MCP server

Twenty-seven tools. One line to connect.

The Inflectiv MCP server has been live since April. Claude, Cursor and three more clients reach it, so an agent connects without anybody building a connector first.

live since Aprilfive clientsone credit per question
Install

Connect it

One command in Claude Code, or the equivalent block in any MCP client's config. No connector to build and no pipeline in between.

Claude Code
claude mcp add inflectiv \
    -e INFLECTIV_API_KEY=inf_your_key_here \
    -- npx -y inflectiv-mcp-server
.mcp.json
{
  "mcpServers": {
    "inflectiv": {
      "command": "npx",
      "args": ["-y", "inflectiv-mcp-server"],
      "env": {
        "INFLECTIV_API_KEY": "inf_your_key_here"
      }
    }
  }
}
The tools

What the tools cover

Datasets, search, agents, files, write-back and webhooks. All twenty-seven, grouped by what they are for. The docs carry every signature.

Datasets

Create one, change its terms, clone it, attach it, and reindex it.

  • create_dataset
  • list_datasets
  • update_dataset
  • update_dataset_mode
  • clone_dataset
  • attach_dataset
  • reindex_dataset
  • list_knowledge_sources

Search

Ask one dataset, ask several at once, or run a batch of questions.

  • search_dataset
  • batch_search
  • cross_query

Agents

Build an agent on one dataset or several, give it instructions, and ask it.

  • create_agent
  • get_agent
  • chat_with_agent
  • update_agent_config
  • list_agents
  • list_agent_datasets

Files

Put raw material in and watch it become records.

  • upload_file
  • list_files
  • get_file
  • reprocess_file

Write-back

the loop

The switch the whole loop runs on: an agent adds what it learned as it works.

  • write_intelligence
  • batch_write_intelligence
  • list_intelligence

Webhooks

Get told when something changes rather than polling for it.

  • register_webhook
  • list_webhooks
  • test_webhook
Two shapes

Two shapes, one price

Take our answer with the record cited, or take the ranked records and ground your own model. The owner's share is the same either way.

Ask for the answer

An Inflectiv agent answers, and hands back the records it used.

agent chat
POST /ext/agents/6113/chat
{ "message": "Which jurisdictions require monthly reserve attestation?" }

→ 200
{
  "answer": "Three of the five require attestation monthly ...",
  "sources_used": [
    { "record_id": 40213, "source": "MiCA Title III, Art. 36",
      "date": "2026-07-14", "score": 0.91 }
  ],
  "credits": 1
}

Ask for the records

If you already have a model, skip ours. Take the ranked records and ground your own. Same price, and the owner earns the same.

dataset query
POST /ext/datasets/query
{ "dataset_id": 28038,
  "query": "Which jurisdictions require monthly reserve attestation?",
  "top_k": 5 }

→ 200  the ranked records, each with its source, its date
     and a similarity score. Nothing generated.
Instructions and billing

A dataset carries its own instructions

5,000 characters

A dataset holds a 5,000 character instruction field. Whatever you write there lands in the prompt of every agent attached to it, so you can change how your data gets used without touching anyone's code.

What a call costs

01

One credit per question

No result tiers and no effort tiers to price out.

02

A miss costs nothing

Every query carries a score threshold. Nothing clears it, nothing is billed.

03

It settles itself

Credits while you build, then USDC over x402 on Base.

Connect it and ask it something.

One line, an API key, and your agent reaches every dataset on Inflectiv as a tool.

live since Aprilfive clientsone credit per question