Citation Intelligence vs PDF Reader MCP
A side-by-side look at the Citation Intelligence and PDF Reader MCP MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Citation Intelligence for search & data extraction work as a local process; choose PDF Reader MCP for search & data extraction as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Citation Intelligence when
- You need dedicated capabilities in the Search & Data Extraction domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: API key required.
- You have the keys it needs:
SERPAPI_KEY, BING_WEBMASTER_API_KEY, GOOGLE_APPLICATION_CREDENTIALS. - You want tools like
citations_check, domain_am_i_cited, signals_ai_overview.
Choose PDF Reader MCP when
- You need dedicated capabilities in the Search & Data Extraction domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: No auth required.
- You want tools like
inspect, outline, read.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Search & Data Extraction | Search & Data Extraction |
| Quality signal | 57/100 · Good | 71/100 · Great |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | API key required | No auth required |
| Pricing | BYOK (pay the provider) | Free |
| Required env vars | SERPAPI_KEYBING_WEBMASTER_API_KEYGOOGLE_APPLICATION_CREDENTIALS | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 9 | 1,041 |
| Directory activity | 2 views 0 installs 0 upvotes | 2 views 0 installs 0 upvotes |
| Tools | 26 | 4 |
| Full listing | View Citation Intelligence | View PDF Reader MCP |
Tools & capabilities
Citation Intelligence tools 26
citations_checkReturn URLs cited by an AI engine (Perplexity, Claude, ChatGPT, Gemini, or Bing) for a query. Use this when an agent or user wants to see what sources an AI search engine grounds answers on. Requires at least one engine API key; auto-picks the first available.domain_am_i_citedCheck whether a domain is cited by an AI engine across a cluster of queries. Returns per-query presence, rank, and a citation-rate summary. Use to measure visibility for a brand, product, or content site in AI search.signals_ai_overviewCheck whether Google shows an AI Overview for a query, and which URLs it cites. Uses SerpAPI (free tier: 100/month). Set SERPAPI_KEY.domain_cited_forList queries that the given domain has been cited for, served from the local cache. Build up a corpus by calling check_citations or am_i_cited first; cited_for queries it without spending API budget.citations_predictScore citation likelihood for a URL from public signals (Wikipedia link presence, schema.org markup, /llms.txt, GitHub and Reddit references, canonical hygiene, HTTPS). No LLM fired - all heuristic. Returns 0-100 score, grade, signal breakdown, and ranked fixes.panel_trackSave, load, or list named query panels. A panel is a persisted set of queries you want to monitor over time (e.g. editorial-watchlist). Use action=save with queries[] to create, action=load to read, action=list to enumerate. Panels live under <config>/panels/<name>.json.panel_runRun a saved panel through am_i_cited and append a timestamped snapshot. Side effects: makes external API calls to the configured AI engine (costs API quota); writes one snapshot file to disk at <config>/snapshots/<panel>/<iso>.json. Requires at least one engine API key (same as am_i_cited). Returns per-query citation presence and a citation_rate summary for the run. Use panel_track to create a panel first; use citations_trend to read the accumulated trend after multiple runs.report_visibilityTurnkey AI visibility report for a domain across a query set. Composes check_citations over every query (or a saved panel) and returns the metrics AI-visibility trackers sell as a dashboard, in one call: mention frequency (citation_rate), share_of_voice vs competitors, average rank when cited, and brand sentiment from the answer text. Side effects: one check_citations call per query (costs API quota for uncached queries; cached queries are free). Returns structured summary + top_domains + per_query, plus a rendered Markdown report (include_markdown=true) suitable for a public page. Provide queries[] or a panel name. Same engine selection as check_citations.citations_trendReport citation rate over time for a panel from stored snapshots. Read-only; cache-only — makes no API calls to any AI engine and costs no API quota. Reads snapshot files from <config>/snapshots/<panel>/. Returns: snapshots[] (one entry per panel_run invocation, each with timestamp and citation_rate), plus per-query deltas (gained/lost/unchanged) comparing first vs last snapshot. Returns an empty series when no snapshots exist yet. No auth required. No rate limits. Use panel_run to accumulate snapshots first; use since to restrict the time window.competitors_compareRun predict_citation on 2-10 URLs and return a side-by-side signal table plus a list of signals where the URLs diverge. Use to compare your URL to top-cited competitors for the same query.signals_wikipediaList Wikipedia articles that reference the given domain. Read-only. One HTTPS GET to the Wikipedia API (en.wikipedia.org/w/api.php?action=query&list=exturlusage). No auth required; no API keys; no rate limits beyond Wikipedia's public API fair-use policy (~1 request/second). Returns article titles and URLs. Wikipedia backlinks are the highest-lift signal for LLM training corpora — a domain cited from Wikipedia is far more likely to appear in AI training data and citation pools. Use lang to query non-English Wikipedias.audit_sitemapFetch a sitemap.xml (or sitemap index) and run predict_citation on every URL. Returns results sorted worst-score-first. Surfaces systemic issues across a whole site in one pass. Zero engine keys needed.- +14 more on the listing page
PDF Reader MCP tools 4
inspectDocument and media evidence when Markdown is not enough. video_timeline (bounded scene/cue timeline) and render_frame (actual decoded frames), and cite_check (quote/location support, not semantic truth), are anymd Pro; PDF operations: inspect (page facts, metadata), render_page (PNG images), extract_regions (crop bounding boxes), ocr_pages / analyze_regions (configured OCR or vision provider), structure (JSON with document map, elements, geometry; profile quality|research adds trust and accessibility reports), compare (page-level diff of sources[0] vs sources[1]).outlineReturn a document heading tree with stable node ids, title paths, page/slide/chapter ranges and Markdown byte ranges. PDF bookmarks or detected headings, Word headings, PowerPoint slides, EPUB chapters and headings, HTML and Markdown. format json (default) or tree. Pass a node id to read to fetch that section.readRead any document as clean Markdown: PDF, Word (DOCX), PowerPoint (PPTX), Excel (XLSX/XLS/ODS), CSV, EPUB, HTML or a web URL, Markdown/text, images (metadata + OCR), audio/video (metadata, chapters, subtitles). Pages/slides/sheets carry <!-- page N --> style markers for citation. Long documents stop at max_tokens (default 20000) and end with a cursor to continue; choose pages with pages: "1-5,8". A directory returns its readable files.searchSearch documents for text: one file, many files, whole directories (recursive, .gitignore aware), or URLs, across every format read supports. Returns each hit as file + page/slide/sheet + a snippet with the match in bold. mode auto (default) finds the exact phrase and falls back to BM25-ranked passages when there is none; literal or ranked force one. Narrow directories with glob, e.g. "*.pdf".
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Citation Intelligence
{
"mcpServers": {
"automatelab-tech-citation-intelligence": {
"command": "npx",
"args": [
"-y",
"@automatelab/citation-intelligence"
],
"env": {
"SERPAPI_KEY": "YOUR_SERPAPI_KEY_HERE",
"BING_WEBMASTER_API_KEY": "YOUR_BING_WEBMASTER_API_KEY_HERE",
"GOOGLE_APPLICATION_CREDENTIALS": "YOUR_GOOGLE_APPLICATION_CREDENTIALS_HERE"
}
}
}
}
PDF Reader MCP
{
"mcpServers": {
"pdf-reader-mcp": {
"command": "npx",
"args": [
"-y",
"@sylphx/anymd"
]
}
}
}
Frequently asked questions
Citation Intelligence is categorized under Search & Data Extraction and uses a local stdio subprocess. PDF Reader MCP belongs to Search & Data Extraction and uses a local stdio subprocess. Pick Citation Intelligence for search & data extraction work and PDF Reader MCP for search & data extraction.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Citation Intelligence is bring-your-own-key: usage is billed by the API provider you supply a key for. Auth: api key required. PDF Reader MCP is listed as free. Auth: no auth required. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add automatelab-tech-citation-intelligence and pdf-reader-mcp as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Citation Intelligence has a quality signal of 57/100 (Good) with 2 views, 0 installs and 9 GitHub stars. PDF Reader MCP has 71/100 (Great) with 2 views, 0 installs and 1,041 GitHub stars.