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IDE Chat Summarizer

by RalphLi213
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IDE Chat Summarizer MCP Server

A Model Context Protocol (MCP) server designed for IDE users to summarize chat conversations with AI and automatically store them as organized markdown files in your notes directory. Perfect for VS Code, Cursor, and Visual Studio users.

šŸŽÆ Purpose

This MCP server transforms chat conversations into structured summaries and saves them to your configured notes directory (default: ~/Documents/ChatSummaries) for easy reference and organization. Perfect for keeping track of important discussions, decisions, and insights from your AI conversations.

Related MCP server: Notes MCP Server

āœØ Features

šŸ”§ Tools

  • summarize_chat: Summarize chat history and save as markdown

    • Supports different summary styles (brief, detailed, bullet_points)

    • NEW: Smart code detection and preservation of final solutions

    • NEW: Options for handling large histories (include_full_history, create_separate_full_history)

    • Auto-generates timestamped filenames

    • Custom titles for better organization

    • Smart handling: Uses collapsible sections for large conversations (>1MB)

  • summarize_large_chat: Handle extremely large chat histories (NEW!)

    • Automatically chunks huge conversations into manageable pieces

    • Creates individual files for each chunk with overlap for context

    • Generates a master summary file linking all chunks

    • Configurable chunk size (default: 50,000 characters)

    • Perfect for multi-hour conversations or extensive documentation

  • list_summaries: View recent chat summaries

    • Shows creation dates and file sizes

    • Configurable limit for results

    • Sorted by most recent first

  • delete_summary: Remove unwanted summary files

    • Safety checks to only delete chat summary files

    • Confirmation messages

šŸ“„ Resources

  • summary://{filename}: Read content of specific summary files

  • notes://directory: Get information about your notes directory

šŸ’¬ Prompts

  • create_summary_prompt: Generate customized prompts for different conversation types

    • Supports: general, technical, meeting, brainstorm

    • Focus areas: all, decisions, action_items, insights

šŸš€ Installation and Setup

Prerequisites

  • uv package manager

  • Python 3.13+

Install Dependencies

uv sync

Run the Server

1. Using MCP Inspector (Recommended for Development)

uv run mcp dev main.py

Opens a web interface at http://localhost:6274 for testing.

2. Direct Server Run

uv run python main.py

3. Using MCP CLI

uv run mcp run main.py

šŸ“ Usage Examples

Handling Large Chat Histories

For huge chat histories, you have several options depending on your client:

Option 1: Standard with Full History (Recommended)

In VS Code/Cursor/Visual Studio:

  1. Use the summarize_chat tool

  2. Parameters to set:

    • chat_history: Copy and paste your entire conversation

    • title: "Long AI Discussion" (optional)

    • summary_style: Choose "detailed", "brief", or "bullet_points"

    • include_full_history: āœ… True (default - keeps everything!)

    • create_separate_full_history: āŒ False

Result: Creates 1 file with smart organization - collapsible sections for large histories (>1MB)

Option 2: Separate Files for Organization

In VS Code/Cursor/Visual Studio:

  1. Use the summarize_chat tool

  2. Parameters to set:

    • chat_history: Your huge conversation

    • title: "Extended Coding Session"

    • summary_style: "detailed"

    • include_full_history: āœ… True

    • create_separate_full_history: āœ… True (creates 2 files!)

Result: Creates 2 files: chat_summary_*.md + chat_full_*.md

Option 3: Extremely Large Histories (Chunking)

In VS Code/Cursor/Visual Studio:

  1. Use the summarize_large_chat tool (NEW!)

  2. Parameters to set:

    • chat_history: Your massive conversation

    • title: "All Day Coding Session"

    • chunk_size: 50000 (characters per chunk)

    • overlap: 5000 (overlap between chunks for context)

Result: Creates master summary + individual chunk files with preserved context

Summarizing a Regular Chat

In VS Code/Cursor/Visual Studio:

  1. Use the summarize_chat tool

  2. Fill in the parameters:

    • chat_history: 

      User: How do I optimize my Python code?
AI: Here are several optimization techniques...
User: What about memory usage?
AI: For memory optimization, consider...

    • title: "Python Optimization" (optional)

    • summary_style: "detailed" (or "brief", "bullet_points")

Result: Creates chat_summary_20240115_143022_Python_Optimization.md with summary and full conversation

Listing Your Summaries

In VS Code/Cursor/Visual Studio:

  1. Use the list_summaries tool

  2. Optional parameter:

    • limit: 10 (number of summaries to show)

Result: Shows recent summaries with dates, sizes, and filenames

Reading a Summary

In VS Code/Cursor/Visual Studio:

  1. Use the summary://filename resource

  2. Parameter:

    • filename: chat_summary_20240115_143022_Python_Optimization.md

Result: Returns full content of the summary file

Managing Summaries

In VS Code/Cursor/Visual Studio:

  1. Use the delete_summary tool

  2. Parameter:

    • filename: Name of the file to delete

Result: Safely removes the summary file

šŸŽÆ Quick Start Guide for Large Histories

Step-by-Step: Saving Your Huge Chat

  1. šŸ“‹ Copy your entire conversation from your chat interface

  2. šŸ”§ Open MCP tools in VS Code/Cursor/Visual Studio

  3. āš™ļø Choose your approach:

    For most large chats (recommended):

    • Tool: summarize_chat

    • Paste conversation in chat_history

    • Set include_full_history: true

    • Leave other defaults

    For extremely large chats (>100MB):

    • Tool: summarize_large_chat

    • Paste conversation in chat_history

    • Leave defaults (50k chunk size)

  4. šŸš€ Run the tool - Your conversation is now safely stored!

What You Get:

  • āœ… Complete preservation - Nothing lost from original

  • āœ… Smart organization - Easy to read and navigate

  • āœ… Searchable files - Find anything quickly

  • āœ… Multiple formats - Summary + full history options

šŸ“ File Organization

Summaries are saved to your configured notes directory (default: ~/Documents/ChatSummaries)

Filename Pattern: chat_summary_YYYYMMDD_HHMMSS_[title].md

Example Files:

  • chat_summary_20240115_143022_Python_Optimization.md

  • chat_summary_20240115_150330_API_Design_Discussion.md

  • chat_summary_20240115_162145_Untitled.md

šŸ“‹ Summary Styles

Brief

  • 2-3 sentence overview

  • Key points only

  • Quick reference

Detailed (Default)

  • Comprehensive summary

  • Main topics and subtopics

  • Key decisions and insights

  • Structured with headings

Bullet Points

  • Organized bullet list format

  • Main topics as bullets

  • Easy to scan

  • Action-oriented

šŸ’» Smart Code Detection

The summarizer automatically detects and preserves important code from your conversations:

šŸŽÆ What It Detects:

  • Code blocks (language ... )

  • Inline code (code)

  • Final solutions - Code that appears near keywords like "final", "solution", "working", "complete"

  • Late-conversation code - Code blocks in the last 30% of the conversation (likely to be solutions)

šŸ” How It Works:

  1. Scans conversation for all code blocks and inline code

  2. Identifies final solutions using context analysis and position weighting

  3. Preserves in summary with proper syntax highlighting

  4. Organizes by importance - Final solutions first, then other code snippets

šŸ“‹ What You Get:

  • šŸ’» Final Code Solutions section with working code

  • šŸ“ Code Snippets section with relevant code examples

  • Language detection and proper syntax highlighting

  • Context preservation - knows which code is the final answer

šŸ”§ Client Integration

VS Code

When adding this MCP server to VS Code:

Command: 

uv run --directory "/path/to/your/ide-chat-summarizer-mcp" python main.py

Replace /path/to/your/ide-chat-summarizer-mcp with your actual project directory path.

Server Name: chat-summarizer

Cursor

In Cursor settings:

  1. Go to Extensions ā†’ MCP

  2. Add Server with:

    • Name: chat-summarizer

    • Command: uv run --directory "/path/to/your/ide-chat-summarizer-mcp" python main.py

    • Working Directory: /path/to/your/ide-chat-summarizer-mcp

Visual Studio (Full IDE)

Visual Studio has MCP support! Here's how to configure it:

Native MCP Configuration

  1. Open the Configure MCP server dialog in Visual Studio

  2. Fill out the configuration:

Server ID: 

chat-summarizer

Type: 

stdio

(Keep this as default)

Command (with optional arguments):

uv run --directory "/path/to/your/ide-chat-summarizer-mcp" python main.py

(Replace /path/to/your/mcp-server-demo with your actual project path)

Environment Variables (Optional):

  • Click "+ Add" if you want a custom notes directory

  • Name: CHAT_NOTES_DIR

  • Value: /path/to/your/custom/notes/directory

  1. Click "Save"

  2. Restart Visual Studio

šŸŽÆ Typical Workflow

For VS Code/Cursor/Visual Studio:

  1. šŸ’» Power up computer

  2. šŸš€ Open VS Code/Cursor/Visual Studio

  3. šŸ’¬ Start chatting with AI

  4. šŸ”§ Use MCP tools directly - server starts automatically!

For MCP Inspector (Testing):

  1. šŸ’» Power up computer

  2. šŸ“ Navigate to project directory

  3. āš” Run: uv run mcp dev main.py

  4. šŸŒ Use web interface at http://localhost:6274

šŸ’” Pro Tip: Most clients (VS Code, Cursor, Visual Studio) automatically start your MCP server when you use the tools. You only need to manually start the server for testing with MCP Inspector.

šŸ“Š Directory Information

The server provides insights about your notes directory:

  • Total markdown files count

  • Chat summaries count

  • Directory size

  • Last activity timestamp

šŸ›  Configuration

Notes Directory

Default: ~/Documents/ChatSummaries (user's Documents folder)

Method 1: Environment Variable (Recommended) Set the CHAT_NOTES_DIR environment variable:

# Windows
set CHAT_NOTES_DIR=C:\Users\YourName\Notes\ChatSummaries

# macOS/Linux
export CHAT_NOTES_DIR="/home/username/Notes/ChatSummaries"

Method 2: Edit main.py Modify the NOTES_DIR variable in main.py:

# Examples:
NOTES_DIR = Path("your/custom/path")              # Custom absolute path
NOTES_DIR = Path.home() / "Notes" / "Summaries"  # User's home directory
NOTES_DIR = Path.cwd() / "summaries"             # Relative to project directory

Summary Templates

Customize summary styles by modifying the prompt generation in the summarize_chat function.

šŸ” Project Structure

ide-chat-summarizer-mcp/
ā”œā”€ā”€ main.py              # MCP server implementation
ā”œā”€ā”€ pyproject.toml       # Project configuration
ā”œā”€ā”€ README.md           # This documentation
ā”œā”€ā”€ mcp-config.json     # MCP server configuration
ā””ā”€ā”€ uv.lock            # Dependency lock file

šŸ“š Learn More

šŸ¤ Usage Tips

  1. Organize by Topic: Use meaningful titles when summarizing

  2. Regular Cleanup: Use delete_summary to remove outdated summaries

  3. Style Selection: Choose the right summary style for your needs

  4. Batch Processing: Use list_summaries to review and manage multiple summaries

Transform your conversations into organized, searchable knowledge with the Chat History Summarizer MCP Server!

Available Tools

4 tools
delete_summaryB

Delete a chat summary file.

Args: filename: Name of the summary file to delete

Returns: Confirmation message

ParametersJSON Schema
NameRequiredDescriptionDefault
filenameYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states the action ('Delete') but doesn't clarify if this is permanent, requires specific permissions, has side effects (e.g., affecting other data), or provides error handling. The mention of a 'confirmation message' in returns is helpful but minimal. More behavioral context is needed for a mutation tool.

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 extremely concise and well-structured: a clear purpose statement followed by Args and Returns sections. Every sentence earns its place with no wasted words. It's front-loaded with the main action, making it easy to scan and understand quickly.

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 complexity (simple deletion with one parameter), the description covers the basics: purpose, parameter meaning, and return type. However, with no annotations and an output schema (which handles return structure), it lacks behavioral context like permanence or error conditions. For a destructive operation, this is minimally viable but leaves 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 description coverage is 0%, so the description must compensate. It adds the meaning of 'filename' as 'Name of the summary file to delete', which clarifies the parameter's purpose. However, it doesn't provide format details (e.g., file extension, case sensitivity) or constraints beyond what's implied. Given the single parameter, this is adequate but not comprehensive.

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 verb ('Delete') and resource ('a chat summary file'), making the purpose immediately understandable. It distinguishes from siblings like 'list_summaries' and 'summarize_chat' by focusing on deletion rather than listing or creation. However, it doesn't specify whether this is permanent deletion or reversible, which prevents a perfect score.

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 doesn't mention prerequisites (e.g., needing to know the filename from 'list_summaries'), error conditions (e.g., what happens if the file doesn't exist), or when not to use it. The agent must infer usage from the tool name alone.

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

list_summariesB

List recent chat summaries from the notes directory.

Args: limit: Maximum number of summaries to list (default: 10)

Returns: List of recent summary files with their creation dates

ParametersJSON Schema
NameRequiredDescriptionDefault
limitNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

B3.3/5.0
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 burden of behavioral disclosure. It states the tool lists 'recent' summaries and returns files with creation dates, but lacks details on what 'recent' means (e.g., time-based criteria), how ordering works, error handling, or permissions needed. For a tool with no annotation coverage, this leaves significant behavioral gaps.

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 well-structured and concise, with a clear purpose statement followed by separate 'Args' and 'Returns' sections. Every sentence adds value: the first defines the action, and the others detail inputs and outputs without redundancy. It's appropriately sized for a simple list tool.

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

Completeness4/5

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

Given the tool's low complexity (one optional parameter) and the presence of an output schema (which handles return values), the description is mostly complete. It covers the purpose, parameter semantics, and return overview. However, it lacks behavioral details like ordering or error handling, which would be beneficial even with an output schema, keeping it from a perfect score.

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 description adds meaningful context for the single parameter 'limit,' explaining it's the 'Maximum number of summaries to list' with a default of 10. Since schema description coverage is 0% (the schema only provides a title and type), the description fully compensates by clarifying the parameter's purpose and default value, going beyond what the schema 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's purpose: 'List recent chat summaries from the notes directory.' It specifies the verb ('List'), resource ('chat summaries'), and location ('notes directory'). However, it doesn't explicitly differentiate from sibling tools like 'summarize_chat' (which creates summaries) or 'delete_summary' (which removes them), though the 'list' vs 'create/delete' distinction is implied.

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 doesn't mention sibling tools like 'summarize_chat' for creating summaries or 'delete_summary' for removal, nor does it specify prerequisites (e.g., existing summaries in the directory). Usage is implied by the action 'list,' but no explicit context or exclusions are given.

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

summarize_chatA

Summarize chat history and save it as a markdown file.

Args: chat_history: The chat conversation text to summarize title: Optional title for the summary (will be used in filename) summary_style: Style of summary - 'brief', 'detailed', or 'bullet_points' include_full_history: Whether to include the full chat history in the summary file (default: True) create_separate_full_history: Whether to create a separate file with just the full history (default: False)

Returns: Path to the created summary file and preview of the summary

ParametersJSON Schema
NameRequiredDescriptionDefault
chat_historyYes
titleNo
summary_styleNodetailed
include_full_historyNo
create_separate_full_historyNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.2/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it creates files (implied mutation/write operation), specifies output format (markdown), and mentions return values (path and preview). However, it doesn't cover permissions needed, rate limits, error conditions, or file system implications (e.g., overwriting).

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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by a structured 'Args' and 'Returns' section that efficiently documents parameters and output. Every sentence earns its place with no redundancy or waste.

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

Completeness4/5

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

Given 5 parameters with 0% schema coverage and no annotations, the description does an excellent job explaining parameters and output (with an output schema present). However, for a tool that creates files, it could better address behavioral aspects like file naming, location, or error handling to be fully complete.

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?

Schema description coverage is 0%, so the description must compensate. It adds significant meaning beyond the schema by explaining all 5 parameters: 'chat_history' (conversation text to summarize), 'title' (used in filename), 'summary_style' (with enum values), and two boolean flags with their purposes. This fully documents parameter semantics, though it doesn't specify format details like filename conventions.

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 specific action ('Summarize chat history and save it as a markdown file') and distinguishes it from siblings like 'summarize_large_chat' (implied for larger chats), 'delete_summary', and 'list_summaries'. It specifies both the processing (summarize) and output (save as markdown file) aspects.

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?

The description provides clear context for when to use this tool (to summarize chat history into a markdown file), but doesn't explicitly state when not to use it or name alternatives like 'summarize_large_chat' for specific cases. The context is sufficient but lacks explicit exclusions or sibling comparisons.

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

summarize_large_chatB

Handle extremely large chat histories by chunking them into manageable pieces. Each chunk gets its own summary, then creates a master summary.

Args: chat_history: The large chat conversation text to summarize title: Optional title for the summary chunk_size: Size of each chunk in characters (default: 50,000) overlap: Overlap between chunks in characters (default: 5,000)

Returns: Information about the chunked summaries created

ParametersJSON Schema
NameRequiredDescriptionDefault
chat_historyYes
titleNo
chunk_sizeNo
overlapNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

B3.3/5.0
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 burden. It describes the chunking and summarization process, but lacks details on behavioral traits such as performance characteristics (e.g., processing time, rate limits), error handling (e.g., what happens with invalid input), or side effects (e.g., whether summaries are stored persistently). The mention of 'Returns: Information about the chunked summaries created' is vague and doesn't specify format or content.

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 appropriately sized and front-loaded, starting with the core purpose, followed by a clear breakdown of arguments and returns. Each sentence adds value, with no redundant information. However, the structure could be slightly improved by integrating the 'Args' and 'Returns' sections more seamlessly into the narrative flow.

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 complexity (chunking and summarizing large histories) and the presence of an output schema (which should cover return values), the description is moderately complete. It explains the process and parameters well, but lacks context on prerequisites (e.g., input format requirements), limitations (e.g., maximum size), or integration with sibling tools like 'delete_summary' or 'list_summaries'. The output schema existence reduces the need to detail returns, but behavioral gaps remain.

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 description adds significant meaning beyond the input schema, which has 0% schema description coverage. It explains each parameter's purpose: 'chat_history' as 'The large chat conversation text to summarize', 'title' as 'Optional title for the summary', 'chunk_size' as 'Size of each chunk in characters (default: 50,000)', and 'overlap' as 'Overlap between chunks in characters (default: 5,000)'. This compensates well for the low schema coverage, though it doesn't detail units or constraints beyond defaults.

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's purpose: 'Handle extremely large chat histories by chunking them into manageable pieces. Each chunk gets its own summary, then creates a master summary.' This specifies the verb (chunk and summarize), resource (large chat histories), and method (chunking with individual and master summaries). It distinguishes from 'summarize_chat' by emphasizing handling 'extremely large' histories through chunking, though it doesn't explicitly contrast with siblings.

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 when to use this tool by stating it's for 'extremely large chat histories' and involves chunking, suggesting it should be used for large inputs where 'summarize_chat' might not suffice. However, it doesn't provide explicit guidance on when to choose this over 'summarize_chat' or when not to use it, nor does it mention alternatives like 'list_summaries' or 'delete_summary'.

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. 4 tool updates
    • First observeddelete_summary
    • First observedlist_summaries
    • First observedsummarize_chat
    • First observedsummarize_large_chat
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TDQS

A3.5/5.0
Disambiguation4/5

The tools are mostly distinct with clear purposes: list, create (summarize), and delete. However, summarize_chat and summarize_large_chat have overlapping functionality for summarizing chats, which could cause confusion about when to use each. The descriptions help differentiate them based on chat size, but the overlap is notable.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (e.g., delete_summary, list_summaries, summarize_chat, summarize_large_chat). The naming is predictable and readable, with no deviations in style or convention.

Tool Count4/5

With 4 tools, the count is reasonable for a chat summarizer server, covering core operations like list, create, and delete. It's slightly lean but well-scoped, though adding a tool for updating or viewing summaries might enhance completeness.

Completeness3/5

The toolset covers create (summarize), list, and delete operations, but lacks a tool for updating or retrieving a specific summary (e.g., get_summary). This gap could hinder agents from modifying existing summaries or accessing details without listing all. The domain is chat summarization, and while basic workflows are supported, the absence of update/retrieve is a notable omission.

Maintenance

ActivityInactive
ResponsivenessNo issues

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