Which integrations are available for this server?

Automatically scans for FastAPI to extract API signatures and validate code patterns, helping prevent the reinvention of existing functionality. Scans for Flask in the environment to extract API signatures and validate code against existing library patterns. Supports exporting thinking sessions and project indexes to Markdown format for documentation and team collaboration. Discovers Pydantic in the development environment to extract API signatures and validate data model usage patterns. Analyzes the Python environment to discover installed packages, extract API signatures, and validate code against existing libraries.

How do I use Memory Bank MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Memory Bank MCP start a coding session for the payment API and check for existing packages" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Memory Bank MCP

by spideynolove

Overview Schema Related Servers Score Discussions

Python

Local

🧠 Memory Bank MCP

A Model Context Protocol (MCP) server for Claude Code that enables persistent memory, structured thinking, team collaboration, and project-based knowledge management — with full export, revision, and analysis capabilities. Now featuring comprehensive coding integration to prevent package reinvention and enforce existing API usage.

🚀 Features

🧠 Core Memory Management

Session-Based Thinking: Start with a problem and track related insights
Persistent Storage: Store and retrieve memories across sessions
Collections: Group related memories with clear purposes
Revision & Dependencies: Refine ideas, track changes and links

💻 Coding Integration (NEW)

Package Discovery: Auto-scan installed packages and extract API signatures
Reinvention Prevention: Validate code against existing libraries before implementation
Code Pattern Storage: Store and retrieve proven code templates and examples
Coding Sessions: Specialized session types for development workflows
Validation Gates: Catch potential issues and suggest existing solutions

📦 Project & Export System

Export to Markdown/JSON: Full or filtered memory exports
Project Structure Generation: Standardized folders for teams
Project Indexing: Maintain status updates and documentation
Context Loading: Load ongoing work for seamless continuation

📊 Search & Analytics

Tag-Based Search: Find insights by topics or keywords
Importance Scores: Prioritize content using confidence metrics
Session Analysis: Detect contradictions, gaps, and themes

⚡ Quick Start

git clone https://github.com/spideynolove/memory-bank-mcp
cd memory-bank-mcp
uv sync
uv run main.py

Requires Python 3.10+ and uv

🛠 Installation Options

Direct run: uv run main.py
Global install: uv tool install .
Development mode: uv pip install -e .

🧪 Test

uv run -c "import main; print('Installation successful')"

🔧 Session Workflow (API Example)

Basic Memory Session

create_memory_session(
    problem="Implement user auth",
    success_criteria="Secure + scalable",
    constraints="Use existing DB"
)

store_memory(
    content="Use JWT with refresh",
    tags="auth,jwt",
    importance=0.9
)

analyze_memories()
export_session_to_file("auth_session.md")

Coding Session with Validation

# Start a coding-specific session
create_memory_session(
    problem="Build HTTP client for API integration",
    success_criteria="Efficient, maintainable, using existing libraries",
    constraints="Must handle auth, retries, rate limiting",
    session_type="coding_session"
)

# Discover what packages are already available
discover_packages()

# Check if functionality already exists before coding
prevent_reinvention_check("HTTP client for REST APIs")
# Returns: Found existing APIs: requests.get(), urllib3.request(), etc.

# Validate code before implementation
validate_package_usage("""
def make_request(url):
    import urllib.request
    return urllib.request.urlopen(url).read()
""")
# Returns: Warning - Consider using requests library

# Store proven patterns for reuse
store_codebase_pattern(
    pattern_type="api_usage",
    code_snippet="import requests\nresponse = requests.get(url, headers=headers)",
    description="Standard HTTP GET with auth headers",
    language="python"
)

⚠️ Must start with create_memory_session() before storing anything.

🧩 Session Tools

Core Memory Tools

Tool	Description
`create_memory_session()`	Start a new thinking session (now supports `session_type`)
`store_memory()`	Save insights with tags and confidence (now supports `code_snippet`)
`revise_memory()`	Update previous memories
`create_collection()`	Group insights
`merge_collection()`	Combine collections
`analyze_memories()`	Run quality checks
`export_session_to_file()`	Export full sessions
`export_memories_to_file()`	Export filtered memories
`load_project_context()`	Resume prior sessions
`update_project_index()`	Document team progress

💻 Coding Integration Tools (NEW)

Tool	Description
`discover_packages()`	Auto-scan installed packages and extract APIs
`validate_package_usage()`	Validate code against existing packages
`explore_existing_apis()`	Find existing APIs for needed functionality
`prevent_reinvention_check()`	Comprehensive reinvention prevention warning
`store_codebase_pattern()`	Store code patterns with metadata
`load_codebase_context()`	Load project structure into memory

Enhanced Tools

create_memory_session(): Now accepts session_type parameter for coding_session, debugging_session, architecture_session
store_memory(): Now accepts code_snippet, language, pattern_type parameters for code integration

💻 Coding Session Types & Workflows

Session Types

coding_session: General development work with package discovery and validation
debugging_session: Problem-solving focused with enhanced error pattern storage
architecture_session: System design with emphasis on integration patterns

Validation Workflow

# 1. Start coding session
create_memory_session("Build user service", "Efficient API", session_type="coding_session")

# 2. Discover available packages
discover_packages()
# Scans environment and stores: requests, fastapi, pydantic, etc.

# 3. Check for existing solutions before coding
prevent_reinvention_check("HTTP server framework")
# ⚠️ POTENTIAL REINVENTION DETECTED ⚠️
# Found existing APIs: fastapi.FastAPI(), flask.Flask(), etc.

# 4. Validate specific code patterns
validate_package_usage("""
class CustomHTTPServer:
    def __init__(self, port):
        self.port = port
    def start(self):
        # custom server implementation
        pass
""")
# Warning: Consider using FastAPI or Flask instead

# 5. Store proven patterns for team reuse
store_codebase_pattern(
    pattern_type="api_endpoint",
    code_snippet="""
@app.get("/users/{user_id}")
async def get_user(user_id: int):
    return {"user_id": user_id}
""",
    description="Standard FastAPI endpoint pattern",
    language="python",
    tags="api,fastapi,endpoint"
)

🔍 MCP Resources for Coding

Access coding data through these resources:

Resource	Description
`codebase://packages`	View discovered packages in session
`codebase://patterns`	View stored code patterns
`codebase://validation-checks`	View validation check history

Example Usage

# View discovered packages
# Resource: codebase://packages
{
  "requests": [
    {"signature": "requests.get(url, **kwargs)", "usage_example": "requests.get('https://api.example.com')"},
    {"signature": "requests.post(url, data=None, json=None, **kwargs)", "usage_example": "requests.post(url, json={'key': 'value'})"}
  ],
  "fastapi": [
    {"signature": "fastapi.FastAPI()", "usage_example": "app = FastAPI()"}
  ]
}

# View code patterns  
# Resource: codebase://patterns
[
  {
    "id": "abc123",
    "type": "api_endpoint", 
    "description": "Standard FastAPI endpoint",
    "language": "python",
    "tags": ["api", "fastapi"],
    "code_snippet": "@app.get('/users/{user_id}')\nasync def get_user(user_id: int)..."
  }
]

🏗 Project Structure

memory-bank/
├── thinking_sessions/
├── domain_knowledge/
├── implementation_log/
├── exports/
├── project_knowledge_index.md
└── README.md

Initialize with:

create_project_structure("My Project")

🧑‍🤝‍🧑 Collaboration Patterns

Developer A - Research Phase

create_memory_session("Research auth libs", "Evaluate options", session_type="coding_session")
discover_packages()  # Find available auth libraries
prevent_reinvention_check("JWT authentication")
store_memory("Use FastAPI JWT plugin", code_snippet="from fastapi_jwt import JWT", language="python")
export_session_to_file("thinking_sessions/research_alice.md")

Developer B - Implementation Phase

load_project_context("memory-bank")
create_memory_session("Design auth flow", "Complete plan", session_type="architecture_session")

# Load existing patterns from team
# Resource: codebase://patterns shows Alice's JWT pattern

validate_package_usage("""
def custom_jwt_encode(payload):
    import base64
    return base64.b64encode(json.dumps(payload).encode())
""")
# Warning: Consider using existing JWT libraries

export_memories_to_file("domain_knowledge/auth_decisions.json")

Developer C - Debugging Phase

create_memory_session("Fix auth token expiry", "Resolve production issue", session_type="debugging_session")
explore_existing_apis("JWT token refresh")
store_codebase_pattern("debugging", "Token expiry logs", "Check exp claim in JWT payload")

💥 Error Recovery

try:
    analyze_memories()
except:
    create_memory_session("Recovery", "Rebuild context")

Safe export:

def safe_export(path):
    try:
        export_session_to_file(path)
    except:
        export_session_to_file(path.replace(".md", ".json"))

🧑‍💼 Role-Based Usage

Role	Actions
Lead	`create_project_structure()`, `update_project_index()`, `discover_packages()`
Developer	`create_memory_session()`, `validate_package_usage()`, `store_codebase_pattern()`
New Teammate	`load_project_context()`, `explore_existing_apis()`, `prevent_reinvention_check()`

🗄️ Database Schema Extensions

The coding integration adds 4 new tables to the SQLite database:

New Tables

-- Package APIs discovered in sessions
CREATE TABLE package_apis (
    id TEXT PRIMARY KEY,
    session_id TEXT NOT NULL,
    package_name TEXT NOT NULL,        -- e.g., "requests"
    api_signature TEXT NOT NULL,       -- e.g., "requests.get(url, **kwargs)"
    usage_example TEXT,                -- e.g., "requests.get('https://api.com')"
    documentation TEXT,                -- API documentation excerpt
    discovered_at TIMESTAMP,
    usage_count INTEGER DEFAULT 0
);

-- Code patterns stored for reuse
CREATE TABLE codebase_patterns (
    id TEXT PRIMARY KEY,
    session_id TEXT NOT NULL,
    pattern_type TEXT NOT NULL,        -- 'api_usage', 'integration', 'structure'
    code_snippet TEXT NOT NULL,       -- Actual code
    description TEXT,                  -- Human description
    language TEXT,                     -- 'python', 'javascript', etc.
    file_path TEXT,                    -- Original file path if applicable
    tags_json TEXT DEFAULT '[]',       -- JSON array of tags
    created_at TIMESTAMP,
    updated_at TIMESTAMP
);

-- Coding session metadata
CREATE TABLE coding_sessions (
    session_id TEXT PRIMARY KEY,
    session_type TEXT NOT NULL,       -- 'coding_session', 'debugging_session', 'architecture_session'
    project_path TEXT,               -- Project directory
    language TEXT,                   -- Primary language
    framework TEXT,                  -- Primary framework
    packages_discovered INTEGER,     -- Count of packages found
    patterns_stored INTEGER,         -- Count of patterns stored
    validation_checks INTEGER        -- Count of validations run
);

-- Validation check results  
CREATE TABLE validation_checks (
    id TEXT PRIMARY KEY,
    session_id TEXT NOT NULL,
    check_type TEXT NOT NULL,         -- 'package_usage', 'reinvention_prevention'
    target_code TEXT NOT NULL,       -- Code that was validated
    result TEXT NOT NULL,            -- 'passed', 'failed', 'warning'
    message TEXT,                    -- Human-readable result
    suggestions_json TEXT,           -- JSON array of suggestions
    created_at TIMESTAMP
);

Migration & Compatibility

Automatic Migration: New tables created automatically when first used
Backwards Compatible: Existing sessions continue to work unchanged
Schema Evolution: Database adapts seamlessly to new features
Data Isolation: Coding features are project-specific via session isolation

📘 Best Practices

General Memory Management

Always start with create_memory_session()
Use specific tags for search/export
Run analyze_memories() before final export
Use collections for structure
Track everything under version control

Coding Integration Best Practices

Start coding sessions with package discovery: discover_packages() first
Check for reinvention before coding: Use prevent_reinvention_check() early
Validate code patterns: Run validate_package_usage() before implementation
Store proven patterns: Use store_codebase_pattern() for team knowledge sharing
Load project context: Always load_codebase_context() when joining existing projects
Use appropriate session types:
- coding_session for general development
- debugging_session for problem-solving
- architecture_session for system design

Team Workflow

# 1. Project Lead: Set up discovery
create_memory_session("Project kickoff", "Team alignment", session_type="architecture_session")
discover_packages()  # Establish baseline
load_codebase_context()  # Scan existing code

# 2. Developers: Check before coding
prevent_reinvention_check("user authentication")  # Before starting work
validate_package_usage(proposed_code)  # Before committing

# 3. Knowledge Sharing: Store patterns
store_codebase_pattern("error_handling", error_code, "Team standard error pattern")
export_session_to_file("team_standards.md")

🧩 Claude Desktop Integration

{
  "mcpServers": {
    "memory-bank": {
      "command": "uv",
      "args": ["run", "/path/to/memory-bank-mcp/main.py"]
    }
  }
}

📄 License

MIT License

🆘 Support

Open an issue on GitHub
Read the usage examples above

Available Tools

17 tools

analyze_memoriesD

ParametersJSON Schema

Name	Required	Description	Default
No parameters

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

D1/5.0

Behavior1/5

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

Tool has no description.

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

Conciseness1/5

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

Tool has no description.

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?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

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

Purpose1/5

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

Tool has no description.

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

Usage Guidelines1/5

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

Tool has no description.

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

create_collectionD

ParametersJSON Schema

Name	Required	Description	Default
`name`	Yes
`from_memory`	Yes
`purpose`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

D1/5.0

Behavior1/5

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

Tool has no description.

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

Conciseness1/5

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

Tool has no description.

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?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

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

Purpose1/5

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

Tool has no description.

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

Usage Guidelines1/5

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

Tool has no description.

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

create_memory_sessionD

ParametersJSON Schema

Name	Required	Default
`problem`	Yes
`success_criteria`	Yes
`constraints`	No
`session_type`	No	general

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

D1/5.0

Behavior1/5

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

Tool has no description.

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

Conciseness1/5

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

Tool has no description.

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?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

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

Purpose1/5

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

Tool has no description.

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

Usage Guidelines1/5

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

Tool has no description.

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

create_project_structureD

ParametersJSON Schema

Name	Required	Description	Default
`project_name`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

D1/5.0

Behavior1/5

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

Tool has no description.

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

Conciseness1/5

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

Tool has no description.

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?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

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

Purpose1/5

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

Tool has no description.

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

Usage Guidelines1/5

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

Tool has no description.

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

discover_packagesB

Discover available packages and their APIs

ParametersJSON Schema

Name	Required	Description	Default
`scan_imports`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

B3/5.0

Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It vaguely implies a read operation ('discover'), but doesn't specify if it's safe, requires network access, has rate limits, or what the output entails. This is inadequate for a tool that likely interacts with package systems.

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, clear sentence with no wasted words. It's front-loaded and efficiently conveys the core purpose without unnecessary elaboration, making it easy for an agent to parse 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 has an output schema, the description doesn't need to explain return values. However, with no annotations and a vague purpose, it lacks details on behavior and context. For a discovery tool that might involve scanning or querying, more information on scope and constraints would be helpful.

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 tool has 0 required parameters and 1 optional parameter with 0% schema description coverage. The description doesn't mention parameters at all, which is acceptable here since there are no required parameters, and the optional one is simple (a boolean). This aligns with the baseline for 0 required 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 'Discover available packages and their APIs' states a general purpose but lacks specificity about what 'discover' entails or what scope of packages is involved. It distinguishes from siblings like 'analyze_memories' or 'create_collection' by focusing on packages, but doesn't clarify if this is for local or remote packages, or what 'APIs' specifically refers to.

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. Sibling tools like 'explore_existing_apis' or 'validate_package_usage' might overlap, but the description doesn't mention them or specify contexts like initial setup vs. ongoing discovery. This leaves the agent with minimal direction.

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

explore_existing_apisC

Explore existing APIs that might provide the needed functionality

ParametersJSON Schema

Name	Required	Description	Default
`functionality`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

C2/5.0

Behavior1/5

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. However, it only vaguely describes the action ('explore') without detailing traits like whether it's read-only, how results are returned (e.g., list, details), potential rate limits, or authentication needs. For a tool with no annotation coverage, this is inadequate, failing to inform the agent about critical operational aspects.

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, straightforward sentence that efficiently conveys the core idea without unnecessary words. It is front-loaded with the main action ('Explore existing APIs'), making it easy to parse. However, it could be more structured by explicitly stating the tool's scope or output, but it earns high marks for brevity and clarity within its limited content.

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's complexity (1 parameter, no annotations, but has an output schema), the description is incomplete. It does not explain what 'explore' entails behaviorally, how results are returned (though the output schema might cover this), or usage context. For a tool with no annotations and low schema coverage, more detail is needed to guide the agent effectively, making it minimally adequate at best.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 1 parameter with 0% description coverage, so the schema provides no semantic information. The description mentions 'functionality' implicitly but does not explain what the parameter represents (e.g., a search query, API category) or how to format it. It adds minimal value beyond the schema, insufficient to compensate for the low coverage.

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

Purpose2/5

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

The description states a vague purpose ('Explore existing APIs that might provide the needed functionality') without specifying what 'explore' entails (e.g., search, list, analyze) or what resource is being explored (e.g., APIs in a registry, codebase, or external sources). It distinguishes from siblings like 'discover_packages' or 'validate_package_usage' by focusing on APIs, but the verb is too generic, making it unclear how this differs from similar tools.

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 mention prerequisites (e.g., needing a functionality query), exclusions (e.g., not for creating APIs), or direct alternatives among siblings (e.g., 'discover_packages' for packages instead of APIs). The lack of context leaves the agent guessing about appropriate usage scenarios.

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

export_memories_to_fileD

ParametersJSON Schema

Name	Required	Description	Default
`filename`	Yes
`tags`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

D1/5.0

Behavior1/5

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

Tool has no description.

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

Conciseness1/5

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

Tool has no description.

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?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

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

Purpose1/5

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

Tool has no description.

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

Usage Guidelines1/5

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

Tool has no description.

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

export_session_to_fileD

ParametersJSON Schema

Name	Required	Description	Default
`filename`	Yes
`format`	No		markdown

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

D1/5.0

Behavior1/5

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

Tool has no description.

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

Conciseness1/5

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

Tool has no description.

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?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

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

Purpose1/5

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

Tool has no description.

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

Usage Guidelines1/5

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

Tool has no description.

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

load_codebase_contextC

Load existing codebase structure and patterns into memory

ParametersJSON Schema

Name	Required	Description	Default
`project_path`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

C2.7/5.0

Behavior2/5

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

With no annotations provided, the description carries full burden but offers minimal behavioral insight. It mentions loading 'into memory' but doesn't clarify what this entails operationally—such as whether it's a read-only scan, how it handles large codebases, or what permissions are needed. The description lacks details on side effects, performance, or error handling.

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, efficient sentence with no wasted words. It's front-loaded and appropriately sized for the tool's apparent complexity, making it easy to parse 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 has an output schema (which may cover return values) and no annotations, the description is minimally adequate but incomplete. It states the core action but misses key context like parameter meaning, usage distinctions, and operational behavior, leaving gaps for an AI agent to infer correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description provides no information about the single parameter 'project_path'. It doesn't explain what this path represents, its format, or default behavior, failing to compensate for the schema's lack of descriptions.

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 the tool 'loads existing codebase structure and patterns into memory', which provides a clear verb ('load') and resource ('codebase structure and patterns'). However, it doesn't distinguish itself from the sibling tool 'load_project_context', making the purpose somewhat vague in relation to alternatives.

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 like 'load_project_context' or 'create_project_structure'. There's no mention of prerequisites, timing, or exclusions, leaving usage entirely implied from the name alone.

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

load_project_contextD

ParametersJSON Schema

Name	Required	Description	Default
`project_path`	No		memory-bank

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

D1/5.0

Behavior1/5

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

Tool has no description.

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

Conciseness1/5

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

Tool has no description.

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?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

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

Purpose1/5

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

Tool has no description.

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

Usage Guidelines1/5

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

Tool has no description.

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

merge_collectionD

ParametersJSON Schema

Name	Required	Description	Default
`collection_id`	Yes
`target_memory`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

D1/5.0

Behavior1/5

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

Tool has no description.

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

Conciseness1/5

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

Tool has no description.

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?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

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

Purpose1/5

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

Tool has no description.

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

Usage Guidelines1/5

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

Tool has no description.

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

prevent_reinvention_checkB

Check if functionality might already exist in known packages

ParametersJSON Schema

Name	Required	Description	Default
`functionality_description`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

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 burden of behavioral disclosure. It states the tool checks for existing functionality but doesn't describe how it performs this check (e.g., search methods, data sources), what the output entails, or any limitations (e.g., rate limits, accuracy). This leaves significant gaps in understanding the tool's behavior.

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, efficient sentence that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, making it easy for an agent to quickly grasp the intent, earning a high score for conciseness.

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 moderate complexity (checking functionality in packages), no annotations, and an output schema present (which should cover return values), the description is minimally adequate. It states what the tool does but lacks details on behavior, usage context, or parameter guidance, making it incomplete for optimal agent 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?

The description adds no meaning beyond the input schema, which has 0% description coverage for the single parameter 'functionality_description'. Since there's only one parameter and no schema details, the baseline is 3, as the description doesn't compensate for the lack of schema information but doesn't worsen it either.

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 as checking if functionality might already exist in known packages, using a specific verb ('check') and resource ('functionality in known packages'). However, it doesn't explicitly differentiate from sibling tools like 'discover_packages' or 'explore_existing_apis', which may have overlapping purposes, preventing a score of 5.

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, context, or exclusions, and with siblings like 'discover_packages' and 'explore_existing_apis', there's no indication of how this tool differs in usage, leaving the agent without clear direction.

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

revise_memoryD

ParametersJSON Schema

Name	Required	Description	Default
`memory_id`	Yes
`new_content`	Yes
`confidence`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

D1/5.0

Behavior1/5

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

Tool has no description.

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

Conciseness1/5

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

Tool has no description.

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?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

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

Purpose1/5

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

Tool has no description.

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

Usage Guidelines1/5

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

Tool has no description.

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

store_codebase_patternC

Store a codebase pattern for future reference

ParametersJSON Schema

Name	Required	Description	Default
`pattern_type`	Yes
`code_snippet`	Yes
`description`	No
`language`	No
`file_path`	No
`tags`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

C2.6/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Store' implies a write operation, but the description doesn't address permissions, persistence, side effects, rate limits, or what 'future reference' entails. It lacks critical behavioral details for a tool with 6 parameters.

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, clear sentence with no wasted words. It's appropriately sized for a basic tool definition and front-loaded with the core purpose, making it easy to parse quickly.

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 complexity (6 parameters, 2 required), no annotations, and 0% schema coverage, the description is insufficient. While an output schema exists, the description doesn't address parameter meanings, usage context, or behavioral traits, leaving significant gaps for effective tool invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, meaning none of the 6 parameters are documented in the schema. The description doesn't explain any parameters, such as what 'pattern_type' or 'code_snippet' should contain, or how 'tags' should be formatted. It fails to compensate for the complete lack of schema documentation.

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 the tool's purpose as 'Store a codebase pattern for future reference', which provides a basic verb+resource combination ('store' + 'codebase pattern'). However, it lacks specificity about what constitutes a 'codebase pattern' and doesn't differentiate from sibling tools like 'store_memory' or 'create_collection', making it somewhat 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?

The description provides no guidance on when to use this tool versus alternatives. With sibling tools like 'store_memory' and 'create_collection' that might handle similar data, there's no indication of context, prerequisites, or exclusions for this tool's usage.

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

store_memoryD

ParametersJSON Schema

Name	Required	Description	Default
`content`	Yes
`dependencies`	No
`confidence`	No
`collection_id`	No
`code_snippet`	No
`language`	No
`pattern_type`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

D1/5.0

Behavior1/5

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

Tool has no description.

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

Conciseness1/5

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

Tool has no description.

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?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

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

Purpose1/5

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

Tool has no description.

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

Usage Guidelines1/5

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

Tool has no description.

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

update_project_indexC

Update specific section in project_knowledge_index.md

ParametersJSON Schema

Name	Required	Description	Default
`section`	Yes
`content`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

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 carries the full burden of behavioral disclosure. It states 'Update' which implies a mutation operation, but doesn't specify whether this requires permissions, what happens to existing content, or if changes are reversible. It also doesn't mention side effects, rate limits, or response format, leaving significant gaps in understanding the tool's behavior.

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, efficient sentence that directly states the tool's purpose without any fluff. It's appropriately sized and front-loaded, making it easy to parse quickly. Every word earns its place in conveying the core functionality.

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 has an output schema (which reduces the need to describe return values) but no annotations and poor parameter documentation, the description is incomplete. It covers the basic purpose but lacks behavioral context and parameter details needed for safe and effective use. It's minimally adequate but with clear gaps in a mutation context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, meaning neither parameter has documentation in the schema. The description only mentions 'specific section' and implies content update, but doesn't explain what 'section' refers to (e.g., a heading, an ID, a file path) or the format/constraints for 'content'. This adds minimal value beyond the parameter names, failing to compensate for the schema gap.

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 ('Update') and the resource ('specific section in project_knowledge_index.md'), making the purpose immediately understandable. However, it doesn't differentiate this tool from potential siblings like 'create_project_structure' or 'store_memory', which might also involve knowledge base operations, so it doesn't reach the highest 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, context, or exclusions, leaving the agent to infer usage based on the name alone. This lack of explicit guidance limits its effectiveness in tool selection.

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

validate_package_usageB

Validate if code uses existing packages appropriately

ParametersJSON Schema

Name	Required	Description	Default
`code_snippet`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

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 full burden. It states the tool validates package usage but doesn't disclose behavioral traits: it doesn't specify what 'appropriately' means (e.g., correct imports, version compatibility, best practices), whether it's read-only or has side effects, what the output includes, or any rate limits. For a validation tool with zero annotation coverage, this is a significant gap.

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, efficient sentence: 'Validate if code uses existing packages appropriately'. It's front-loaded with the core purpose, has zero wasted words, and is appropriately sized for the tool's apparent complexity. Every word earns its place.

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 1 parameter, no annotations, but an output schema exists, the description is minimally complete. The output schema likely covers return values, reducing the need for output details in the description. However, for a validation tool, it lacks context on what 'appropriately' entails, error handling, or dependencies, leaving gaps despite the output schema.

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 implies the parameter is code-related ('code uses existing packages'), which aligns with the 'code_snippet' parameter in the schema. However, it doesn't add meaning beyond this basic mapping—no details on format, length, or examples. With 1 parameter and low coverage, the description provides minimal but not sufficient semantic context.

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: 'Validate if code uses existing packages appropriately' - a specific verb (validate) applied to a resource (code usage of packages). It distinguishes itself from siblings like 'discover_packages' or 'prevent_reinvention_check' by focusing on validation rather than discovery or prevention. However, it doesn't explicitly differentiate from all siblings, keeping it at 4 rather than 5.

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., when packages are already known), exclusions (e.g., not for discovering new packages), or direct comparisons to siblings like 'prevent_reinvention_check' or 'discover_packages'. The agent must infer usage from the purpose alone.

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.

17 tool updatesv1.0.0Jan 26, 2026
- First observedanalyze_memories
- First observedcreate_collection
- First observedcreate_memory_session
- First observedcreate_project_structure
- First observeddiscover_packages
- First observedexplore_existing_apis
- First observedexport_memories_to_file
- First observedexport_session_to_file
- First observedload_codebase_context
- First observedload_project_context
- First observedmerge_collection
- First observedprevent_reinvention_check
- First observedrevise_memory
- First observedstore_codebase_pattern
- First observedstore_memory
- First observedupdate_project_index
- First observedvalidate_package_usage

Install Server

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TDQS

C2/5.0

Disambiguation3/5

The tools have overlapping purposes that could cause confusion, such as load_codebase_context and load_project_context, or create_memory_session and create_collection. However, many tools like discover_packages, explore_existing_apis, and prevent_reinvention_check have distinct functions, and descriptions help clarify some overlaps, preventing complete ambiguity.

Naming Consistency4/5

Most tools follow a consistent verb_noun pattern (e.g., analyze_memories, create_collection, export_memories_to_file), with only minor deviations like prevent_reinvention_check and update_project_index. The naming is readable and predictable overall, though not perfectly uniform.

Tool Count3/5

With 17 tools, the count is borderline heavy for a memory or codebase management server, as it might feel overwhelming. However, given the broad scope implied by tools like discover_packages and validate_package_usage, it's not unreasonable, but could benefit from consolidation to improve focus.

Completeness4/5

The tool set covers core memory and project management operations well, including creation, revision, export, and validation. Minor gaps exist, such as no explicit delete or search tools for memories or collections, but agents can likely work around these with existing tools like analyze_memories or merge_collection.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

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