Pwc Mcp logo

Pwc Mcp

Organization
huggingface
pwc-mcp

Papers With Code MCP tools for searching and reading AI/ML papers, discovering recent and trending research, finding related work and paper lineage, browsing tasks, methods, conferences, organizations, and frameworks, and inspecting benchmark leaderboards with model-size and metric filters through the public Papers With Code catalog. Use whenever the user asks to find papers, survey literature, compare research, inspect an arXiv paper, explore AI/ML taxonomy or conferences, discover benchmarks or state-of-the-art models, or mentions Papers With Code, the PwC MCP server, or paperswithcode.co/mcp.

Overview

Publisherhuggingface
Repositorypwc-cli
Skill namepwc-mcp
Stars
210
Forks
13
Bundled files
17
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 17 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by huggingface on GitHub. Read the source before you install it.

Installation

Install the Pwc Mcp AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/huggingface/pwc-cli/tree/main/mcp_server
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/huggingface/pwc-cli.git /tmp/pwc-cli
mkdir -p .claude/skills
cp -r /tmp/pwc-cli/mcp_server .claude/skills/pwc-mcp
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pwc Mcp in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Pwc Mcp on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Pwc Mcp is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Generated for pwc-mcp v0.2.7 and stock-client MCP protocol 2025-11-25.

The tools query the public Papers With Code catalog anonymously and are read-only. Every tool runs the matching pwc CLI research command, and every research flag of that command is a tool parameter, so results match pwc ... --json. If live tool discovery and this skill disagree, the discovered tool schemas are authoritative.

Every result carries schema_version and data, the complete CLI JSON payload for the call. Typed fields such as items, paper, task, evaluations are projections of data; read data when a projection lacks a detail. Use structured output directly for filtering, joining, or other schema-dependent processing.

When the user identifies an author, prefer repeatable structured list_papers calls with the authors argument. Add search for stated topic terms and explicit order_by: "date_published" and order_direction: "desc" for newest or recent work; the default order is trending. Author references accept an exact normalized name, numeric ID, or @HF_USERNAME; multiple authors use AND semantics.

Publication date ranges are inclusive: use published_after: "YYYY-MM-DD" and published_before: "YYYY-MM-DD" with search_papers or list_papers; the start date must not be later than the end date.

Paper discovery results always include official implementation status and the total linked repository count. Use has_official_implementation: true with search_papers or list_papers to require a catalog-linked official repository; this filter fails closed if unconfirmed.

Use get_benchmark with max_parameters to keep models at or below an inclusive parameter limit. It accepts values such as "500M", "1.5B", "3B", and raw integers. Models without one consistent parameter count are excluded from constrained results. Combine require_metrics, minimum_metrics, maximum_metrics, sort_metric, and pareto to select leaderboard rows by metric; a metric the leaderboard does not report returns the unfiltered leaderboard with a metric_note naming the available metrics.

paper accepts a modern or legacy arXiv ID, a numeric external-paper ID, an arXiv, Hugging Face, or Papers With Code URL, an exact paper title, or the short name before a title's colon ("MuseTalk"). Title matching is case-insensitive but exact; ambiguous titles fail with their matching IDs. When a paper is not indexed on Papers With Code, get_paper_info, read_paper, and get_related_papers return public Hugging Face Papers, arXiv, or Crossref (for DOIs and exact titles) metadata marked in_catalog: false with a note and available_at links; say the paper is not indexed on Papers With Code and link to those sources, never to a Papers With Code paper page. Task, method, conference, organization, framework, and benchmark arguments take an exact name, slug, or ID.

Tools

  • search_papers({"query": QUERY, "mode": "hybrid"|"keyword"|"semantic", "page": PAGE, "limit": LIMIT, "published_after": START_DATE, "published_before": END_DATE, "has_official_implementation": BOOLEAN}) — search papers by title, topic, author, or arXiv ID (pwc search). Omit optional arguments when they are not needed.
  • get_paper_info({"paper": PAPER, "include_resources": BOOLEAN, "repo_limit": LIMIT, "include_evaluations": BOOLEAN}) — show compact paper metadata, official-first code, and the total repository count; opt into capped additional resources (pwc paper info).
  • get_paper_evaluations({"paper": PAPER, "page": PAGE, "limit": LIMIT}) — page through one paper's benchmark evaluations, including protocol, sources, openness, and task-scoped ranks (pwc paper evaluations).
  • read_paper({"paper": PAPER, "cursor": CURSOR}) — read one stored paper Markdown chunk (pwc paper read). If truncated is true, call read_paper again with the same paper and the returned next_cursor; repeat until truncated is false. Treat the cursor as opaque and use it within one hour; omit it (or pass "next" before any read) to start from the beginning.
  • list_papers({"search": SEARCH, "task": TASK, "method": METHOD, "conference": CONFERENCE, "framework": FRAMEWORK, "organization": ORGANIZATION, "authors": [AUTHOR], "published_after": START_DATE, "published_before": END_DATE, "all_versions": BOOLEAN, "order_by": "trending"|"date_published"|"citation_count", "order_direction": "asc"|"desc", "include_resources": BOOLEAN, "has_official_implementation": BOOLEAN, "page": PAGE, "limit": LIMIT}) — list and filter papers by exact catalog associations (pwc paper list). Omit optional arguments when they are not needed.
  • list_recent_papers({"limit": LIMIT}) — list the most recently added papers (pwc paper recent).
  • list_trending_papers({"limit": LIMIT, "max_age_days": DAYS, "min_velocity": VELOCITY}) — list trending papers by repository velocity (pwc paper trending).
  • get_related_papers({"paper": PAPER, "limit": LIMIT}) — list related papers (pwc paper related); limit is at most 20.
  • get_paper_lineage({"paper": PAPER}) — list explicit predecessors and successors (pwc paper lineage list).
  • get_task({"task": TASK}) — inspect one exact task, including its area, hierarchy, sister tasks, ranked benchmarks, common methods, recommended frameworks, and trending papers (pwc task --name).
  • list_tasks({"search": SEARCH, "area": AREA, "level": LEVEL, "visible_only": BOOLEAN, "group_by_area": BOOLEAN, "order_by": "name"|"created_at"|"level"|"paper_count", "order_direction": "asc"|"desc", "page": PAGE, "limit": LIMIT}) — search, list, and filter research tasks, or set group_by_area: true for the complete visible top-level taxonomy without pagination (pwc task list).
  • get_method({"method": METHOD}) — inspect one exact method with its area (pwc method --name).
  • list_methods({"search": SEARCH, "area": AREA, "introduced_year": YEAR, "order_by": "name"|"full_name"|"introduced_year"|"created_at"|"paper_count", "order_direction": "asc"|"desc", "page": PAGE, "limit": LIMIT}) — search, list, and filter research methods (pwc method list).
  • get_conference({"conference": CONFERENCE}) — inspect one exact conference (pwc conference --name).
  • list_conferences({"year": YEAR}) — list conferences with imported papers (pwc conference list).
  • get_organization({"organization": ORGANIZATION}) — inspect one exact research organization (pwc organization --name).
  • list_organizations({"featured_only": BOOLEAN}) — list research organizations (pwc organization list).
  • get_framework({"framework": FRAMEWORK}) — inspect one exact research framework (pwc framework --name).
  • list_frameworks({"domain": DOMAIN, "category": CATEGORY, "platform": PLATFORM}) — list research frameworks (pwc framework list).
  • get_benchmark({"benchmark": BENCHMARK, "page": PAGE, "limit": LIMIT, "is_open": BOOLEAN, "max_parameters": SIZE, "require_metrics": [METRIC], "minimum_metrics": {METRIC: VALUE}, "maximum_metrics": {METRIC: VALUE}, "sort_metric": "METRIC:asc|desc", "pareto": ["METRIC:higher", "METRIC:lower"]}) — inspect one paginated benchmark leaderboard with model-size, metric threshold, sort, and Pareto selection (pwc benchmark --name). matched_count reports rows that satisfied the filters before pagination.
  • list_benchmarks({"search": SEARCH, "task": TASK, "include_descendants": BOOLEAN, "minimum_evaluations": COUNT, "is_open": BOOLEAN, "group_by_area": BOOLEAN, "area": AREA, "benchmarks_per_task": COUNT, "order_by": "trending"|"name"|"full_name"|"created_at"|"paper_count", "order_direction": "asc"|"desc", "page": PAGE, "limit": LIMIT}) — list and filter benchmarks (pwc benchmark list). With task, results are ranked by trend unless order_by is set; group_by_area or area returns top benchmarks under each visible task.

All page numbers start at 1. limit is between 1 and 25 and defaults to the CLI page size capped at 25. Follow next_page when the user asks for more results than one response contains; do not infer that a missing item does not exist until the relevant pages have been checked.

The MCP server does not expose the standalone CLI's paper editing, authentication, skill installation, or version commands, and pwc paper read is served as read_paper chunks rather than one document. Do not invent equivalent tools. Use the separate pwc CLI only when it is available and the user needs one of those capabilities.

Research workflow

  1. Use list_benchmarks({"task": TASK}) to discover active benchmarks, then get_benchmark({"benchmark": NAME}) to inspect a leaderboard. Add max_parameters when model size is part of the request and sort_metric or minimum_metrics when a specific metric matters.
  2. Use get_paper_info({"paper": PAPER}) to inspect promising results. Its response includes official-first code; use get_paper_evaluations to page through its benchmark results.
  3. Use exact list_papers authors, task, method, conference, framework, and organization arguments for known identities or catalog associations. Combine them to require every association; do not substitute a keyword search. Add search for title or abstract topic terms.
  4. Use search_papers for broader discovery, then read_paper for primary evidence. Follow every returned continuation cursor needed for the user's question.
  5. Expand the literature with get_related_papers and use get_paper_lineage when model or method ancestry matters. Use get_task, get_method, list_conferences, list_organizations, and list_frameworks to orient within the taxonomy.
  6. Preserve paper titles, identifiers, and URLs so claims remain traceable.

Output and limits

  • Tool results use stable, versioned structured output with compact text fallbacks. Prefer structured fields over parsing the text fallback.
  • Search mode is deterministic: hybrid is the default, matching pwc search; keyword uses lexical retrieval alone and semantic uses dense retrieval alone. Both hybrid and semantic count toward the semantic search rate limit, so pass mode: "keyword" for exact terminology or when that limit is close.
  • Catalog-filtered paper lists fail closed unless the server confirms every requested filter; never treat results from an older server as filtered. Parameter-filtered leaderboards fail closed unless the server confirms parameter-filter support and every returned model satisfies the limit.
  • read_paper returns at most one 64 KiB chunk per call. A continuation stays pinned to the resolved paper and content version; if the paper changes, start reading again from the beginning.
  • The hosted service limits each client to 60 total requests and 10 semantic or hybrid searches per minute, with at most four concurrent requests. Respect retry metadata instead of trying to work around limits.
  • The tools contain no authentication, catalog mutation, ingestion, publication, image, embedding, CRON, or infrastructure-maintenance operations.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Pwc Mcp AI skill do?

Papers With Code MCP tools for searching and reading AI/ML papers, discovering recent and trending research, finding related work and paper lineage, browsing tasks, methods, conferences, organizations, and frameworks, and inspecting benchmark leaderboards with model-size and metric filters through the public Papers With Code catalog. Use whenever the user asks to find papers, survey literature, compare research, inspect an arXiv paper, explore AI/ML taxonomy or conferences, discover benchmarks or state-of-the-art models, or mentions Papers With Code, the PwC MCP server, or paperswithcode.co...

Why use Pwc Mcp on TypingMind?

Because you install it once and use it with any model. Pwc Mcp is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Pwc Mcp in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/huggingface/pwc-cli/tree/main/mcp_server. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Pwc Mcp?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Pwc Mcp?

As many as you like. As long as a model supports skills, you can use Pwc Mcp with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Pwc Mcp AI skill free?

It is published on GitHub by huggingface. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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