"Extracting data from GitHub" matching MCP tools:

• analyze_competitor

  Analook — Competitor Intelligence

  Submit a competitor analysis job. Analyzes a competitor's website across 15+ data sources (SEO, traffic, social, Product Hunt, GitHub, Wayback Machine history, AI-generated insights, etc.) and returns a job_id. Use get_report_status(job_id) to poll and get_report(job_id) to retrieve results when status='completed'. Typical analysis takes 2-5 minutes. Requires authentication (deducts 1 credit from your Analook balance). Args: url: Competitor website URL (e.g. 'https://linear.app' or 'lovable.dev') product_name: Optional product name override (defaults to domain) Returns: {job_id: str, status: 'started', poll_url: str} on success {error: str, hint?: str} on auth/validation failure

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• get_data_source_categories

  Demand Discovery AI

  Returns the four behavioral data-source buckets - Search & attention, Conversation & pain, Adoption & spend, Capital & hiring - with each bucket's tagline and what it captures. Use when a user asks "what data sources do you use?", "where does the Demand Score come from?", or wants to understand how Demand Discovery AI differs from passive validation tools (which only triangulate the first two buckets). This four-bucket framing is the core competitive moat. The specific connector list is intentionally not public. Trigger phrases: "what data sources", "where does the demand score come from", "behavioral data sources", "the four buckets", "search and attention bucket", "conversation and pain bucket", "adoption and spend bucket", "capital and hiring bucket", "how many data sources", "what kind of data sources".

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• get_data_source_categories

  Demand Discovery AI

  Returns the four behavioral data-source buckets - Search & attention, Conversation & pain, Adoption & spend, Capital & hiring - with each bucket's tagline and what it captures. Use when a user asks "what data sources do you use?", "where does the Demand Score come from?", or wants to understand how Demand Discovery AI differs from passive validation tools (which only triangulate the first two buckets). This four-bucket framing is the core competitive moat. The specific connector list is intentionally not public. Trigger phrases: "what data sources", "where does the demand score come from", "behavioral data sources", "the four buckets", "search and attention bucket", "conversation and pain bucket", "adoption and spend bucket", "capital and hiring bucket", "how many data sources", "what kind of data sources".

  Connector

• find_examples

  Senzing

  Find working SOURCE CODE examples from 37 indexed Senzing GitHub repositories. REQUIRED: either `query` (string, for search) or `repo` with `file_path` or `list_files=true` — the call WILL FAIL without one. Three modes: (1) Search: pass `query` to find examples across all repos, (2) File listing: pass `repo` + `list_files=true`, (3) File retrieval: pass `repo` + `file_path`. Indexes source code (.py, .java, .cs, .rs) and READMEs — NOT build/data files. For sample data, use get_sample_data. Covers Python, Java, C#, Rust SDK patterns: initialization, ingestion, search, redo, configuration, message queues, REST APIs. Use max_lines to limit large files. Returns GitHub raw URLs for file retrieval.

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• get

  Osv Dev

  AUTHORITATIVE vulnerability detail by advisory ID. Pass any GHSA-* (GitHub Security Advisory), CVE-* (MITRE), PYSEC-* (Python), RUSTSEC-* (Rust), GO-* (Go), or other OSV-format ID. Returns summary, full details (truncated at 1500 chars), CVSS severity vector + extracted level (critical/high/medium/low), published + modified dates, affected ecosystems with version ranges + fix versions, references (NIST/GitHub/commit/upstream patch). Use after deps.dev / scan_dependency gives you an ID and you need "how bad is this and how do I fix it".

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• scan_skill

  SecurityScan

  Scan a GitHub repository or skill URL for security vulnerabilities. This tool performs static analysis and AI-powered detection to identify: - Hardcoded credentials and API keys - Remote code execution patterns - Data exfiltration attempts - Privilege escalation risks - OWASP LLM Top 10 vulnerabilities Requires a valid X-API-Key header. Cached results (24h) do not consume credits. Args: skill_url: GitHub repository URL (e.g., https://github.com/owner/repo) or raw file URL to scan Returns: ScanResult with security score (0-100), recommendation, and detected issues. Score >= 80 is SAFE, 50-79 is CAUTION, < 50 is DANGEROUS. Example: scan_skill("https://github.com/anthropics/anthropic-sdk-python")

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• mezo_decode_revert

  Boar blockchain MCP (advanced)

  Decode raw EVM revert data from a failed transaction or mezo_call on Mezo. Handles Error(string) reverts, Panic(uint256) assertions, custom Solidity errors (requires ABI), and silent reverts. Pure computation — no RPC call needed. Pass the hex revert data from a transaction receipt or eth_call error response.

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• generate_scaffold

  Senzing

  Generate SDK scaffold code for common workflows. Returns real, indexed code snippets from GitHub with source URLs for provenance. Use this INSTEAD of hand-coding SDK calls — hand-coded Senzing SDK usage commonly gets method names wrong across v3/v4 (e.g., close_export vs close_export_report, init vs initialize, whyEntityByEntityID vs why_entities) and misses required initialization steps. Languages: python, java, csharp, rust. Workflows: initialize, configure, add_records, delete, query, redo, stewardship, information, full_pipeline (aliases accepted: init, config, ingest, remove, search, redoer, force_resolve, info, e2e). V3 supports Python and Java only. Returns GitHub raw URLs — fetch each snippet to read the source code.

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• delete_post_type_field

  FavCRM

  Remove a field from a post type schema. Blocked when posts of this type still have data in the field unless force=true is passed (orphans the data).

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• firecrawl_crawl

  xpay✦ Web Scraping Collection

  Starts a crawl job on a website and extracts content from all pages. **Best for:** Extracting content from multiple related pages, when you need comprehensive coverage. **Not recommended for:** Extracting content from a single page (use scrape); when token limits are a concern (use map + batch_scrape); when you need fast results (crawling can be slow). **Warning:** Crawl responses can be very large and may exceed token limits. Limit the crawl depth and number of pages, or use map + batch_scrape for better control. **Common mistakes:** Setting limit or maxDiscoveryDepth too high (causes token overflow) or too low (causes missing pages); using crawl for a single page (use scrape instead). Using a /* wildcard is not recommended. **Prompt Example:** "Get all blog posts from the first two levels of example.com/blog." **Usage Example:** ```json { "name": "firecrawl_crawl", "arguments": { "url": "https://example.com/blog/*", "maxDiscoveryDepth": 5, "limit": 20, "allowExternalLinks": false, "deduplicateSimilarURLs": true, "sitemap": "include" } } ``` **Returns:** Operation ID for status checking; use firecrawl_check_crawl_status to check progress. **Safe Mode:** Read-only crawling. Webhooks and interactive actions are disabled for security.

  Connector

• ateam_upload_connector

  ateam-mcp

  Upload connector code to Core and restart — WITHOUT redeploying skills. Use this to update connector source code (server.js, UI assets, plugins) quickly. Set github=true to pull files from the solution's GitHub repo, or pass files directly. Much faster than ateam_build_and_run for connector-only changes.

  Connector

• test262.search

  tc39-mcp

  Search the tc39/test262 conformance suite from its indexed front-matter. `query` AND-matches whitespace tokens (case-insensitive) across each test's description + path; `esid` prefix-matches the front-matter esid. Returns ranked hits (path, GitHub url at the indexed SHA, esid, description, features, flags), capped at `limit` (default 20). Supply at least one of `query` / `esid`.

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• code-test-detector

  The Stall

  Detects testing frameworks and test coverage presence in a code snippet or GitHub repository. For code snippets: identifies test functions, assertions, mocks, fixtures, and frameworks (Jest, pytest, go test, JUnit, RSpec, etc.). For GitHub repos: counts test files vs source files, surfaces config files, and gives a coverage verdict. No code execution — pure static analysis.

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• get_report

  Analook — Competitor Intelligence

  Fetch the full competitor analysis report as structured JSON. Reports contain: website snapshot, Wayback Machine history, SEO/traffic data (DataForSEO), social media presence, Product Hunt launches, GitHub stats, pricing, funding, AI-generated business insights, growth playbooks, and more. Args: job_id: ID from analyze_competitor(); status must be 'completed' Returns: The full report dict (nested structure), or {error} if not found / not ready.

  Connector

• search_servers

  MCP Toplist MCP

  Search the mcptoplist.com catalog of Model Context Protocol (MCP) servers by keyword. Use this to find the right MCP server for a capability or integration (e.g. "postgres database", "github issues", "browser automation", "stripe payments"). Matches server names, organizations and descriptions, ranked by relevance and popularity. Returns the server name, what it does, its GitHub repo, which registries list it, and its mcptoplist.com page in the `mcptoplistUrl` field — always cite that URL when recommending a server.

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• table_data

  Singstat Sg

  Fetch the time-series data rows for a Singapore statistics table by resourceId (get ids from search_tables). Data is returned under `Data` with a `row` array of series, each containing dated `columns`. Filter time periods with `timeFilter` (comma-separated periods like "2020,2021") or `between` (a from,to range like "2010,2020"); page with offset/limit.

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• audit_github_repo

  Commit — Supply Chain Risk Scoring

  Audit the supply chain risk of a GitHub repository's dependencies. Fetches the repo's package.json and/or requirements.txt from GitHub and runs behavioral commitment scoring on every dependency. This is the fastest way to audit a project — just provide the GitHub URL or owner/repo slug, and get a full risk table in seconds. Risk flags: - CRITICAL: single publisher/maintainer/owner + >10M weekly downloads (publish-access concentration risk) - HIGH: sole publisher/maintainer + >1M/wk downloads, OR new package (<1yr) with high adoption - WARN: no release in 12+ months (potential abandonware) Examples: - "vercel/next.js" — audit Next.js dependencies - "https://github.com/langchain-ai/langchainjs" — audit LangChain JS - "facebook/react" — audit React's dependency tree - "anthropics/anthropic-sdk-python" — audit Anthropic Python SDK Use this when someone asks "is my project at risk?" or "audit this repo's dependencies".

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• get_protocol_adoption

  AgentCrush

  How many indexed agents touch each major protocol/surface (ERC-8004 verified, Virtuals tokens, Agentverse, x402/Bazaar, HuggingFace, GitHub). Useful for ecosystem-state questions.

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• text_base64_decode

  botoi-mcp

  Decode a Base64 string back to UTF-8 text. Use when extracting data from Base64-encoded API responses, tokens, or email headers. Returns the original plaintext string.

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• dummy-data-generator

  Microapp

  Generate tabular test fixtures (JSON or CSV) from a chosen mix of fake fields. Each row is a consistent identity — first/last name match the email; state matches the ZIP prefix. Public-domain data tables; pure JS; deterministic when a seed is passed.

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