What can @iflow-mcp/db-mcp-tool do?

postgresql schema introspection and metadata extraction, mysql schema introspection and metadata extraction, firestore collection and document structure discovery, multi-database connection management and routing, sql query execution with result streaming, firestore document querying with field filtering, database schema caching and invalidation

@iflow-mcp/db-mcp-tool

MCP ServerFree

Database Explorer MCP Tool - PostgreSQL, MySQL ve Firestore veritabanları için yönetim aracı

Open Source

/ 100

7 capabilities

Capabilities7 decomposed

postgresql schema introspection and metadata extraction

Medium confidence

Connects to PostgreSQL databases via native libpq protocol or TCP sockets to extract and expose complete schema metadata including tables, columns, indexes, constraints, and relationships. Uses information_schema queries to build a queryable representation of database structure without requiring ORM abstractions, enabling direct schema inspection for code generation or documentation purposes.

Solves for

I need to understand the structure of a PostgreSQL database I'm working withI want to generate TypeScript types or database client code from my schemaI need to document database relationships and constraints programmaticallyI'm building a tool that needs to discover available tables and columns at runtime

Best for

Backend developers building database-aware tooling or code generators

Teams using MCP servers to expose database metadata to Claude or other LLM agents

Database administrators automating schema documentation

Requires

PostgreSQL 9.6 or later

Network access to PostgreSQL host with valid credentials

Node.js 16+ runtime

Limitations

Requires network connectivity to PostgreSQL instance; no offline schema caching

Schema changes are not automatically refreshed — requires explicit reconnection to see updates

Does not expose database-level permissions or role-based access control details

What makes it unique

Implements MCP protocol binding for PostgreSQL schema access, allowing LLM agents to directly query database structure through standardized tool-calling interface rather than requiring custom REST APIs or database client libraries

vs alternatives

Provides schema introspection as an MCP tool callable by Claude, enabling AI agents to autonomously explore and reason about database structure without developer-written query wrappers

mysql schema introspection and metadata extraction

Medium confidence

Connects to MySQL/MariaDB databases via TCP protocol to extract schema metadata including tables, columns, indexes, foreign keys, and constraints using INFORMATION_SCHEMA queries. Exposes database structure through MCP tool interface, enabling programmatic discovery of table relationships and column definitions without ORM dependencies.

Solves for

I need to explore a MySQL database schema programmaticallyI want to generate database client code or migrations from MySQL schemaI'm building an agent that needs to understand available tables and relationshipsI need to validate or document MySQL database structure automatically

Best for

Backend developers working with MySQL/MariaDB databases

Teams deploying MCP servers to expose MySQL metadata to AI agents

Database migration and schema documentation automation

Requires

MySQL 5.7+ or MariaDB 10.2+

Network access to MySQL host with valid user credentials

Node.js 16+ runtime

Limitations

No support for MySQL 5.5 or earlier; requires MySQL 5.7+ for full INFORMATION_SCHEMA support

Schema metadata is read-only; does not support schema modifications through this capability

Does not expose MySQL-specific features like partitioning or storage engine details

What makes it unique

Provides MySQL schema introspection as an MCP tool, allowing Claude and other LLM agents to autonomously query database structure through standardized tool-calling without custom API wrappers

vs alternatives

Simpler integration than building custom REST endpoints for schema discovery; leverages MCP protocol for direct agent access to MySQL metadata

firestore collection and document structure discovery

Medium confidence

Connects to Google Cloud Firestore using service account credentials to enumerate collections, sample documents, and infer document schema structure. Uses Firestore SDK to traverse collection hierarchies and analyze document fields, enabling runtime discovery of data structure without requiring pre-defined schemas or manual documentation.

Solves for

I need to understand the structure of my Firestore database programmaticallyI want to generate TypeScript types from Firestore collectionsI'm building an agent that needs to discover available collections and document fieldsI need to validate or document Firestore data structure automatically

Best for

Firebase/Firestore developers building schema-aware tooling

Teams using MCP servers to expose Firestore structure to AI agents

Full-stack developers automating type generation from Firestore

Requires

Google Cloud project with Firestore enabled

Service account JSON key file with Firestore read permissions

Node.js 16+ runtime

Limitations

Requires Google Cloud service account with Firestore read permissions; no anonymous access

Schema inference based on document sampling — may miss fields present in only some documents

Does not expose Firestore security rules or access control configuration

What makes it unique

Implements MCP tool binding for Firestore schema discovery, enabling LLM agents to explore NoSQL document structure through standardized interface without requiring custom Firebase client code

vs alternatives

Provides Firestore schema introspection as an MCP tool callable by Claude, allowing agents to autonomously discover collection and document structure without developer-written Firestore client wrappers

multi-database connection management and routing

Medium confidence

Manages connection lifecycle and routing across PostgreSQL, MySQL, and Firestore databases through a unified MCP tool interface. Handles credential storage, connection pooling, and request routing to appropriate database driver based on connection type, abstracting database-specific protocol details behind a common tool-calling surface.

Solves for

I need to switch between multiple databases in my agent without reconfiguring toolsI want to manage database credentials securely without hardcoding themI'm building an agent that works with heterogeneous database backendsI need to route schema queries to the correct database based on connection type

Best for

Teams managing multi-database environments (polyglot persistence)

Developers building database-agnostic agents or tools

Organizations with mixed SQL and NoSQL database stacks

Requires

Node.js 16+ runtime

Environment variables or config file with database credentials

Network access to all configured database instances

Limitations

No built-in connection pooling — each query may create new connections

Credential management relies on environment variables or config files; no vault integration

No automatic failover or connection retry logic

What makes it unique

Provides unified MCP tool interface for managing connections to heterogeneous databases (SQL and NoSQL), abstracting protocol differences and enabling single agent to query multiple database types

vs alternatives

Simpler than building separate MCP tools for each database type; unified routing layer reduces agent configuration complexity

sql query execution with result streaming

Medium confidence

Executes arbitrary SQL queries against PostgreSQL and MySQL databases through MCP tool interface, returning results as structured JSON with column metadata. Implements query result streaming for large result sets, handling pagination and memory-efficient result buffering to prevent agent context overflow.

Solves for

I want my agent to execute custom SQL queries against the databaseI need to fetch data based on dynamic conditions determined by the agentI'm building a tool that requires ad-hoc database queries beyond schema discoveryI need to handle large query results without overwhelming agent context

Best for

Developers building database-aware agents that need flexible querying

Teams using agents for data analysis or reporting

Applications requiring dynamic SQL generation based on user intent

Requires

PostgreSQL 9.6+ or MySQL 5.7+

Database user with SELECT (and potentially INSERT/UPDATE/DELETE) permissions

Node.js 16+ runtime

Limitations

No built-in SQL injection prevention — requires agent to sanitize queries or use parameterized queries

Query timeout not configurable; may fail on long-running analytical queries

No support for transactions or multi-statement queries

What makes it unique

Exposes SQL query execution as an MCP tool with result streaming, enabling LLM agents to execute dynamic queries while managing memory through pagination rather than loading entire result sets into context

vs alternatives

Safer than giving agents direct database access; MCP tool interface provides audit trail and allows for query validation/filtering before execution

firestore document querying with field filtering

Medium confidence

Executes Firestore queries against collections using field-based filtering, ordering, and pagination through MCP tool interface. Translates filter conditions into Firestore SDK query API calls, returning documents as JSON with automatic type inference. Supports compound filters and ordering without requiring agents to understand Firestore query syntax.

Solves for

I want my agent to query Firestore collections with dynamic filtersI need to fetch documents matching specific field conditionsI'm building an agent that needs to search Firestore without writing raw Firestore queriesI need to handle large Firestore collections with pagination

Best for

Firebase developers building Firestore-aware agents

Teams using agents for Firestore data retrieval and analysis

Applications requiring dynamic Firestore queries based on agent reasoning

Requires

Google Cloud project with Firestore enabled

Service account credentials with Firestore read permissions

Node.js 16+ runtime

Limitations

Limited to simple field filters and ordering; no full-text search or complex aggregations

Firestore composite index requirements not automatically handled; may fail on complex multi-field queries

No support for transactions or atomic multi-document operations

What makes it unique

Provides Firestore querying as an MCP tool with automatic filter translation, enabling agents to query NoSQL documents without understanding Firestore SDK syntax or composite index requirements

vs alternatives

Abstracts Firestore query complexity; agents can express queries in natural filter conditions rather than learning Firestore SDK API

database schema caching and invalidation

Medium confidence

Caches schema metadata from PostgreSQL, MySQL, and Firestore in memory with configurable TTL and manual invalidation triggers. Reduces repeated schema queries to databases, improving agent response latency for repeated schema introspection. Implements cache invalidation hooks for schema change detection or explicit refresh requests.

Solves for

I want to reduce latency for repeated schema queries in my agentI need to cache database structure to avoid repeated network callsI want to invalidate schema cache when the database is modifiedI'm building an agent that frequently references database structure

Best for

Agents with frequent schema introspection needs

High-latency database connections where caching provides significant speedup

Applications with stable schemas that rarely change

Requires

Node.js 16+ runtime

Sufficient memory for schema metadata (typically <10MB for most databases)

Limitations

In-memory cache only; lost on process restart — no persistent cache store

No automatic schema change detection; requires explicit invalidation

Cache invalidation is manual or time-based; no event-driven updates

What makes it unique

Implements configurable in-memory schema caching with TTL and manual invalidation, reducing repeated database queries for schema introspection in agent loops

vs alternatives

Faster than repeated schema queries for agents with frequent schema references; simpler than external cache systems but limited to single-process deployments

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

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database schema introspection and metadata exposure

1 shared capability

Best For

✓Backend developers building database-aware tooling or code generators
✓Teams using MCP servers to expose database metadata to Claude or other LLM agents
✓Database administrators automating schema documentation
✓Backend developers working with MySQL/MariaDB databases
✓Teams deploying MCP servers to expose MySQL metadata to AI agents
✓Database migration and schema documentation automation
✓Firebase/Firestore developers building schema-aware tooling
✓Teams using MCP servers to expose Firestore structure to AI agents

Known Limitations

⚠Requires network connectivity to PostgreSQL instance; no offline schema caching
⚠Schema changes are not automatically refreshed — requires explicit reconnection to see updates
⚠Does not expose database-level permissions or role-based access control details
⚠Limited to PostgreSQL 9.6+ for full information_schema compatibility
⚠No support for MySQL 5.5 or earlier; requires MySQL 5.7+ for full INFORMATION_SCHEMA support
⚠Schema metadata is read-only; does not support schema modifications through this capability

Requirements

PostgreSQL 9.6 or laterNetwork access to PostgreSQL host with valid credentialsNode.js 16+ runtimeMySQL 5.7+ or MariaDB 10.2+Network access to MySQL host with valid user credentialsGoogle Cloud project with Firestore enabledService account JSON key file with Firestore read permissions@google-cloud/firestore SDK

Input / Output

Accepts: connection string (postgresql://user:pass@host:port/dbname), host, port, username, password, database name as separate parameters, connection string (mysql://user:pass@host:port/dbname), host, port, username, password, database name parameters, service account JSON credentials, Firestore project ID, collection path (e.g., 'users', 'users/userId/posts'), database connection configuration (type, host, credentials), database identifier or alias for routing, SQL query string, query parameters (for parameterized queries), result limit and offset for pagination, collection path, filter conditions (field, operator, value), ordering specification (field, direction), limit and offset for pagination, cache TTL configuration (milliseconds), invalidation trigger (manual or time-based)

Produces: JSON schema representation, structured metadata objects, table/column definitions, table and column metadata objects, relationship and constraint definitions, JSON schema representation of collections, document field definitions and inferred types, collection hierarchy structure, connection status, routed query results from appropriate database, JSON array of result rows, column metadata (names, types), row count and pagination info, JSON array of matching documents, document IDs and metadata, total match count (if available), cached schema metadata, cache hit/miss statistics

UnfragileRank

Adoption15%(30% weight)

Quality16%(25% weight)

Ecosystem30%(25% weight)

Match Graph10%(15% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: MCP Server

7 capabilities

Visit @iflow-mcp/db-mcp-tool→

Package Details

npm

Registry

1.0.0

Version

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About

Database Explorer MCP Tool - PostgreSQL, MySQL ve Firestore veritabanları için yönetim aracı

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Capabilities7 decomposed

postgresql schema introspection and metadata extraction

Medium confidence

Solves for

Best for

Backend developers building database-aware tooling or code generators

Teams using MCP servers to expose database metadata to Claude or other LLM agents

Database administrators automating schema documentation

Requires

PostgreSQL 9.6 or later

Network access to PostgreSQL host with valid credentials

Node.js 16+ runtime

Limitations

Requires network connectivity to PostgreSQL instance; no offline schema caching

Schema changes are not automatically refreshed — requires explicit reconnection to see updates

Does not expose database-level permissions or role-based access control details

What makes it unique

vs alternatives

Provides schema introspection as an MCP tool callable by Claude, enabling AI agents to autonomously explore and reason about database structure without developer-written query wrappers

mysql schema introspection and metadata extraction

Medium confidence

Solves for

Best for

Backend developers working with MySQL/MariaDB databases

Teams deploying MCP servers to expose MySQL metadata to AI agents

Database migration and schema documentation automation

Requires

MySQL 5.7+ or MariaDB 10.2+

Network access to MySQL host with valid user credentials

Node.js 16+ runtime

Limitations

No support for MySQL 5.5 or earlier; requires MySQL 5.7+ for full INFORMATION_SCHEMA support

Schema metadata is read-only; does not support schema modifications through this capability

Does not expose MySQL-specific features like partitioning or storage engine details

What makes it unique

Provides MySQL schema introspection as an MCP tool, allowing Claude and other LLM agents to autonomously query database structure through standardized tool-calling without custom API wrappers

vs alternatives

Simpler integration than building custom REST endpoints for schema discovery; leverages MCP protocol for direct agent access to MySQL metadata

firestore collection and document structure discovery

Medium confidence

Solves for

Best for

Firebase/Firestore developers building schema-aware tooling

Teams using MCP servers to expose Firestore structure to AI agents

Full-stack developers automating type generation from Firestore

Requires

Google Cloud project with Firestore enabled

Service account JSON key file with Firestore read permissions

Node.js 16+ runtime

Limitations

Requires Google Cloud service account with Firestore read permissions; no anonymous access

Schema inference based on document sampling — may miss fields present in only some documents

Does not expose Firestore security rules or access control configuration

What makes it unique

Implements MCP tool binding for Firestore schema discovery, enabling LLM agents to explore NoSQL document structure through standardized interface without requiring custom Firebase client code

vs alternatives

multi-database connection management and routing

Medium confidence

Solves for

Best for

Teams managing multi-database environments (polyglot persistence)

Developers building database-agnostic agents or tools

Organizations with mixed SQL and NoSQL database stacks

Requires

Node.js 16+ runtime

Environment variables or config file with database credentials

Network access to all configured database instances

Limitations

No built-in connection pooling — each query may create new connections

Credential management relies on environment variables or config files; no vault integration

No automatic failover or connection retry logic

What makes it unique

Provides unified MCP tool interface for managing connections to heterogeneous databases (SQL and NoSQL), abstracting protocol differences and enabling single agent to query multiple database types

vs alternatives

Simpler than building separate MCP tools for each database type; unified routing layer reduces agent configuration complexity

sql query execution with result streaming

Medium confidence

Solves for

Best for

Developers building database-aware agents that need flexible querying

Teams using agents for data analysis or reporting

Applications requiring dynamic SQL generation based on user intent

Requires

PostgreSQL 9.6+ or MySQL 5.7+

Database user with SELECT (and potentially INSERT/UPDATE/DELETE) permissions

Node.js 16+ runtime

Limitations

No built-in SQL injection prevention — requires agent to sanitize queries or use parameterized queries

Query timeout not configurable; may fail on long-running analytical queries

No support for transactions or multi-statement queries

What makes it unique

vs alternatives

Safer than giving agents direct database access; MCP tool interface provides audit trail and allows for query validation/filtering before execution

firestore document querying with field filtering

Medium confidence

Solves for

Best for

Firebase developers building Firestore-aware agents

Teams using agents for Firestore data retrieval and analysis

Applications requiring dynamic Firestore queries based on agent reasoning

Requires

Google Cloud project with Firestore enabled

Service account credentials with Firestore read permissions

Node.js 16+ runtime

Limitations

Limited to simple field filters and ordering; no full-text search or complex aggregations

Firestore composite index requirements not automatically handled; may fail on complex multi-field queries

No support for transactions or atomic multi-document operations

What makes it unique

Provides Firestore querying as an MCP tool with automatic filter translation, enabling agents to query NoSQL documents without understanding Firestore SDK syntax or composite index requirements

vs alternatives

Abstracts Firestore query complexity; agents can express queries in natural filter conditions rather than learning Firestore SDK API

database schema caching and invalidation

Medium confidence

Solves for

Best for

Agents with frequent schema introspection needs

High-latency database connections where caching provides significant speedup

Applications with stable schemas that rarely change

Requires

Node.js 16+ runtime

Sufficient memory for schema metadata (typically <10MB for most databases)

Limitations

In-memory cache only; lost on process restart — no persistent cache store

No automatic schema change detection; requires explicit invalidation

Cache invalidation is manual or time-based; no event-driven updates

What makes it unique

Implements configurable in-memory schema caching with TTL and manual invalidation, reducing repeated database queries for schema introspection in agent loops

vs alternatives

Faster than repeated schema queries for agents with frequent schema references; simpler than external cache systems but limited to single-process deployments

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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@iflow-mcp/db-mcp-tool

Capabilities7 decomposed

postgresql schema introspection and metadata extraction

mysql schema introspection and metadata extraction

firestore collection and document structure discovery

multi-database connection management and routing

sql query execution with result streaming

firestore document querying with field filtering

database schema caching and invalidation

Related Artifactssharing capabilities

libSQL by xexr

WhoDB

PostgreSQL MCP Server

supabase-mcp-server

Dbsensei

Prisma MCP Server

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

Categories

Alternatives to @iflow-mcp/db-mcp-tool

Are you the builder of @iflow-mcp/db-mcp-tool?

Get the weekly brief

Data Sources

@iflow-mcp/db-mcp-tool

Capabilities7 decomposed

postgresql schema introspection and metadata extraction

mysql schema introspection and metadata extraction

firestore collection and document structure discovery

multi-database connection management and routing

sql query execution with result streaming

firestore document querying with field filtering

database schema caching and invalidation

Related Artifactssharing capabilities

libSQL by xexr

WhoDB

PostgreSQL MCP Server

supabase-mcp-server

Dbsensei

Prisma MCP Server

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

Categories

Alternatives to @iflow-mcp/db-mcp-tool

Are you the builder of @iflow-mcp/db-mcp-tool?

Get the weekly brief

Data Sources