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Precis MCP

Research, authoring, simulation, memory, and web tools exposed through one MCP interface backed by PostgreSQL and pgvector.

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Install Precis MCP

Pick your client, then add it in one click or paste the config.

STDIOPython
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "precis-mcp": {
      "command": "uvx",
      "args": [
        "precis-mcp"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Install not yet sandbox-tested— we haven't run this command through our automated sandbox yet. Not a red flag; we're working through the catalog.

Overview

precis-mcp connects language-model agents to research references, authored artifacts, code, memory, autonomous work, and cached external tools. Its unified API uses verbs such as get, search, put, edit, delete, tag, link, and more, with a kind argument selecting the content or tool type. PostgreSQL with pgvector stores content and supports hybrid lexical and semantic search. Reach for it when an agent needs paragraph-level evidence, structured technical artifacts, persistent work records, or a broad research workspace.

Use cases

  • Search papers and patents with paragraph-level evidence
  • Draft and export research papers, patents, and reports
  • Build and inspect CAD, molecular, structure, and PCB artifacts
  • Manage recurring research quests, todos, and execution jobs
  • Store linked memories, citations, findings, and provenance

Key features

  • Unified verb API for content and tool kinds
  • Hybrid lexical and semantic search with pgvector
  • Paragraph-level citations and evidence chains
  • Draft export to LaTeX, PDF, and Word
  • Code, CAD, molecular, structure, and PCB representations
  • Persistent quests, memory, links, tags, and provenance

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 1d ago

Recent health check succeeded.

Supply-chain signal

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Scanned 10d ago via OSV.dev · precis-mcp (PyPI)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars8Stargazers on the source repository.
Last commit1d agoMost recent push to the default branch.
Directory activity2 viewsConfig copies, upvotes, and views on AllMCPs.

How Precis MCP works

What precis-mcp does

precis-mcp provides a single MCP tool surface for research, writing, technical design, agent work, memory, evidence, and selected external lookups. It can work with ingested papers, patents, proposal documents, conversations, slide decks, skills, and files under PRECIS_ROOT. Authored content includes chunk-based drafts, analytic CAD solids, structural-envelope designs, molecular-machine designs, atomistic structures, PCB graphs, and folders.

The server also models work and evidence as persistent objects. Quests represent open problems that can be advanced over repeated ticks; todos can recur and create work; jobs represent execution units; and plans, defect reports, memories, citations, findings, provenance, and agent logs provide supporting records. Tool kinds cover local SymPy calculations, Wolfram queries, YouTube transcripts, web extraction, Wikipedia, web search, and Perplexity research modes.

How it works

Operations are selected through a verb and a kind value. Use get when you know an identifier or need to invoke a tool, search for topical or phrase-based discovery, and put to create a reference. edit changes file regions using content anchors, while delete, tag, and link manage existing records. Truncated results can be continued with more and its cursor.

Reference records may be addressed by slug or numeric ID. Responses provide compact handles such as pa5 for a paper or me42 for a memory. Content searches combine PostgreSQL full-text indexing with pgvector semantic search and reciprocal-rank fusion. File regions use a compact selector form such as notes--meeting~L42-58.

Setup and configuration

Install the package with uv using Python 3.13:

bash
uv tool install --python 3.13 'precis-mcp[all]'

The installation creates an isolated environment containing the precis command. Upgrade it with uv tool upgrade precis-mcp. The core installation is approximately 650 MB, while the all-extras installation is approximately 1.6 GB on the documented macOS arm64 measurement; Linux CUDA dependencies can require several additional gigabytes. Models are downloaded on first use.

Optional extras and environment configuration determine which kinds appear in a running instance. The live registry can be inspected with get(kind='skill', id='precis-help'); precis-overview provides a guided tour, and precis-toolpath-help maps goals to calls. Single-machine and multi-host deployment paths are included in the project documentation.

Tools and capabilities

  • Search papers, patents, documents, code, memory, and other indexed content.
  • Preserve claims with paragraph-level citations and evidence chains.
  • Create drafts that export to LaTeX, PDF, and Word, with figures, diagrams, and audio narration.
  • Navigate Python repositories using symbol and call-graph information.
  • Build and analyze CAD, molecular, atomistic, and PCB representations.
  • Manage quests, todos, jobs, plans, agent fleets, links, tags, and provenance.
  • Query cached web, Wolfram, YouTube, Wikipedia, web search, and Perplexity sources when enabled.

Limitations and notes

The available kind set depends on installed optional dependencies and configured environment values. A capability whose requirements are unavailable is omitted from the tool surface. The catalogue is therefore build-specific; the live registry is the authoritative way to discover enabled kinds. Some operations also depend on configured services such as PostgreSQL, pgvector, or external research tools. The project is actively developed, and its catalogue changes over time.

Read the full README View source on GitHub

Reviews

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Frequently Asked Questions about Precis MCP

precis-mcp is an MCP server that connects language-model agents to research papers, patents, documents, code, memory, authored artifacts, PostgreSQL with pgvector, and cached web or scientific tools. Its main interface uses get, search, put, edit, delete, tag, link, and more to retrieve, create, modify, and connect content.

Install it with uv and Python 3.13 using `uv tool install --python 3.13 'precis-mcp[all]'`. The command creates an isolated environment and installs the `precis` command. Use the package without `[all]` for the core installation, or run `uv tool upgrade precis-mcp` to upgrade it.

You can search papers, patents, documents, code, memory, authored artifacts, and other indexed kinds. Search combines PostgreSQL lexical indexing with pgvector semantic retrieval and reciprocal-rank fusion. Enabled tool kinds can also provide web, Wikipedia, YouTube, Wolfram, web-search, and Perplexity lookups.

It links claims to source passages at paragraph level and can represent verified citations and chains of evidence. Papers and patents are among the supported reference kinds, while findings record evidence gathered through a citation chase.

A kind appears only when its optional dependencies and required environment configuration are available. The enabled set is therefore specific to each build and running instance. Query `get(kind='skill', id='precis-help')` to inspect the live registry.

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