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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Fetches live search engine results pages (SERPs) from Google, Bing, and other search engines through FetchSERP's cloud API infrastructure, parsing structured results including organic rankings, paid ads, featured snippets, and knowledge panels. The MCP server wraps the FetchSERP REST API endpoints, translating tool calls into HTTP requests and normalizing heterogeneous SERP formats into consistent JSON structures for downstream processing.
Unique: Exposes FetchSERP's managed cloud SERP infrastructure as MCP tools, eliminating need for agents to manage their own scraping infrastructure or deal with IP rotation and bot detection; normalizes results across heterogeneous search engines into a unified schema
vs alternatives: Simpler than building custom scrapers or managing Selenium/Puppeteer infrastructure, and more cost-effective than enterprise SERP APIs for agents that need occasional search context rather than continuous monitoring
Analyzes keyword metrics including search volume, competition level, cost-per-click (CPC), and trend data by querying FetchSERP's keyword research database. The MCP server translates keyword queries into API calls that return aggregated search demand signals, enabling agents to identify high-value keywords and understand search intent distribution without maintaining their own keyword databases.
Unique: Integrates keyword research as a native MCP tool, allowing agents to dynamically discover keywords during content planning rather than requiring pre-computed keyword lists; aggregates data from multiple sources to provide more robust estimates than single-source APIs
vs alternatives: More accessible than SEMrush/Ahrefs APIs for agents that need occasional keyword lookups, and provides real-time integration vs. static keyword databases
Retrieves backlink profiles, domain authority metrics, and link quality indicators for any domain through FetchSERP's link intelligence API. The server translates domain analysis requests into API calls that return structured backlink data including referring domains, anchor text, link type (dofollow/nofollow), and domain authority scores, enabling agents to assess domain credibility and competitive link profiles.
Unique: Exposes link intelligence as a native MCP tool, allowing agents to dynamically assess domain credibility and competitive positioning without external tools; integrates multiple link quality signals (anchor text, link type, domain authority) into a single API response
vs alternatives: More cost-effective than Ahrefs/Moz APIs for agents that need occasional backlink lookups, and provides structured data suitable for agent decision-making vs. UI-focused tools
Performs automated technical SEO audits by crawling websites and analyzing on-page factors including meta tags, heading structure, internal linking, page speed metrics, mobile-friendliness, and structured data markup. The MCP server translates audit requests into FetchSERP API calls that return detailed crawl reports with actionable issues and recommendations, enabling agents to identify technical barriers to search visibility.
Unique: Integrates website crawling and technical analysis as a native MCP tool, allowing agents to perform on-demand audits without managing separate crawling infrastructure; combines multiple technical signals (meta tags, schema, speed, mobile) into a single structured report
vs alternatives: Simpler than managing Screaming Frog or Sitebulb for agents that need programmatic audits, and provides agent-friendly structured output vs. UI-focused tools
Monitors how specific content ranks for target keywords and tracks which SERP features appear (featured snippets, knowledge panels, local packs, image carousels). The MCP server queries FetchSERP's SERP tracking API to return position history, SERP feature presence, and visibility metrics, enabling agents to understand content performance and optimize for featured snippet opportunities.
Unique: Combines rank tracking with SERP feature detection in a single MCP tool, allowing agents to optimize content for specific SERP features (snippets, panels) rather than just position; provides structured feature data suitable for automated optimization workflows
vs alternatives: More feature-rich than basic rank tracking APIs, and provides agent-friendly structured data for automated decision-making vs. manual monitoring tools
Implements the Model Context Protocol (MCP) server specification, exposing FetchSERP capabilities as standardized tools with JSON schema definitions. The server registers tool handlers that translate MCP tool calls into FetchSERP API requests, handle response parsing, and return results in MCP-compatible formats, enabling any MCP-compatible LLM client (Claude, etc.) to invoke SEO functions natively.
Unique: Implements MCP server specification for FetchSERP, providing standardized tool schemas and request/response handling that works with any MCP-compatible client; abstracts FetchSERP API complexity behind MCP's uniform interface
vs alternatives: More standardized than custom API wrappers, and enables tool reuse across multiple LLM providers that support MCP vs. provider-specific integrations
Analyzes multiple competitors' SERP presence for the same keywords, comparing their rankings, featured snippets, paid ads, and content strategies. The MCP server aggregates SERP data for multiple domains and keywords, returning comparative metrics that enable agents to understand competitive positioning and identify market gaps or opportunities.
Unique: Aggregates SERP data across multiple competitors in a single tool call, enabling agents to perform comparative analysis without orchestrating multiple API calls; returns structured competitive positioning data suitable for automated strategy generation
vs alternatives: More efficient than manual SERP checking or building custom comparison logic, and provides agent-friendly structured data for automated competitive intelligence
Analyzes local search results including Google Business Profile (GBP) listings, local pack rankings, reviews, and location-specific SERP features. The MCP server queries FetchSERP's local SEO API to return local ranking data, GBP information, and local SERP features, enabling agents to optimize for location-based search visibility.
Unique: Integrates local SERP analysis with GBP data in a single tool, enabling agents to optimize for local search without managing separate local and GBP APIs; provides location-aware SERP features suitable for multi-location optimization
vs alternatives: More comprehensive than basic local rank tracking, and provides structured GBP data suitable for automated local SEO workflows
+1 more capabilities
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs FetchSERP at 22/100.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
+1 more capabilities