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Nimo1987

Harness Research MCP

by Nimo1987
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TypeScript
Remote

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Harness Research MCP

Give any AI Agent professional-grade deep research capabilities.

An MCP (Model Context Protocol) server that performs real-time deep research across 5 data sources, evaluates source credibility with the CRAAP framework, cross-verifies findings, and generates professional reports — all driven by affordable models like Kimi K2.5.

A full deep research report takes ~10 minutes and can be driven by Kimi K2.5 at ~$0.01/run.

For AI Agents: How to install this tool

This is a Node.js MCP server, NOT a Python package. Do NOT git clone or pip install.

Step 1 — One-time setup (run in user's terminal):

npx harness-research-mcp setup

This launches an interactive wizard to configure API keys (stored in ~/.harness-research/.env).

Step 2 — Register as MCP server in your agent's config file:

Claude Desktop / Cursor / Windsurf — edit MCP config JSON:

{
  "mcpServers": {
    "harness-research": {
      "command": "npx",
      "args": ["-y", "harness-research-mcp"]
    }
  }
}

OpenCode — add to ~/.config/opencode/opencode.json:

{
  "mcp": {
    "harness-research": {
      "command": "npx",
      "args": ["-y", "harness-research-mcp"]
    }
  }
}

That's it. After registration, the agent gets 3 tools: harness_research (full report), harness_search (quick search), harness_status (progress check).

Do NOT clone this repo, build from source, or run node/python directly. npx handles everything.

Async workflow — no timeout issues: harness_research returns a task_id immediately (< 1 second). The research runs in the background (~10 minutes). You must poll harness_status(task_id) every 30–60 seconds until status is "completed" or "failed". This design means the default 60-second MCP timeout will NOT be a problem.

If your MCP client has a configurable timeout, setting it to 1200 seconds (20 minutes) is still recommended as a safety margin:

{
  "mcpServers": {
    "harness-research": {
      "command": "npx",
      "args": ["-y", "harness-research-mcp"],
      "timeout": 1200
    }
  }
}

Why This Tool Exists

The Problem with Current "Deep Research" Tools

Existing deep research tools (Perplexity Deep Research, ChatGPT Research, Gemini Deep Research, etc.) share fundamental flaws:

Problem

Explanation

Rely on stale knowledge

They primarily draw from the LLM's training data, not real-time internet search. You may get data that's months or years out of date.

Opaque sourcing

Most tools don't show where information came from. Some cited URLs are hallucinated.

No source evaluation

A social media post and a government statistical report are treated equally. No mechanism to assess credibility.

Single search source

One search engine, narrow coverage. Academic papers, financial data, and government reports are unreachable.

Not integrable

Locked into specific platforms. Can't plug into your own AI Agent workflow.

Expensive

Require GPT-4, Claude, etc. Each research session costs $1-5+.

How Harness Research Is Different

Feature

Harness Research

Perplexity / ChatGPT / Gemini

Data sources

5 real-time search APIs (Tavily + Brave + arXiv + PubMed + Tushare)

Single search engine or model's internal knowledge

Data freshness

100% real-time search — zero reliance on LLM training data

Mixed stale knowledge + limited search

Source evaluation

CRAAP framework with 5-dimension scoring + T0-T5 tier classification (530+ domain database)

None

Cross-verification

Automatic conflict detection + counterintuitive finding identification

None

Citations

Every reference tagged with source tier, credibility score, publication date

Simple URL list or no citations

LLM requirement

Kimi K2.5 works great (~$0.01/run)

GPT-4 / Claude ($1-5/run)

Output formats

HTML + DOCX + PDF + Markdown

Plain text

Integrability

Standard MCP protocol — works with any Agent

Locked to specific platform

Open source

Apache 2.0

Proprietary

Core principle: The LLM only "thinks" — it never "knows." All factual data comes from real-time search.

Related MCP server: GPT Researcher MCP Server

The 6-Step Research Pipeline

User: "Research the global AI chip market landscape in 2025"
         │
         ▼
Step 1 ── Research Plan (LLM)
         │  Generate chapter structure + search keywords
         ▼
Step 2 ── 5-Source Parallel Search (Code)
         │  Tavily + Brave + arXiv + PubMed + Tushare
         │  Dedup → cap at 50 results
         ▼
Step 3 ── CRAAP Source Evaluation (Code + LLM)
         │  Code pre-filter: T5 eliminated, >3yr eliminated
         │  LLM batch scoring: Relevance + Accuracy + Purpose
         │  Weighted average → filter low-scoring sources
         ▼
Step 4 ── Cross-Verification (LLM)
         │  Data triangulation + conflict detection + counterintuitive findings
         ▼
Step 5 ── Parallel Writing (LLM)
         │  All chapters in parallel + executive summary
         ▼
Step 6 ── Render Output (Code)
         │  HTML + DOCX + PDF (macOS) + Markdown
         ▼
    Professional research report (~10 minutes)

Quick Start

1. Setup (one-time)

npx harness-research-mcp setup

The interactive wizard will guide you through:

  • Configuring search API keys (Tavily or Brave, at least one)

  • Configuring an LLM API key (Kimi K2.5 recommended — cheapest option)

  • Optional: Tushare (Chinese financial data), NCBI (PubMed academic search)

  • Automatic API connectivity test

2. Register with Your AI Agent

Copy the appropriate config for your Agent framework:

Claude Desktop / Cursor / Windsurf:

{
  "mcpServers": {
    "harness-research": {
      "command": "npx",
      "args": ["-y", "harness-research-mcp"]
    }
  }
}

OpenClaw:

openclaw mcp set harness-research '{"command":"npx","args":["-y","harness-research-mcp"]}'

OpenCode:

// ~/.config/opencode/opencode.json
{
  "mcp": {
    "harness-research": {
      "command": "npx",
      "args": ["-y", "harness-research-mcp"]
    }
  }
}

3. Use It

Just tell your Agent:

"Do a deep research on the global AI chip market landscape in 2025"

The Agent will automatically call harness_research and return the full report in ~10 minutes.

Three MCP Tools

Tool

Description

Duration

harness_research

Full deep research with professional report output

~10 min

harness_search

Quick multi-source search, returns structured results

Seconds

harness_status

Check research task progress

Instant

API Keys Explained

Why Do You Need These Keys?

Harness Research does not rely on any LLM's historical knowledge. All information is fetched in real-time from the internet. This requires calling various search and data APIs.

Key

Purpose

Required?

Get it

Cost

TAVILY_API_KEY

Advanced web search (deep scraping support)

Required (pick one)

tavily.com

Free 1000 calls/mo

BRAVE_API_KEY

Privacy-focused web search

Required (pick one)

brave.com/search/api

Free 2000 calls/mo

KIMI_API_KEY

LLM reasoning (planning, evaluation, writing)

Required (pick one)

platform.moonshot.cn

Very low cost

OPENROUTER_API_KEY

LLM reasoning (alternative to Kimi)

Required (pick one)

openrouter.ai

Per-model pricing

TUSHARE_TOKEN

Chinese A-share financial data

Optional

tushare.pro

Free basic tier

NCBI_API_KEY

PubMed academic paper search

Optional

ncbi.nlm.nih.gov

Free

Minimum: 1 search key + 1 LLM key = 2 keys to get started.

Why Kimi K2.5?

  • Cost: ~$0.01 per full research session (vs. GPT-4 at $1-5)

  • Chinese support: Native Chinese language, no translation layer needed

  • Context: 128K token window — handles large volumes of search results

  • Reliability: 99.9%+ API availability

Output Formats

Format

macOS

Windows / Linux

Notes

HTML

✅

✅

Professional layout, dark theme support

DOCX

✅

✅

Word document, ready to edit and share

PDF

✅

❌

Puppeteer-based, macOS only

Markdown

✅

✅

Plain text, easy to post-process

CRAAP Evaluation Framework

Every source is scored across 5 dimensions:

Dimension

Weight

What It Measures

Currency

15%

How recent is the publication?

Authority

25%

Source tier: Government > Academic > Media > Blog

Relevance

25%

How well does it match the research topic?

Accuracy

20%

Is the data verifiable? Does it cite sources?

Purpose

15%

Is the writing objective or biased?

6-Tier Source Classification

Tier

Weight

Source Type

Examples

T0

1.2x

Raw government data APIs

World Bank API, Fed FRED, SEC EDGAR

T1

1.0x

Authoritative institutions

WHO, Nature, Science, government reports

T2

0.8x

Professional organizations

McKinsey, Gartner, Financial Times

T3

0.6x

Mainstream media

Reuters, Bloomberg, TechCrunch

T4

0.3x

General websites

Unclassified domains (default)

T5

0.15x

Social media

Twitter, Reddit (auto-eliminated)

Built-in 530+ domain credibility database covering major governments, academia, media, and professional institutions worldwide.

Diagnostics

npx harness-research-mcp doctor

Architecture

┌──────────────────────────────────────────┐
│  Claude / Cursor / OpenClaw / OpenCode   │
│            (MCP Client)                  │
└────────────────┬─────────────────────────┘
                 │ stdio (MCP Protocol)
                 ▼
┌──────────────────────────────────────────┐
│      harness-research-mcp (Node.js)      │
│                                          │
│  Tools:                                  │
│    harness_research — full deep research  │
│    harness_search   — quick multi-search  │
│    harness_status   — progress query      │
│                                          │
│  6-Step Pipeline:                         │
│    Plan → Search → CRAAP → Verify →       │
│    Write → Render                         │
│                                          │
│  Pure Node.js. Zero Python dependency.    │
└──────────────────────────────────────────┘

Development

git clone https://github.com/Nimo1987/harness-research.git
cd harness-research
npm install
npm run build

License

Apache 2.0

Available Tools

3 tools
harness_researchA

Start a deep research session: multi-source search (Tavily/Brave/arXiv/PubMed/Tushare) + CRAAP source evaluation + cross-verification → professional HTML/DOCX/PDF report.

IMPORTANT: This tool returns IMMEDIATELY with a task_id. The research runs in the background and takes ~8-12 minutes. After calling this tool, you MUST poll harness_status with the returned task_id every 30-60 seconds until status is "completed" or "failed". Do NOT wait idle — poll actively.

Workflow:

  1. Call harness_research → get task_id (returns in <1 second)

  2. Call harness_status with task_id every 30-60s to check progress

  3. When status is "completed", harness_status returns the output file paths

Driven by affordable models like Kimi K2.5 (~$0.01/run).

ParametersJSON Schema
NameRequiredDescriptionDefault
topicYesResearch topic, e.g. 'Global AI chip market landscape 2025'
providerNoLLM provider: kimi (default, cheapest) or openrouter
modelNoModel name. kimi default: kimi-k2.5, openrouter default: anthropic/claude-sonnet-4
output_dirNoOutput directory for reports. Defaults to current working directory.
formatsNoOutput formats. Default: ['html', 'docx']. PDF only available on macOS.

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description fully discloses that the tool returns immediately (non-blocking), runs in background, and uses affordable models ($0.01/run). It also mentions PDF availability limitation (macOS only). All behavioral traits are transparently communicated.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear heading, an IMPORTANT note, and a numbered workflow. Each sentence is purposeful, no redundant text, efficiently conveying essential information in a few paragraphs.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (async, 5 parameters, multiple outputs), the description covers all key aspects: async behavior, polling, output format limitations, and cost. No output schema exists, but the description explains that harness_status returns file paths, completing the context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so parameters are well-described structurally. The description adds value by explaining defaults (e.g., provider='kimi', model='kimi-k2.5', formats=['html','docx']) and the purpose of the tool, though the schema itself already covers basic parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states it 'start[s] a deep research session' with multi-source search and report generation. It lists sources (Tavily/Brave/arXiv/PubMed/Tushare) and output formats (HTML/DOCX/PDF), clearly distinguishing it from siblings like harness_search (likely simple search) and harness_status (status polling).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit step-by-step workflow: call this tool, get task_id, poll harness_status every 30-60s until completion. It warns against idle waiting and notes the ~8-12 minute runtime, giving clear when-to-use and how-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

harness_statusA

Check the progress of a research task started by harness_research.

After calling harness_research, you MUST poll this tool with the returned task_id every 30-60 seconds.

  • status "running": research is in progress, keep polling

  • status "completed": research is done, output file paths are included

  • status "failed": an error occurred, error message is included

If no task_id is provided, lists all tasks.

ParametersJSON Schema
NameRequiredDescriptionDefault
task_idNoTask ID to check. If omitted, lists all tasks.

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description fully discloses behavior: polling mechanism, status meanings, and inclusion of output file paths or error messages. It also covers the fallback of listing all tasks.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Extremely concise: one sentence for purpose, bullet points for statuses, and a closing note on missing task_id. Every sentence adds value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter polling tool with no output schema, the description covers all essential aspects: when to use, how to poll, status interpretations, and listing fallback. No additional context is needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema already describes task_id as optional and its omission lists all tasks. Description adds minimal extra context (the polling behavior and status outcomes), but since schema coverage is 100%, baseline is 3; slight improvement justifies 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool checks progress of a research task started by harness_research, distinguishing it from the sibling tools harness_research (starts tasks) and harness_search.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use (after harness_research), polling frequency (every 30-60 seconds), and what to do based on status (keep polling, retrieve outputs, or handle error). Also mentions listing all tasks if no task_id.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 3 tool updatesv2.0.0
    • First observedharness_research
    • First observedharness_search
    • First observedharness_status
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TDQS

A4.7/5.0
Disambiguation5/5

Each tool has a distinct purpose: harness_research initiates a deep research session, harness_search provides quick results, and harness_status checks progress. No overlap in functionality.

Naming Consistency5/5

All tool names follow the consistent pattern 'harness_<verb>' using underscores, with predictable conventions.

Tool Count5/5

Three tools cover the essential workflow of starting research, quick searching, and status polling. The count is well-scoped for the server's purpose.

Completeness4/5

The tool set covers the core research workflow (start, search, check progress) but lacks a cancel or delete operation for tasks, which is a minor gap.

Maintenance

ActivityInactive
ResponsivenessResponsive

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