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Doings Evidence

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This MCP server provides critical evidence assessment for organization-design, leadership and transformation claims, enabling users to critique consulting text,

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Описание

This MCP server provides critical evidence assessment for organization-design, leadership and transformation claims, enabling users to critique consulting text, check claims against research, and generate safer phrasing.

README

v0.9 Thinking Interface

The default user-facing tool is now think_with_evidence. It helps Doings users think, phrase, challenge and make organizational arguments client-safe. It builds on the evidence engine, but adds:

  • argument mapping,
  • solution-first detection,
  • Doings voice rewriting,
  • client-safe language,
  • evidence-to-language translation,
  • learning nudges that teach better reasoning patterns.

Example:

{
  "input": "We think the client needs a flatter organization to become faster.",
  "context": "Nordic professional-services company, 50-150 employees, project-based client delivery",
  "mode": "thinking_partner"
}

The tool should respond by mapping the reasoning, flagging solution-first risk, asking where speed is actually lost, and producing Doings-voice and client-safe language.

See docs/v0.9-thinking-interface.md.

Local MCP server for critical evidence assessment of organization-design, leadership and transformation claims.

Current version: 0.11.0

v0.11 deployment chain

v0.11 adds a GitHub -> Azure -> remote MCP deployment chain:

  • GitHub Actions workflow for Azure Container Apps
  • Bicep infrastructure template
  • remote MCP smoke test
  • deployment runbook

Start with docs/deploy-chain-runbook.md.

Purpose

Doings Evidence MCP is a critical evidence editor for organizational thinking. It helps Doings distinguish between:

  • academic research
  • internal experience / IP
  • practical heuristics
  • unsupported consulting claims
  • claims that are plausible but overstated
  • text that is usable only with caveats and safer wording

It is not a recommendation generator and it is not a systematic literature review engine.

v0.9 highlights

v0.9 adds a user-facing critique layer on top of the v0.7 evidence engine:

  • intent detection for rough user questions and draft text
  • critique modes: quick_check, rewrite_safely, red_team, evidence_brief
  • critique_org_text tool for day-to-day consulting text
  • can_we_say_this fast check alias
  • consulting-language risk detector
  • safer-phrasing generator
  • narrative response layer that explains what to say, what to avoid and why

Core tools

critique_org_text

Best default tool for human questions, pitch/RFP sentences, rough consulting text and “can we say this?” prompts.

Example:

{
  "input": "Autonomous teams unlock agility and reduce the need for middle management.",
  "context": "Nordic professional-services company, 50-150 employees, project-based client delivery, senior expert dependency.",
  "mode": "auto",
  "includeRawCritique": false
}

Output includes:

  • detected user intent
  • selected critique mode
  • primary claim extracted from the text
  • consulting-language risk
  • narrative answer
  • safer version
  • caveats and warnings

can_we_say_this

Fast practical check for whether a claim or draft sentence is safe enough to say. It uses the same schema as critique_org_text but defaults to practical quick-check behavior.

critique_claim

Research-heavy tool for a specific explicit claim.

Example:

{
  "claim": "Autonomous teams make organizations more agile and reduce the need for middle management.",
  "context": "Nordic professional-services company, 50-150 employees, project-based client delivery, senior expert dependency.",
  "strictness": "high",
  "yearFrom": 2000,
  "maxPapers": 10,
  "fullTextMode": "open_access",
  "maxFullTextPapers": 3,
  "redTeamMode": true
}

Output includes:

  • decomposedClaims
  • levelOfAnalysis
  • levelAlignment
  • contextFit
  • studyTypeProfile
  • evidencePassages
  • redTeam
  • statusLabel: exploratory_evidence_scan_not_systematic_review

search_research_evidence

Searches OpenAlex and Semantic Scholar, optionally escalating to open-access full text.

fetch_doings_document

Fetches one SharePoint/OneDrive document, extracts local text when possible, and can classify, audit and validate high-risk claims.

audit_doings_document_claims

Audits raw text or SharePoint document text for research-checkable claims, nearby citation markers and high-risk unsupported claims.

Use:

{
  "validateHighRiskClaims": true,
  "validationFullTextMode": "open_access",
  "validationRedTeamMode": true
}

rate_evidence_quality

Returns a conservative heuristic rating with study-type profile and full-text coverage.

Critique modes

quick_check

For “kan vi säga detta?” or one rough claim.

Returns a short verdict, why it is risky or usable, safer wording and use-with-caution notes.

rewrite_safely

For pitch/RFP/report wording.

Returns a research-honest rewrite, what changed and what not to imply.

red_team

For finding the weak points.

Returns the most vulnerable assumption, likely skeptical objections, alternative explanations and a stress test.

evidence_brief

For “vad säger forskningen?”

Returns an evidence status, what is better supported, cautions, boundary conditions and safer formulation.

Run locally

npm install
npm run dev

Build:

npm run build
npm start

Environment

Copy .env.example to .env and configure Microsoft Graph if using SharePoint tools.

cp .env.example .env

Minimum SharePoint variables:

MS_TENANT_ID=...
MS_CLIENT_ID=...
MS_GRAPH_SCOPES=Files.Read.All Sites.Read.All offline_access

Research sources

  • OpenAlex for broad scholarly metadata and open-access locations
  • Semantic Scholar for additional academic search and abstracts
  • Open-access PDF/HTML/text fetching when available

Important limitations

This is an exploratory evidence scan, not a systematic literature review. It does not perform formal inclusion/exclusion coding, PRISMA-style review, quality appraisal by multiple reviewers or exhaustive full-text search. Treat outputs as a critical first-pass assessment.

v0.10 remote-ready deployment

v0.10 can run in two modes:

# Local MCP / STDIO
npm run build
npm run start

# Hosted MCP / Streamable HTTP
MCP_REQUIRE_AUTH=true MCP_BEARER_TOKEN=<token> npm run start:http

Hosted endpoints:

GET  /health
POST /mcp
GET  /mcp
DELETE /mcp

See:

docs/remote-mcp-azure.md
docs/github-setup.md
docs/v0.10-remote-ready.md
deployment/example-client-config.local.json
deployment/example-client-config.remote.json

Recommended path:

GitHub repo -> Azure Container Apps -> remote MCP URL -> ChatGPT / MCP-compatible client

from github.com/Marcwarn/doings-evidence-mcp

Установка Doings Evidence

У этого сервера нет опубликованного пакета — он собирается из исходников. Открой репозиторий и следуй инструкции в README.

▸ github.com/Marcwarn/doings-evidence-mcp

FAQ

Doings Evidence MCP бесплатный?

Да, Doings Evidence MCP бесплатный — установка в пару кликов через Unyly без оплаты.

Нужен ли API-ключ для Doings Evidence?

Нет, Doings Evidence работает без API-ключей и переменных окружения.

Doings Evidence — hosted или self-hosted?

Self-hosted: сервер запускается локально на твоей машине командой из раздела установки.

Как установить Doings Evidence в Claude Desktop, Claude Code или Cursor?

Открой Doings Evidence на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.

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