How to Use the Estimation Prover MCP in LlamaIndex

Q: Can I use Estimation Prover to audit old LlamaIndex query logs?

Yes, you can pipe your historical query logs through the validateestimation tool. This helps you analyze where past project estimates deviated from actual delivery times.

Ground your LlamaIndex RAG pipelines in realistic project planning with the Estimation Prover MCP Server.

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Connect Estimation Prover MCP to LlamaIndex

Create your Vinkius account to connect Estimation Prover to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Estimation Prover with LlamaIndex

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Ground timeline estimates in actual project data

The `validate_estimation` tool connects your index of past projects directly to your agent's decision-making loop. Instead of guessing, your LlamaIndex agent queries your vector store for historical precedents to justify every timeline. This setup stops hallucinations about developer velocity. Your agent must find a real, documented past task to back up its current projection or the validation fails.

Index and query estimation risk factors

The `validate_estimation` tool lets you turn raw estimation outputs into searchable index nodes. When the tool runs, it documents every single assumption and unknown. You can then query these nodes later to see which assumptions broke. This feedback loop improves your team's planning accuracy over time by exposing patterns of optimism bias.

Force granular task decomposition in RAG workflows

The `validate_estimation` tool rejects any project plan containing tasks longer than two days. Your LlamaIndex agent must parse the document, split the scope, and validate each chunk. This granular approach makes your vector search more effective. Smaller, well-defined tasks are easier to match against your historical database, leading to highly accurate context retrieval.

Setup guide

Set up Estimation Prover MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Estimation Prover MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Estimation Prover tools.",
)
response = await agent.run("List recent Estimation Prover data")

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Common questions about Estimation Prover MCP in LlamaIndex

The MCP client connects directly to your agent as a tool. It ensures the historical precedents found in your vector store match the current scope complexity.

Yes, you can pipe your historical query logs through the `validate_estimation` tool. This helps you analyze where past project estimates deviated from actual delivery times.

It forces the agent to break down specs into two-day tasks. This structured decomposition results in highly detailed, searchable documentation for your engineering team.

It demands a minimum 20% buffer for routine tasks. For novel work, the tool rejects any timeline that does not include a 40% to 60% contingency.

All timeline data, historical precedents, and estimation scope details are processed in a zero-trust, isolated environment. No data is stored on disk, and the endpoint token prevents unauthorized access to your validation logs on this MCP Server.

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