VerveContext

Ground LangChain agents with live data

Wire grounding into the pipeline you already build. Load VerveContext's MCP tools into a chain or agent and 300+ live, verifiable sources become callable tools your steps can reach — so each step reasons over a real fact, with a citation your app can surface.

  • MCP tools
  • Chains & agents
  • Cited results
Get grounded→All frameworks
Why ground LangChain

Grounding as a node in your graph

For the builder assembling chains and agents that can't afford a poisoned intermediate fact.

Tools in your chain

VerveContext's sources load as standard tools your agent can bind to. Drop them alongside your own functions and the model picks the right source at each step — grounding is just another node in the graph you already build.

  • Tool binding
  • Any node

Grounded steps in a pipeline

In a multi-step chain, a wrong intermediate fact poisons everything downstream. Grounding the step that needs a real value — a rate, a price, a location — keeps the rest of the pipeline reasoning over truth, not a guess.

  • Multi-step
  • No drift

Citations your app can surface

Each grounded value comes back with its source, so your application layer can render a citation next to the result. The provenance flows through the chain to the user, not just to the model.

  • Provenance
  • App-surfaced
Setup

Load the tools into your chain

Connect through LangChain's MCP adapter, bind the returned tools to your agent, and let steps call sources when they need a real value.

  1. 01

    Load the MCP tools

    Use LangChain's MCP adapter to connect to the VerveContext server at https://api.vervecontext.com/v1/mcp, passing your scoped key as the x-api-key header. The adapter returns the enabled sources as LangChain tool objects.

  2. 02

    Bind them to your agent

    Add the returned tools to your agent's tool list — the same way you'd add any custom tool — so the model can choose a source when a step calls for a real value.

  3. 03

    Let the chain call sources mid-run

    As the agent executes, it invokes the matching source when it needs a fact and feeds the typed value into the next step. No source is called unless a step actually needs it.

  4. 04

    Carry the provenance through

    Each tool result includes where the value came from. Thread that through your chain's output so the final answer — and your app — can cite the source, not just state the number.

Grounding LangChain, answered.

The adapter, tools and pipeline specifics builders ask about.

MCP setup guides →
How do LangChain and VerveContext connect?
Through MCP. LangChain's MCP tool adapter connects to the VerveContext server endpoint and converts each enabled source into a LangChain tool. From your code's point of view they're ordinary tools — you bind them to an agent and it calls them like any other.
Do I wrap each source as its own tool?
No. One connection to the MCP server yields all your enabled sources as tools at once. Enable a new source in VerveContext and it appears in the tool list the next time the adapter loads — no new wrapper code.
Can I control which sources the agent can call?
Yes, at two levels: scope the key so only certain sources are reachable at all, and then bind only the subset of returned tools you want a given agent to use. Every call the agent makes is recorded in your usage logs.
Does this work for chains, not just agents?
Yes. Because the sources are plain tools, you can invoke them directly inside a chain step — you don't need a fully autonomous agent loop. Use them wherever a step needs to ground itself in a real value.
What shows on my bill?
VerveContext runs on APIVerve, our production data engine, so invoices and card statements read APIVERVE. Same account, same key, same rails — nothing else changes.

Ground every step of the chain. Load the MCP tools once and your pipeline reasons over facts, with citations that carry through.

Get grounded→How grounding works

Other agents?

Claude, Cursor, or any MCP client connect the same way.

All connect guides