Beamtrace MCP vs Proofsource — MCP Server Comparison | AllMCPs
Side-by-Side Model Context Protocol Comparison
Beamtrace MCP vs Proofsource
In-depth architectural comparison of the Beamtrace MCP and Proofsource MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.
At a Glance & Executive Verdict
Beamtrace MCP
Marketing · Remote HTTP/SSE
Quality: 48/100 (Fair) | Auth: No auth required
Proofsource
Marketing · Remote HTTP/SSE
Quality: 69/100 (Great) | Auth: OAuth 2.0
Verdict Summary: Choose Beamtrace MCP if you need specialized Marketing tools running via a hosted cloud SSE transport. Choose Proofsource if your workspace requires Marketing integration with remote web transport. Both servers can be configured concurrently in your client's mcpServers manifest.
Which MCP Server Should You Choose?
Choose Beamtrace MCP when:
You need dedicated capabilities in the Marketing domain.
You prefer remote streaming HTTP/SSE transport architecture.
Your security boundary fits: No auth required (Free / Open Source).
AI visibility tracking & website fixes for better AI search presence
AI visibility for your brand: which buyer questions name you in ChatGPT, Perplexity, Claude and Google AI Overviews, which competitors AI recommends instead, which sources it cites, and what to fix. Remote server with OAuth sign-in.
Beamtrace MCP is categorized under Marketing and uses a remote streaming HTTP/SSE transport. In contrast, Proofsource belongs to Marketing using remote streaming HTTP/SSE transport. Select Beamtrace MCP when you need capabilities focused on marketing and Proofsource when you require tools for marketing.
The topics and buyer questions Proofsource asks ChatGPT, Perplexity, Claude and Google AI for a brand. Gives the topic_id create_prompt needs.
get_visibility
How often ChatGPT, Perplexity, Claude and Google AI mention the brand when buyers ask about its category. by=engine (default): mention rate, share of voice, average position, citation share and sentiment per engine, with the change from the previous period. by=question: each buyer question's mention rate and rank.
get_competitors
The competitors AI answers name for the brand's buyer questions, per engine, with each one's share of voice against the brand and how it changed over the window.
get_citations
The websites AI answers cite for the brand's buyer questions, ranked by share of citations and by how many answers cite them, and where the brand's own site ranks.
get_changes
What moved over the window: the daily mention-rate trend, how much the engines' answers changed, notable changes on competitors' websites, and new pages about the category.
list_fix_opportunities
The gaps that keep AI engines from citing or recommending the brand, each with its evidence and the suggested fix. Gives the gap_id mark_fix_shipped needs.
get_fix_verification
Each fix marked shipped for the brand with its mention rate before and after and a verdict: moved (beyond normal answer churn), within_noise, or awaiting_samples. Fixes marked "Not a fix" are left out; auto-detected page changes carry source auto_detected.
create_prompt
Adds a buyer question to a topic so Proofsource asks it on every engine in the plan from the next run. Counts toward the plan's question limit. Calling again with the same text returns the existing question, never a duplicate.
mark_fix_shipped
Records that a fix shipped, against a gap_id from list_fix_opportunities or a published content_draft_id, so get_fix_verification can measure whether it moved the brand's mention rate. Calling again with the same fix and description returns the existing record.
add_brand
Start measuring a brand: does ChatGPT, Perplexity, Claude or Google AI mention it, and who does AI recommend instead? Give the brand's website (or its app store, Google Maps, YouTube or Instagram link). Proofsource reads the site, picks the buyer questions, and starts the first scan across the AI engines. Reading the site takes 1-2 minutes; if it is still running, call again with the same link and it picks up the same run, never a second one. Returns the brand_id; then use get_scan_status.
get_scan_status
Progress of a brand's first scan after add_brand: the stage, how many AI answers are in out of how many expected, per engine, and when it should finish. Once complete, use get_visibility for the results.