Vela Corpus

Read-only MCP access to Vela's corpus of human experience: cited passages, coordinates, reading paths.

CommunityOnlineNo sign-inGlobalFreeCan modify data

What it can do

  • Vela.Corpus.Query: Ask the corpus by COORDINATE rather than keyword. Filter works by axis weights (e.g. religion ∈ {T,s} AND work ∈ {T,s} returns the God-and-Work source set across genres), controlled
  • Vela.Corpus.List Topics: List the coordinate systems the corpus can be queried through — each with its thesis, axes, and controlled vocabularies. Call this before vela.corpus.query to learn what axes
  • Vela.Corpus.Describe Topic: Describe ONE topic in full — its axes and their operative definitions, its complete controlled theme and quality vocabularies, plus the vocabulary actually IN USE across th

What data it sees

Do you need an account

No: the server works without sign-in

Read-only MCP access to Vela's corpus of human experience: cited passages, coordinates, reading paths.

Vela (vela.study) is a study of being human read through literature, art, and behavioral science. This server gives an agent read-only, unauthenticated access to Vela's corpus of ~1,100 scored literary and behavioral-science extraction cards, queryable by controlled theme, emotion, and a four-axis coordinate system (sex / work / learning / religion), plus the magazine's adaptive sequencing engine. Every result returns citations (source, passage code, relevance score, a short clamped snippet) rather than full passage text or a fabricated summary, because the underlying corpus is licensed for Vela's own use and is not reproduced wholesale. No sign-in, no API key, no writes.

Tools: vela.corpus.list_topics, vela.corpus.describe_topic, vela.corpus.query, vela.corpus.ground_claim, vela.corpus.witness, vela.magazine.sequence_path, vela.magazine.relation_floor. Call list_topics first to learn the vocabulary.

Docs: https://vela.study/llms.txt · OpenAPI twin: https://vela.study/openapi.json · Official MCP Registry: study.vela/corpus

How to connect

How to connect Vela Corpus to Claude

The server address to paste into Claude settings:

https://vela-corpus--mike-6yge.run.tools

  1. Open Claude (claude.ai or the desktop app).
  2. Go to Settings → Connectors.
  3. Click Add custom connector.
  4. Paste the server address copied below into Remote MCP server URL and click Add.
  5. In a chat, click + → Connectors and switch the new connector on.

Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.

Authorization

After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.

How to connect Vela Corpus to ChatGPT

The server address to paste into ChatGPT settings:

https://vela-corpus--mike-6yge.run.tools

  1. Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
  2. Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
  3. Open Settings → Connectors and click Create.
  4. Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
  5. Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
  6. In a new chat open + → Apps/Connectors and enable the connector.

OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.

Authorization

On first use ChatGPT opens the service's sign-in window. Sign in and approve access.

How to connect Vela Corpus to Cursor

The server address to paste into Cursor settings:

https://vela-corpus--mike-6yge.run.tools

Fastest: click Open in Cursor below and confirm the prompt.

Manually:

  1. In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
  2. Paste the JSON copied below into ~/.cursor/mcp.json (per project: .cursor/mcp.json in the repo root).
  3. Save the file. The server appears in the MCP list; authorize it there if asked.

Authorization

If the service needs sign-in, an authorize button appears next to the server in the MCP list.

Server tool list (7)

Raw names from tools/list. Only developers need these.

vela.corpus.queryAsk the corpus by COORDINATE rather than keyword. Filter works by axis weights (e.g. religion ∈ {T,s} AND work ∈ {T,s} returns the God-and-Work source set across genres), controlled themes, qualities/emotions, genre, and free text. Returns card metadata: the one-line claim, themes, coordinates, qualities. FREE and read-only. [capability: library-extraction-cards · mirrors: POST /api/corpus/query]
vela.corpus.list_topicsList the coordinate systems the corpus can be queried through — each with its thesis, axes, and controlled vocabularies. Call this before vela.corpus.query to learn what axes and themes exist. FREE and read-only. [capability: topic-coordinate-systems · mirrors: GET /api/corpus/topics]
vela.corpus.describe_topicDescribe ONE topic in full — its axes and their operative definitions, its complete controlled theme and quality vocabularies, plus the vocabulary actually IN USE across the corpus with counts. The declared vocabulary and the used vocabulary are different facts; this returns both. FREE and read-only. [capability: topic-coordinate-systems · mirrors: GET /api/corpus/topics/:slug]
vela.corpus.ground_claimGround a claim in the corpus: hybrid semantic + theme retrieval over real passages, returning citations with source, passage code, scores, and a SHORT snippet. Use to check whether the corpus supports, complicates or is silent on an assertion. COSTS a small amount (one embedding per call). [capability: corpus-ingestion-dual-grade · mirrors: internal — lib/audit/retrieve.ts]
vela.corpus.witnessLiterary Witness Rail (ASN-1577): return passages and one-line claims from Vela's 762-work corpus that illuminate the given themes and/or emotions. Designed for frame-family profile pages — PersonFrame × ProblemFrame × ConditionFrame — to add an inside-out layer (what writers who lived this say) alongside the outside-in portrait the frame join produces. Results carry evidence_grade 'T1' (single-extractor) until PRN-282 lands. FREE for claims; costs one embedding for the passage path. [capability: literary-witness · mirrors: GET /api/vela/witness]
vela.magazine.sequence_pathAsk what this magazine would actually give a reader who said they wanted these things. Returns the REAL sequenced path with the rationale for each piece — the same core the anonymous intake calls, not a preview. Read-only: writes nothing and records no signal. [capability: adaptive-magazine-engine · mirrors: POST /api/magazine/start-path]
vela.magazine.relation_floorWhether a magazine's corpus can support the adaptive promise its pages render. Reports, per property, each relational dimension's fill AND its separating power — a field present on every piece with one distinct value carries no information, so fill rate alone is misleading. Read-only. [capability: adaptive-magazine-engine · mirrors: npm run magazine:relation-floor]