Firecrawl MCP Server ranks higher at 62/100 vs Memory MCP Server at 59/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Memory MCP Server | Firecrawl MCP Server |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 59/100 | 62/100 |
| Adoption | 1 | 1 |
| Quality | 1 |
| 1 |
| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Stores and retrieves entities, relations, and observations in a local JSON file using a graph-based data model. The server implements MCP Tools that allow clients to create entities (with properties), define typed relationships between entities, and record observations tied to entities. Data persists across conversation sessions in a single JSON file, enabling stateful knowledge accumulation without requiring external databases or network calls.
Unique: Official MCP reference implementation using TypeScript SDK with a deliberately simple JSON file backend (not a database) to demonstrate how MCP Tools can expose memory operations. The graph model separates entities (with properties), relations (typed edges), and observations (timestamped facts), allowing LLMs to reason about both structure and temporal context.
vs alternatives: Simpler to deploy and understand than vector-database RAG systems because it uses explicit entity/relation storage instead of embeddings, making the knowledge graph directly inspectable and editable by humans or LLMs.
Exposes memory graph operations as MCP Tools (function-calling interface) that LLM clients can invoke. The server implements Tools following the MCP protocol specification, including tool definitions with JSON schemas, input validation, and structured responses. Each Tool maps to a specific memory operation (create entity, add relation, record observation, query entities) and returns results in a format the LLM can parse and reason about.
Unique: Implements the MCP Tool primitive as defined in the protocol specification, using TypeScript SDK's tool registration API. Each Tool includes a JSON schema describing parameters, enabling LLM clients to understand available operations without hardcoding. The server validates inputs against schemas before execution.
vs alternatives: More transparent than REST API endpoints because Tool schemas are introspectable by the LLM, allowing it to discover and reason about available operations; more structured than free-form function calling because schemas enforce parameter contracts.
Provides a Tool that allows clients to create new entities in the knowledge graph with arbitrary key-value properties. The implementation stores entities with unique IDs, names, and a properties object. Properties can be strings, numbers, or booleans. The Tool validates that entity names are non-empty and returns the created entity with its assigned ID, enabling the LLM to reference the entity in subsequent operations.
Unique: Implements entity creation as a simple MCP Tool with automatic ID generation (likely UUID or sequential), allowing the LLM to create entities without managing ID assignment. Properties are stored as a flat key-value object, keeping the model simple for reference implementation purposes.
vs alternatives: Simpler than schema-based entity systems (like RDF or property graphs with strict typing) because it accepts any properties, making it more flexible for exploratory LLM use cases where entity structure isn't known in advance.
Provides Tools to create directed relationships between entities with a specified relation type (e.g., 'works_on', 'knows', 'parent_of'). Relationships are stored as source entity ID, target entity ID, and relation type string. The implementation allows querying relationships by source entity, target entity, or relation type, returning matching relationships. This enables the LLM to express and retrieve semantic connections between entities.
Unique: Implements relationships as simple typed edges in the knowledge graph, using string relation types rather than a fixed ontology. This allows the LLM to define relationship semantics on-the-fly while keeping the implementation lightweight. The reference design stores relationships in a flat list, making it easy to understand but not optimized for large graphs.
vs alternatives: More flexible than RDF triples because relation types are arbitrary strings rather than URIs, and more explicit than embedding-based similarity because relationships are discrete, queryable facts rather than continuous vectors.
Provides a Tool to record observations (facts or events) associated with one or more entities, with automatic timestamp recording. Observations are stored as text content, associated entity IDs, and a creation timestamp. The implementation allows querying observations by entity, enabling the LLM to retrieve historical facts about entities. This enables the system to track events, notes, or discoveries over time.
Unique: Observations are first-class citizens in the memory model alongside entities and relationships, allowing the LLM to record facts that don't fit neatly into entity properties or relationships. Automatic server-side timestamps provide temporal ordering without requiring the LLM to manage time explicitly.
vs alternatives: More suitable for conversational memory than pure entity-relationship models because observations capture natural language facts and events, while timestamps enable temporal reasoning without requiring explicit time entities.
Provides Tools to query the knowledge graph and retrieve entities, relationships, and observations. The implementation supports querying by entity ID, entity name (substring or exact match), relationship type, or observation content. Results are returned as structured JSON arrays, allowing the LLM to inspect the graph state and make decisions based on current knowledge. Queries are executed by filtering the in-memory graph representation.
Unique: Queries are implemented as simple in-memory filters over the JSON graph structure, making the implementation transparent and easy to understand. The reference design prioritizes clarity over performance, suitable for small-to-medium graphs but not optimized for large-scale deployments.
vs alternatives: More transparent than vector database queries because results are exact matches rather than similarity-based, making it easier for the LLM to reason about what was found and why; simpler to debug than SQL queries because the data model is flat JSON.
Implements the MCP server lifecycle using the TypeScript SDK, including initialization, Tool registration, request handling, and graceful shutdown. The server supports standard MCP transports (stdio, HTTP, SSE) through SDK abstractions. On startup, the server loads the JSON memory file (or creates it if missing), registers all memory Tools with the MCP protocol, and begins accepting requests from MCP clients. The implementation handles connection lifecycle events and ensures the JSON file is persisted after each operation.
Unique: Uses the official MCP TypeScript SDK to implement server lifecycle, abstracting away transport details and protocol handling. The reference implementation demonstrates the minimal boilerplate needed to create an MCP server, making it an educational example for developers learning the SDK.
vs alternatives: Simpler than building an MCP server from scratch using raw JSON-RPC because the SDK handles protocol compliance, transport abstraction, and Tool registration; more maintainable than custom server implementations because it follows official patterns.
Persists the entire knowledge graph to a single JSON file on disk after each operation. The implementation reads the JSON file on server startup, maintains the graph in memory, and writes the entire graph back to the file after create/update operations. The file format is a JSON object containing arrays of entities, relationships, and observations. This approach ensures data survives server restarts but requires the entire graph to be serialized on each write.
Unique: Uses a simple JSON file as the storage backend rather than a database, making the implementation portable and debuggable. The entire graph is serialized on each write, prioritizing simplicity and correctness over performance — suitable for reference implementation but not production use.
vs alternatives: More portable than database-backed persistence because it requires no external services or setup; more inspectable than binary formats because the JSON is human-readable; simpler than transaction-based systems because it avoids complex consistency logic.
+1 more capabilities
Scrapes a single URL and converts HTML content to clean markdown using Firecrawl's content extraction pipeline. The firecrawl_scrape tool accepts a URL and optional parameters (formats, headers, wait time, screenshot capability) and returns structured markdown output with automatic cleanup of boilerplate, navigation, and ads. Implements MCP tool handler pattern that marshals arguments through the @mendable/firecrawl-js client library to Firecrawl's backend processing engine.
Unique: Integrates Firecrawl's proprietary content extraction engine (which uses ML-based boilerplate removal and semantic content identification) through MCP protocol, enabling AI agents to access production-grade web scraping without managing browser automation or parsing logic themselves. The markdown conversion is handled server-side rather than client-side, reducing latency and ensuring consistent output formatting.
vs alternatives: Cleaner markdown output than regex-based scrapers like Cheerio or Puppeteer-only solutions because Firecrawl uses ML models to identify main content; simpler than self-hosted solutions because it's fully managed and requires only an API key.
Scrapes multiple URLs in a single operation using Firecrawl's batch processing pipeline. The firecrawl_batch_scrape tool accepts an array of URLs and shared options, submitting them to Firecrawl's backend which processes them in parallel and returns an array of markdown-converted content objects. Implements batching through the @mendable/firecrawl-js client's batch method, which handles request queuing, parallel execution, and result aggregation without requiring client-side coordination.
Unique: Implements server-side parallel batch processing through Firecrawl's backend rather than client-side loop iteration, reducing network round-trips and enabling true concurrent scraping. The batch operation is atomic from the MCP client perspective — a single tool call returns all results, simplifying agent orchestration logic.
More efficient than sequential scraping loops because Firecrawl handles parallelization server-side; simpler than managing Promise.all() with individual scrape calls because batching is a first-class operation with built-in error handling.
Firecrawl MCP Server scores higher at 62/100 vs Memory MCP Server at 59/100.
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Packages the Firecrawl MCP server as a Docker container with environment-based configuration, enabling deployment to containerized infrastructure (Kubernetes, Docker Compose, cloud platforms). The Dockerfile builds a Node.js runtime with the server code and exposes configuration through environment variables, allowing operators to deploy without modifying code. Supports both cloud and self-hosted Firecrawl instances through configuration.
Unique: Provides production-ready Docker packaging with environment-based configuration, enabling zero-code deployment to containerized infrastructure. The Dockerfile handles Node.js runtime setup and dependency installation, reducing deployment complexity.
vs alternatives: Simpler than manual deployment because Docker handles environment setup; more portable than binary distribution because containers run consistently across platforms.
Registers the Firecrawl MCP server in the Smithery registry, enabling one-click installation and discovery through Smithery's MCP client marketplace. The server is published to Smithery with metadata (description, tags, configuration schema) allowing users to discover and install it without manual setup. Smithery handles server distribution, version management, and client integration.
Unique: Leverages Smithery's MCP server registry to enable one-click installation without manual configuration, reducing friction for end users. Smithery handles server discovery, versioning, and client integration, abstracting deployment complexity.
vs alternatives: More user-friendly than manual installation because Smithery handles discovery and setup; more discoverable than GitHub-only distribution because Smithery provides a centralized marketplace.
Supports connecting to self-hosted Firecrawl instances in addition to Firecrawl's cloud service through configurable API endpoint. The FIRECRAWL_API_URL environment variable allows operators to specify a custom Firecrawl endpoint, enabling deployment scenarios where Firecrawl runs on-premises or in a private cloud. The @mendable/firecrawl-js client library handles endpoint abstraction, routing all API calls to the configured endpoint.
Unique: Enables flexible deployment by supporting both cloud and self-hosted Firecrawl instances through simple endpoint configuration, allowing operators to choose deployment model without code changes. The endpoint abstraction is handled by @mendable/firecrawl-js, making self-hosted support transparent to MCP server code.
vs alternatives: More flexible than cloud-only solutions because self-hosted option is available; simpler than maintaining separate server implementations because endpoint configuration is unified.
Discovers all URLs within a website by crawling from a base URL and building a sitemap-like structure. The firecrawl_map tool accepts a base URL and optional parameters (max depth, include patterns, exclude patterns) and returns a hierarchical array of discovered URLs with metadata about page structure. Uses Firecrawl's crawler to traverse internal links up to specified depth, filtering by inclusion/exclusion patterns, and returns the complete URL graph without fetching full page content.
Unique: Provides lightweight URL discovery without content extraction, allowing agents to plan scraping strategy before committing credits to full content fetches. The depth-based crawling with pattern filtering enables selective discovery — agents can discover only URLs matching specific criteria (e.g., /blog/* paths) without exploring entire site.
vs alternatives: More efficient than scraping every page to build a sitemap because it skips content extraction; more reliable than parsing robots.txt or sitemaps.xml because it performs actual crawling and discovers dynamically-linked content.
Crawls an entire website and extracts content from all discovered pages in a single asynchronous operation. The firecrawl_crawl tool accepts a base URL and options (max pages, allowed domains, exclude patterns, scrape options) and returns a crawl ID for polling. The crawler discovers URLs, extracts markdown content from each page, and stores results server-side. Clients poll firecrawl_crawl_status to retrieve results as they complete, implementing an async job pattern rather than blocking until completion.
Unique: Implements server-side asynchronous crawling with job-based result retrieval, decoupling the crawl initiation from result consumption. The MCP server handles polling coordination through firecrawl_crawl_status, allowing AI agents to initiate long-running crawls and check progress without blocking. Firecrawl's backend manages the entire crawl lifecycle including URL discovery, content extraction, and result storage.
vs alternatives: More scalable than sequential scraping because crawling happens server-side in parallel; simpler than managing Puppeteer/Playwright browser pools because Firecrawl abstracts browser automation and handles rate limiting internally.
Polls the status of an in-progress or completed website crawl and retrieves extracted content. The firecrawl_crawl_status tool accepts a crawl ID and returns current progress (pages crawled, pages remaining, completion percentage), status state (running/completed/failed), and paginated results. Implements polling pattern where clients repeatedly call this tool with the same crawl ID to check progress and incrementally retrieve content as pages are processed, supporting streaming-like result consumption.
Unique: Provides non-blocking status and result retrieval for asynchronous crawls, enabling agents to manage long-running operations without blocking. The polling pattern with pagination allows incremental result consumption — agents can start processing results before the entire crawl completes, reducing end-to-end latency for large crawls.
vs alternatives: More flexible than blocking crawl operations because agents can check progress and retrieve partial results; simpler than webhook-based result delivery because polling requires no external infrastructure setup.
+5 more capabilities