters

  • ✅ Performance: Benchmarks for all levels

🐳 Docker Deployment

Build Image

docker build -t json2toon:2.0.0 .

Run with Docker Compose

# Production mode
docker-compose up -d json2toon-server

# Development mode
docker-compose --profile dev up json2toon-dev

Docker Features

  • ✅ Python 3.11 optimized image

  • ✅ Non-root user for security

  • ✅ Health checks

  • ✅ Resource limits (2 CPU, 1GB RAM)

  • ✅ Logging configuration

  • ✅ Development mode with live reload

📐 Architecture

┌─────────────────────────────────────────┐
│         JSON2TOON MCP Server            │
│              (v2.0)                     │
└─────────────┬───────────────────────────┘
              │
    ┌─────────┼─────────┐
    │         │         │
    ▼         ▼         ▼
┌─────────┐ ┌──────────┐ ┌──────────┐
│Advanced │ │Pattern   │ │Smart     │
│Converter│ │Analyzer  │ │Optimizer │
└─────────┘ └──────────┘ └──────────┘
    │         │              │
    └─────────┴──────────────┘
              │
    ┌─────────┼─────────┐
    ▼         ▼         ▼
┌──────┐  ┌──────┐  ┌──────┐
│Schema│  │String│  │Value │
│Comp  │  │ Dict │  │ Comp │
└──────┘  └──────┘  └──────┘

🎯 Pattern Types Detected

  1. API Response - REST, GraphQL, JSON-RPC

  2. Database Record - CRUD, audit logs, versioned

  3. User Data - Profiles, auth, preferences

  4. Pagination - Page-based, offset-based

  5. Nested Address - Street, city, state, country

  6. Nested Coordinates - Lat/lng/alt

  7. Nested Dimensions - Width/height/depth

  8. Nested Metadata - Created/updated by, tags

  9. Homogeneous Array - Same-type elements

  10. Consistent Schema Array - Similar object structures

  11. Repeated Structure - Duplicate patterns

  12. Time Series - Temporal data sequences

  13. Graph Node - Network/graph structures

  14. Tree Structure - Hierarchical data

  15. Enum Values - Limited value sets

  16. Sparse Array - Many null/empty values

  17. Deep Nesting - Complex nested levels

🔧 Development

Setup Development Environment

# Install dev dependencies
pip install -e ".[dev]"

# Format code
black src/ tests/

# Lint
ruff src/ tests/

# Type check
mypy src/

Code Quality Tools

  • black: Code formatting (line length: 100)

  • ruff: Fast Python linter

  • mypy: Static type checking (strict mode)

  • pytest: Testing framework with async support

📊 Comparison with TOON v1.0

Feature

TOON v1.0

JSON2TOON v2.0

Compression Levels

2

4

Key Abbreviations

68

150+

Pattern Types

8

17+

MCP Tools

6

12

Max Savings

60%

85%

String Dictionary

❌

✅

Value Compression

❌

✅

Partial Schema

❌

✅

zlib Support

❌

✅

AI Analysis

Basic

Advanced

Custom Abbreviations

❌

✅

Savings Estimation

❌

✅

🤝 Contributing

Contributions are welcome! Please:

  1. Fork the repository

  2. Create a feature branch (git checkout -b feature/amazing-feature)

  3. Run tests (pytest tests/ -v)

  4. Format code (black src/ tests/)

  5. Commit changes (git commit -m 'Add amazing feature')

  6. Push to branch (git push origin feature/amazing-feature)

  7. Open a Pull Request

🌟 Use Cases

1. Large API Responses

Save 50-65% tokens when storing API responses in Claude conversations.

2. Database Query Results

Compress database results by 60-70% for efficient context usage.

3. Time Series Data

Achieve 65-75% compression on temporal datasets.

4. Configuration Files

Store configs in compact format with 40-55% savings.

5. Codebase Analysis

Fit more file contents in token limits when analyzing code.

6. Log Processing

Compress structured logs by 50-60% for pattern analysis.

🚦 Quick Tips

When to Use Each Level

  • MINIMAL: Quick conversions, need high speed

  • STANDARD: General purpose (best balance)

  • AGGRESSIVE: Large datasets, high savings needed

  • EXTREME: Maximum compression, archival use

Optimization Profiles

  • speed: Prefer MINIMAL/STANDARD levels

  • balanced: Auto-select based on data (recommended)

  • size: Prefer AGGRESSIVE/EXTREME levels

Best Practices

  1. ✅ Analyze patterns first with analyze_patterns

  2. ✅ Use smart_optimize for automatic best results

  3. ✅ Validate with validate_toon after conversion

  4. ✅ Use estimate_savings before large batch jobs

  5. ✅ Monitor with get_server_stats for metrics

pip install -e .
python -m src.mcp_server

Available Tools

12 tools
analyze_patternsC

Deep analysis of JSON patterns with AI-powered detection

ParametersJSON Schema
NameRequiredDescriptionDefault
json_dataYesJSON data to analyze
detailedNoInclude detailed pattern information

TDQS

C2.6/5.0
Behavior1/5

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

No annotations are provided, and the description fails to disclose behavioral traits such as whether the operation is read-only, resource-intensive, or what the response looks like. A single vague sentence is insufficient.

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 a single sentence with no wasted words, but it lacks structure and could be more front-loaded with key information.

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

Completeness1/5

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

Given the lack of annotations and output schema, the description must compensate. It does not specify return values, error conditions, or the scope of analysis, making it inadequate for the tool's complexity.

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 descriptions for both parameters, so the baseline is 3. The description adds no additional meaning beyond the schema, e.g., not explaining how 'detailed' affects analysis.

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 states the tool conducts deep analysis of JSON patterns with AI detection, clearly specifying the verb and resource. It distinguishes from sibling tools like batch_convert or calculate_metrics, but could be more specific about what types of patterns are detected.

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, nor does it mention prerequisites or exclusions. Without context signals, an agent lacks information to choose appropriately.

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

batch_convertC

Batch convert multiple JSON objects

ParametersJSON Schema
NameRequiredDescriptionDefault
json_arrayYesArray of JSON objects to convert
levelNoCompression level

TDQS

C2.5/5.0
Behavior2/5

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

No annotations are provided, so the description must fully disclose behavioral traits. It only says 'batch convert', without mentioning any side effects, error handling, output format, or whether the operation is destructive. This is insufficient for safe invocation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very short (one sentence), which is concise but lacks necessary detail. It is front-loaded (verb + resource), but fails to provide essential information about the conversion target, making it under-specified.

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?

Given the tool has 2 parameters and no output schema, the description should more thoroughly explain the conversion process, expected output, and batch behavior. It covers none of these, leaving significant gaps.

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% as both parameters have descriptions. The description adds no additional meaning beyond the schema, but the baseline score of 3 is appropriate since the schema already documents the parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states 'Batch convert multiple JSON objects' which indicates the tool converts JSON objects, but it does not specify the target format (e.g., toon, CSV, etc.). This ambiguity makes it unclear how it differs from sibling tools like convert_to_json or convert_to_toon. The purpose is vague.

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?

No guidance is provided on when to use this tool versus alternatives. Given sibling tools such as convert_to_json and convert_to_toon, the description offers no context for selection criteria or when batch conversion is appropriate.

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

calculate_metricsC

Calculate detailed compression metrics and savings

ParametersJSON Schema
NameRequiredDescriptionDefault
json_dataYesOriginal JSON data
levelNoCompression level to test

TDQS

C2.5/5.0
Behavior2/5

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

No annotations provided, and the description only says 'Calculate.' It does not disclose side effects, whether the tool is read-only, or any rate limits. For a calculation tool, read-only nature should be explicit.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely brief, consisting of four words. While concise, it lacks structure and fails to include essential details for effective use.

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?

No output schema is provided, and the description does not explain what metrics are returned. Given the complexity of compression metrics, the description is incomplete for an agent to understand the full behavior.

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%, so parameters are well-documented. However, the description adds no additional meaning beyond the schema. It does not explain expected formats or constraints for json_data or level.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states 'Calculate detailed compression metrics and savings,' which identifies a verb and resource but is vague. Among sibling tools like estimate_savings, it does not clearly distinguish itself, making selection ambiguous.

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?

No guidance on when to use this tool versus alternatives such as estimate_savings or analyze_patterns. No context on prerequisites, typical use cases, or outcomes.

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

compare_levelsC

Compare compression across all levels

ParametersJSON Schema
NameRequiredDescriptionDefault
json_dataYesJSON data to compare

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description must convey behavioral traits. It only says 'compare compression across all levels', but doesn't disclose whether it modifies data, requires specific permissions, or how it handles large inputs. The behavior is too abstract.

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 extremely concise (one phrase), which is efficient. However, the brevity sacrifices clarity and completeness. It is well-structured but overly terse.

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?

With a single parameter and no output schema, the description should explain what the tool returns or how the comparison works. It fails to do so, leaving the agent guessing about the output format or interpretation of results.

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 one parameter ('json_data') already described in the schema as 'JSON data to compare'. The description adds no additional meaning beyond what the schema provides, so baseline score applies.

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 states the action 'compare' and the resource 'compression across all levels', which indicates the tool compares compression settings. However, 'levels' is ambiguous without context (e.g., compression levels?), and it doesn't differentiate from siblings like 'estimate_savings' or 'get_optimal_strategy'.

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?

No guidance on when to use this tool versus alternatives (e.g., 'analyze_patterns', 'calculate_metrics'). The description lacks context for when this comparison is appropriate or what prerequisites exist.

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

convert_to_jsonB

Convert TOON format back to standard JSON

ParametersJSON Schema
NameRequiredDescriptionDefault
toon_dataYesTOON formatted data

TDQS

B3.4/5.0
Behavior2/5

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

No annotations are provided, and the description only states a conversion without disclosing any behavioral traits such as side effects, error handling, or permissions needed for a potentially destructive operation.

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 a single, concise sentence that front-loads the essential purpose. It could be slightly more informative but is appropriately sized for a simple tool.

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?

For a straightforward conversion tool with one parameter and no output schema, the description covers the basic purpose but lacks details on return format, error conditions, or input validation, leaving gaps for the agent.

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 schema already defines the single parameter 'toon_data' with a description. The tool description adds context about the output format but does not provide additional semantic details about the parameter beyond what is in the schema.

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 (convert) and the specific transformation from TOON format to standard JSON, distinguishing it from its sibling convert_to_toon which performs the reverse operation.

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 description implies usage when one has TOON data and wants JSON, but does not provide explicit guidance on when not to use this tool or mention alternatives like validate_toon for data validation.

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

convert_to_toonC

Convert JSON to TOON format with specified compression level

ParametersJSON Schema
NameRequiredDescriptionDefault
json_dataYesJSON data to convert
levelNoCompression level (1=MINIMAL, 2=STANDARD, 3=AGGRESSIVE, 4=EXTREME)

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description carries full burden. It only says 'Convert', which implies transformation but doesn't disclose whether it's read-only, permissions needed, size limits, or other behavioral traits. Minimal transparency.

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 one concise sentence (9 words) that directly states the purpose. No wasted words, but could benefit from slightly more detail without becoming verbose.

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?

Given multiple sibling conversion tools and no output schema, the description lacks context about the TOON format, what output to expect, and how this tool fits into the workflow. Incomplete for effective use.

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 descriptions for both parameters. The description adds 'with specified compression level', but this is already expressed in the schema for level. No new meaning beyond the schema is provided.

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 states the action (Convert), the resource (JSON to TOON format), and a key parameter (compression level). It distinguishes from siblings like convert_to_json by specifying direction JSON -> TOON, but could be more explicit about what TOON format is.

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?

No guidance on when to use this tool versus alternatives like batch_convert or convert_to_json. No prerequisites, exclusions, or context provided for selection.

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

estimate_savingsC

Estimate compression savings without converting

ParametersJSON Schema
NameRequiredDescriptionDefault
json_dataYesJSON data to estimate

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are present, so the description carries full responsibility for behavioral disclosure. It only states the action but does not describe side effects, return values, error conditions, or any constraints (e.g., idempotency, rate limits). The tool likely performs a read-only estimation, but this is not confirmed.

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 a single sentence with no extraneous words, effectively communicating the core action in a very concise manner.

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?

Given the lack of output schema and the presence of many sibling tools, the description is insufficient. It does not explain the format of the estimation result (e.g., percentage, numeric value), prerequisites for input, or how to interpret the output. The agent would need to infer usage from context.

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 input schema has one required parameter 'json_data' with a description 'JSON data to estimate'. Since schema description coverage is 100%, the description adds marginal value by stating 'without converting', but it does not provide additional meaning beyond what the schema 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 'Estimate compression savings without converting' clearly indicates the tool's purpose: to estimate savings from compression without performing the conversion. It distinguishes from sibling conversion tools like 'convert_to_toon' and 'batch_convert', but could be more specific about the type of compression or target format.

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?

No explicit guidance is provided on when to use this tool versus alternatives. The phrase 'without converting' implies it is a preliminary step before conversion, but no alternative tools (e.g., 'get_optimal_strategy', 'suggest_abbreviations') are mentioned or compared.

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

get_optimal_strategyC

Get AI-recommended optimal compression strategy

ParametersJSON Schema
NameRequiredDescriptionDefault
json_dataYesJSON data to analyze

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description must fully disclose behavior. It only states the purpose, omitting details like side effects, performance, or return format. This is insufficient for an AI agent to understand what happens when invoked.

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 a single concise sentence that earns its place. However, it could be slightly more structured to include additional context without bloat.

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 is too minimal for a tool that returns a recommendation. There is no output schema, and no explanation of what the strategy looks like or any limitations. Context around its use among siblings is lacking.

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 100% (one parameter with a clear description 'JSON data to analyze'). The tool description adds no extra parameter information, but the baseline score of 3 applies because the schema already covers the parameter meaning.

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 states the tool gets an AI-recommended optimal compression strategy, which is a specific verb-resource pair. However, it does not differentiate from siblings like smart_optimize or estimate_savings, which could overlap.

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?

No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, exclusions, or context, leaving the agent without decision support.

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

get_server_statsC

Get comprehensive server statistics and performance metrics

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

C2.9/5.0
Behavior2/5

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

No annotations exist, and the description does not disclose any behavioral traits such as performance impact, data freshness, or error conditions. The brief description fails to inform the agent about side effects or operational details.

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 a single sentence with no wasted words. However, it could be slightly more informative while maintaining conciseness.

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?

Given the lack of annotations and output schema, the description is too brief to fully equip an agent. 'Comprehensive' is vague, and the agent is left without knowing what metrics are returned or how to use the output.

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 tool has no parameters, so the description does not need to elaborate on them. While the schema coverage is 100%, the description adds minimal value by using 'comprehensive' and 'performance metrics', but it does not provide meaningful detail beyond the tool's name.

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 uses 'Get comprehensive server statistics and performance metrics', which clearly states the verb and resource. It is specific enough to understand the tool's function, though it does not differentiate from sibling tools like 'calculate_metrics'.

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?

No guidance is provided on when to use this tool versus its siblings. The description lacks context on prerequisites or scenarios.

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

smart_optimizeC

Automatically detect and apply optimal compression

ParametersJSON Schema
NameRequiredDescriptionDefault
json_dataYesJSON data to optimize
profileNoOptimization profile (speed, balanced, size)balanced

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description must disclose effects. It only states the action without explaining whether compression is lossless, destructive, or what side effects occur. The profile parameter implications are not explained.

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 a single concise sentence with no waste, but it could be more informative without losing conciseness.

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?

Given no output schema and no annotations, the description is insufficient. It does not explain what the tool returns, how to interpret results, or whether it modifies the input data.

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 descriptions for both parameters. The description adds no additional meaning beyond the schema, so baseline score of 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 states a clear action ('detect and apply optimal compression') and resource ('compression'), but lacks specificity on what type of compression (e.g., JSON data) and how it relates to sibling tools like convert_to_json.

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?

No guidance provided on when to use this tool versus alternatives such as estimate_savings or get_optimal_strategy. The description offers no context, prerequisites, or exclusions.

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

suggest_abbreviationsC

Generate custom key abbreviations for your data

ParametersJSON Schema
NameRequiredDescriptionDefault
json_dataYesJSON data to analyze
min_frequencyNoMinimum key frequency to suggest abbreviation

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description must disclose behavioral traits. It only says 'generate', leaving side effects, permissions, and return behavior unspecified. Minimal transparency.

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 a single, front-loaded sentence with no fluff. It is concise but might be too brief, lacking depth for full understanding.

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?

Given no output schema and only two parameters, the description does not explain the output format or behavior beyond generation. It feels incomplete for ensuring correct tool invocation.

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 clear parameter descriptions. The tool description adds context that abbreviations are 'custom' and for 'your data', but doesn't enhance understanding beyond what the schema already provides.

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 states the tool generates custom key abbreviations from data. It is distinct from sibling tools like analyze_patterns or calculate_metrics, although it doesn't explicitly differentiate.

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?

No guidance is provided on when to use this tool versus alternatives, nor any prerequisites or conditions. The description implies usage for key abbreviation generation but lacks exclusions or when-not advice.

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

validate_toonA

Validate TOON format and test round-trip conversion

ParametersJSON Schema
NameRequiredDescriptionDefault
toon_dataYesTOON data to validate

TDQS

A3.6/5.0
Behavior2/5

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

No annotations are provided, so the description must carry the full burden. It mentions validation and round-trip conversion but does not disclose what happens on failure (e.g., return boolean, error), whether it modifies data, or any side effects. This is insufficient for a validation tool.

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 a single sentence that front-loads the action, but it combines two aspects (validate and test round-trip) which could be clarified. Still concise and efficient.

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?

Given the tool's simplicity (1 parameter, no output schema), the description is fairly complete in stating its purpose. However, it omits information about return behavior, which is important for a validation tool. Adequate but with gaps.

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 schema describes the parameter 'toon_data' simply as 'TOON data to validate'. The description adds value by clarifying that validation includes testing round-trip conversion, providing context beyond the schema.

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 explicitly states 'Validate TOON format and test round-trip conversion', specifying both the action (validate) and the resource (TOON format), and distinguishes it from sibling tools like batch_convert and convert_to_toon.

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 description implies usage for validating TOON data but provides no explicit guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 12 tool updatesv2.0.0
    • First observedanalyze_patterns
    • First observedbatch_convert
    • First observedcalculate_metrics
    • First observedcompare_levels
    • First observedconvert_to_json
    • First observedconvert_to_toon
    • First observedestimate_savings
    • First observedget_optimal_strategy
    • First observedget_server_stats
    • First observedsmart_optimize
    • First observedsuggest_abbreviations
    • First observedvalidate_toon
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TDQS

B3.2/5.0
Disambiguation4/5

Tools have distinct purposes overall, but some analytical tools like 'analyze_patterns', 'get_optimal_strategy', and 'smart_optimize' could be confused; however, descriptions help differentiate them.

Naming Consistency4/5

Most tools follow a verb_noun pattern with snake_case, but 'smart_optimize' and 'batch_convert' deviate slightly from the typical verb_noun structure, though they remain readable.

Tool Count5/5

Twelve tools cover the domain well—conversion, validation, analysis, optimization, batch processing, and metrics—without being excessive or sparse.

Completeness5/5

The tool set covers the full workflow: converting to/from TOON, validating, analyzing patterns, optimizing, estimating savings, and batch processing. No obvious gaps for the intended purpose.

Maintenance

ActivityInactive
ResponsivenessSyncing

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