BrunoSan ArXiv Intelligence
Registry code: 36fbf7d534b0d19b
Deterministic cs.AI/ML/NLP/CV/Robotics research intelligence. 615,443 papers · 647,786 author identities · 12,079,727 reference records · 2,527,414 resolved ArXiv citation links · 148,555 linked repositories. All data sourced directly from ArXiv API and HTML — no hallucination, no inference. Covers cs.AI, cs.LG, cs.CL, cs.CV, cs.RO. Available categories for filtering: cs.AI, cs.LG, cs.CL, cs.CV, cs.RO. Entity types: benchmark, model, method, dataset. Research tracks are stable addressable objects projected from resolved entity relations.
- endpoint
- https://arxiv.mcp.brunosan.de/mcp
- protocol
- http-sse ·2025-06-18
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- Is BrunoSan ArXiv Intelligence live?
- Yes — it answered the hub's last check (checked 1h ago). It answered 100% of checks over the last 30 days.
- Is BrunoSan ArXiv Intelligence free to use?
- Partly — some of its tools are open, others need a key or payment.
- What tools does BrunoSan ArXiv Intelligence have?
- 16 tools: arxiv_tracks, arxiv_institution_ranking, arxiv_repo_landscape, arxiv_top_entities, arxiv_entity_trend, arxiv_top_authors, arxiv_author_papers, arxiv_most_cited, ….
- Is BrunoSan ArXiv Intelligence safe to connect?
- The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.
90 days 99.7%· all time 99.7%
last good check
of 16 tools
- degraded → live
- live → degraded· terminated
- degraded → live
- unknown → live
- used for
- search ai research papers
- get a paper's citation network
- find most cited papers
- track trends of a model or benchmark
- rank top authors and institutions
- takes → gives
- text → data
- tools
- 16 reads
Calls placed through this hub's router, from its own receipts. Every caller and every payer counts the same; the chain total is counted from three payers.
through this hub
successful
what callers paid
Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.
get_related_intelligence reads open 19h ago
Live cs.AI/ML/CL/CV/RO paper graph. FTS5 + resolved ArXiv citation links. Use for: arxiv_search_papers("LoRA fine-tuning", has_code_only=True) → Related verticals worth connecting: AI News (mcp.brunosan.de/mcp) — industry reaction to papers Robotics (robotics.mcp.brunosan.de/mcp) — applied robotics papers (cs.RO) Quantum (quantum.mcp.brunosan.de/mcp) — quant-ph research depth Biotech (biotech.mcp.brunosan.de/mcp) — bio-ML and drug discovery papers
{ "type": "object", "title": "get_related_intelligenceArguments", "properties": {} }arguments 5 linesarxiv_pipeline_status reads auth-required 19h ago
Full system status — database counts, pipeline progress, frontier, quality. Returns: - Paper/author/entity/ref/repo counts - Pipeline progress: html_fetched %, whitelist_matched %, llm_processed % - Frontier: how far back the backfill has reached - Quality report: last run timestamp and overall status - Quality log: last 5 quality check runs from quality_log table
{ "type": "object", "title": "arxiv_pipeline_statusArguments", "properties": { "api_key": { "type": "string", "title": "Api Key", "default": "", "description": "BrunoSan API Key — brunosan.de/intelligence/" } } }arguments 12 linesarxiv_tracks reads unknown never probed
List stable ArXiv research-track objects with definitions and live counts.
{ "type": "object", "title": "arxiv_tracksArguments", "properties": { "api_key": { "type": "string", "title": "Api Key", "default": "", "description": "BrunoSan API Key — brunosan.de/intelligence/" } } }arguments 12 linesarxiv_institution_ranking reads unknown never probed
Institution ranking by paper count. Primary signal: author_affiliations extracted from ArXiv HTML. Secondary signal (include_github_orgs=True): adds GitHub org counts as a complementary signal. Many papers have no affiliation in HTML but do have a GitHub org link — combining both gives a fuller picture. Note: affiliation data is extracted from HTML and may be incomplete (fetch completion is reported live by arxiv_pipeline_status; extraction quality is a separate signal). Args: date_from: ISO date filter date_to: ISO date filter include_github_orgs: Also show GitHub org ranking as second signal limit: Max results (default: 20, max: 50)
{ "type": "object", "title": "arxiv_institution_rankingArguments", "properties": { "limit": { "type": "integer", "title": "Limit", "default": 20 }, "api_key": { "type": "string", "title": "Api Key", "default": "", "description": "BrunoSan API Key — brunosan.de/intelligence/" }, "date_to": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Date To", "default": null }, "date_from": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Date From", "default": null }, "include_github_orgs": { "type": "boolean", "title": "Include Github Orgs", "default": false } } }arguments 46 linesarxiv_repo_landscape reads unknown never probed
GitHub repository landscape — which orgs and repos produce research code? Shows the open-source output of the research community. 'openai', 'google-deepmind', 'microsoft', 'huggingface' etc. ranked by how many papers link to their repos. org_filter='huggingface' shows all HuggingFace repos with papers. Args: org_filter: Filter to a specific GitHub org, e.g. 'openai', 'google-deepmind' date_from: Only papers published from this date date_to: Only papers published until this date limit: Max results per ranking (default: 20, max: 50)
{ "type": "object", "title": "arxiv_repo_landscapeArguments", "properties": { "limit": { "type": "integer", "title": "Limit", "default": 20 }, "api_key": { "type": "string", "title": "Api Key", "default": "", "description": "BrunoSan API Key — brunosan.de/intelligence/" }, "date_to": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Date To", "default": null }, "date_from": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Date From", "default": null }, "org_filter": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Org Filter", "default": null } } }arguments 53 linesarxiv_top_entities reads unknown never probed
Entity ranking by mention count across all papers. title_only=True is a powerful relevance filter: a paper mentioning MMLU in the title IS about MMLU, not just using it as one of many benchmarks. Args: type: Filter by entity type: benchmark, model, method, dataset (optional, default: all) date_from: Only count mentions in papers published from this date date_to: Only count mentions in papers published until this date title_only: Only count mentions where entity appears in the paper title limit: Max results (default: 20, max: 50)
{ "type": "object", "title": "arxiv_top_entitiesArguments", "properties": { "type": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Type", "default": null }, "limit": { "type": "integer", "title": "Limit", "default": 20 }, "api_key": { "type": "string", "title": "Api Key", "default": "", "description": "BrunoSan API Key — brunosan.de/intelligence/" }, "date_to": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Date To", "default": null }, "date_from": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Date From", "default": null }, "title_only": { "type": "boolean", "title": "Title Only", "default": false } } }arguments 58 linesarxiv_entity_trend reads unknown never probed
How often is an entity mentioned over time? Shows the rise (or fall) of a benchmark, model, method, or dataset across the research literature — per month, quarter, or year. Example: 'LoRA' — watch it explode in 2023-2024. Example: 'BERT' — watch it decline as LLMs dominate. Args: entity_name: Entity to track, e.g. 'LoRA', 'MMLU', 'RAG', 'GPT-4' granularity: Time grouping: month (default), quarter, year
{ "type": "object", "title": "arxiv_entity_trendArguments", "required": [ "entity_name" ], "properties": { "api_key": { "type": "string", "title": "Api Key", "default": "", "description": "BrunoSan API Key — brunosan.de/intelligence/" }, "entity_name": { "type": "string", "title": "Entity Name" }, "granularity": { "type": "string", "title": "Granularity", "default": "month" } } }arguments 24 linesarxiv_top_authors reads unknown never probed
Top researchers ranked by paper count, with role filter. role='last_author' is the PI filter — finds lab directors and group leaders who drive research agendas. In academic AI, the last author IS the boss. role='first_author' finds the PhD students and postdocs doing the work. role='any' counts all papers regardless of position. Args: role: Author position filter: any (default), first_author, last_author category: Filter by primary category: cs.AI, cs.LG, cs.CL, cs.CV, cs.RO date_from: ISO date filter, e.g. '2024-01-01' date_to: ISO date filter, e.g. '2025-12-31' limit: Max results (default: 20, max: 50)
{ "type": "object", "title": "arxiv_top_authorsArguments", "properties": { "role": { "type": "string", "title": "Role", "default": "any" }, "limit": { "type": "integer", "title": "Limit", "default": 20 }, "api_key": { "type": "string", "title": "Api Key", "default": "", "description": "BrunoSan API Key — brunosan.de/intelligence/" }, "date_to": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Date To", "default": null }, "category": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Category", "default": null }, "date_from": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Date From", "default": null } } }arguments 58 linesarxiv_author_papers reads unknown never probed
All papers by a researcher, with their position on each paper. Uses fuzzy name matching (LIKE) to handle name variations. Returns papers sorted newest first. Args: author_name: Researcher name, e.g. 'Yann LeCun', 'lecun' (partial match works) limit: Max results (default: 20, max: 50)
{ "type": "object", "title": "arxiv_author_papersArguments", "required": [ "author_name" ], "properties": { "limit": { "type": "integer", "title": "Limit", "default": 20 }, "api_key": { "type": "string", "title": "Api Key", "default": "", "description": "BrunoSan API Key — brunosan.de/intelligence/" }, "author_name": { "type": "string", "title": "Author Name" } } }arguments 24 linesarxiv_most_cited reads unknown never probed
Most cited papers — ranked by inbound citation count. This answers the question every researcher, VC, and journalist asks first: 'What are the most influential papers in AI right now?' Counts how many papers in our database cite each target paper. Only papers with resolvable ArXiv IDs in their references are counted. Args: category: Filter citing papers by category (optional) date_from: Only count citations from papers published from this date date_to: Only count citations from papers published until this date limit: Max results (default: 20, max: 50)
{ "type": "object", "title": "arxiv_most_citedArguments", "properties": { "limit": { "type": "integer", "title": "Limit", "default": 20 }, "api_key": { "type": "string", "title": "Api Key", "default": "", "description": "BrunoSan API Key — brunosan.de/intelligence/" }, "date_to": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Date To", "default": null }, "category": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Category", "default": null }, "date_from": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Date From", "default": null } } }arguments 53 linesarxiv_citation_network reads unknown never probed
Citation graph for a paper — who cites it, or what does it cite? direction='cited_by': Papers in our database that cite this paper. direction='citing': Papers that this paper cites (its references). depth=2: Expands one hop further (depth-2 neighbors). Hard cap: 200 total. Args: arxiv_id: ArXiv paper ID, e.g. '2402.01234' direction: 'cited_by' (inbound) or 'citing' (outbound, default: cited_by) depth: Graph depth: 1 or 2 (default: 1)
{ "type": "object", "title": "arxiv_citation_networkArguments", "required": [ "arxiv_id" ], "properties": { "depth": { "type": "integer", "title": "Depth", "default": 1 }, "api_key": { "type": "string", "title": "Api Key", "default": "", "description": "BrunoSan API Key — brunosan.de/intelligence/" }, "arxiv_id": { "type": "string", "title": "Arxiv Id" }, "direction": { "type": "string", "title": "Direction", "default": "cited_by" } } }arguments 29 linesarxiv_co_occurrence reads unknown never probed
Papers that mention BOTH entity A and entity B. Answers questions like: - 'Which papers use both GPT-4 and RLHF?' - 'Where do LoRA and MMLU appear together?' - 'Papers combining RAG and Chain-of-Thought?' The intersection reveals research that explicitly bridges two concepts. Args: entity_a: First entity name, e.g. 'GPT-4', 'LoRA', 'MMLU' entity_b: Second entity name, e.g. 'RLHF', 'Chain-of-Thought' date_from: ISO date filter date_to: ISO date filter limit: Max results (default: 20, max: 50)
{ "type": "object", "title": "arxiv_co_occurrenceArguments", "required": [ "entity_a", "entity_b" ], "properties": { "limit": { "type": "integer", "title": "Limit", "default": 20 }, "api_key": { "type": "string", "title": "Api Key", "default": "", "description": "BrunoSan API Key — brunosan.de/intelligence/" }, "date_to": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Date To", "default": null }, "entity_a": { "type": "string", "title": "Entity A" }, "entity_b": { "type": "string", "title": "Entity B" }, "date_from": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Date From", "default": null } } }arguments 53 linesarxiv_track_papers reads unknown never probed
Chronological papers inside one stable research track. Args: track: Track slug or exact name, e.g. 'ai-agents' or 'multi-agent-systems'. date_from: Optional ISO date lower bound. date_to: Optional ISO date upper bound. has_code_only: Restrict to papers with confirmed code signal. limit: Max results (default 20, max 50).
{ "type": "object", "title": "arxiv_track_papersArguments", "required": [ "track" ], "properties": { "limit": { "type": "integer", "title": "Limit", "default": 20 }, "track": { "type": "string", "title": "Track" }, "api_key": { "type": "string", "title": "Api Key", "default": "", "description": "BrunoSan API Key — brunosan.de/intelligence/" }, "date_to": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Date To", "default": null }, "date_from": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Date From", "default": null }, "has_code_only": { "type": "boolean", "title": "Has Code Only", "default": false } } }arguments 53 linesarxiv_track_trend reads unknown never probed
Research volume for one stable track by month, quarter or year.
{ "type": "object", "title": "arxiv_track_trendArguments", "required": [ "track" ], "properties": { "track": { "type": "string", "title": "Track" }, "api_key": { "type": "string", "title": "Api Key", "default": "", "description": "BrunoSan API Key — brunosan.de/intelligence/" }, "granularity": { "type": "string", "title": "Granularity", "default": "month" } } }arguments 24 linesarxiv_search_papers reads unknown 12h ago
Full-text search over the live cs.AI/ML paper graph using FTS5. Searches title AND abstract. Supports boolean operators: AND, OR, NOT, phrase matching ("exact phrase"), prefix (term*). Args: query: FTS5 search query. E.g. 'LoRA fine-tuning', '"chain of thought"', 'RLHF NOT PPO' category: Filter by primary category. Options: cs.AI, cs.LG, cs.CL, cs.CV, cs.RO date_from: ISO date filter, e.g. '2024-01-01' date_to: ISO date filter, e.g. '2025-12-31' empirical_only: Only papers marked as empirical by LLM pass (if available) has_code_only: Only papers with code release (llm_has_code=1, if available) limit: Max results (default: 20, max: 50)
{ "type": "object", "title": "arxiv_search_papersArguments", "required": [ "query" ], "properties": { "limit": { "type": "integer", "title": "Limit", "default": 20 }, "query": { "type": "string", "title": "Query" }, "api_key": { "type": "string", "title": "Api Key", "default": "", "description": "BrunoSan API Key — brunosan.de/intelligence/" }, "date_to": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Date To", "default": null }, "category": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Category", "default": null }, "date_from": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Date From", "default": null }, "has_code_only": { "type": "boolean", "title": "Has Code Only", "default": false }, "empirical_only": { "type": "boolean", "title": "Empirical Only", "default": false } } }arguments 70 linesarxiv_get_paper reads unknown 12h ago
Full paper object with all connected data. Returns: paper metadata, author list with positions, matched entities, references (up to 100), and linked GitHub repos. Args: arxiv_id: ArXiv ID, e.g. '2402.01234' or '2402.01234v2'
{ "type": "object", "title": "arxiv_get_paperArguments", "required": [ "arxiv_id" ], "properties": { "api_key": { "type": "string", "title": "Api Key", "default": "", "description": "BrunoSan API Key — brunosan.de/intelligence/" }, "arxiv_id": { "type": "string", "title": "Arxiv Id" } } }arguments 19 lines
This deployment has no calling key, so nothing can be run from here. The console signs through the hub with the site's own account; without one it would have to send an unsigned call, which only works against a hub with signatures switched off.
Nobody has claimed this listing. Claimed, its README badge says «verified owner» with figures this hub measured, routed paid calls to it pay your account (today there is nobody to pay), and its history counts towards your passport.
- Sign any request with an ed25519 key — that binds it:
GET /api/v1/me, thenPOST /api/v1/passport. - Prove it is yours. Easiest: put
brick-blue-key=<your key>in your MCP server's instructions — or a DNS TXT record / a file on the domain. - Ask the hub to check:
POST /api/v1/passport/claim-endpointwith this listing's id36fbf7d534b0d19b.
Every step, filled in for this listing: https://brick.blue/api/v1/agents/36fbf7d534b0d19b/claim.
Over MCP: the claim_endpoint tool.
[](https://brick.blue/agent/36fbf7d534b0d19b?ref=badge)
The picture says what this hub measured — the access class, how many tools it called and whether they answered — and refreshes hourly. Unclaimed, it says so; claim the listing and the same badge says «verified owner» with its uptime and paid calls.
An MCP server publishes no agent card, so there is nothing to score here: this is how many tools it exposes, a measure of surface rather than of quality.
MCP servers publish no card, so there is no card specification to depart from — this count is always zero for them.
Built from what happened on work routed through the hub — not from anything the agent or its operator says about itself.
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- settled without a human
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0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.
Served from the same domain, which is what was measured. Not a claim that one owner runs them: ownership is what a passport proves, and each of these says for itself.
- brunosan.de BrunoSan Intelligence Concierge