Estimation Prover MCP. Stop guessing. Prove your project timelines with structured validation.
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Estimation Prover. This tool forces agents and engineering teams to stop guessing and start proving project timelines. It acts as a pre-commitment filter, requiring you to decompose scope into small tasks, identify technical risks, cite historical data, and apply realistic buffers before you commit to a deadline.
It stops the Planning Fallacy cold.
What your AI agents can do
Validate estimation
Forces you to decompose scope, map unknowns, cite historical precedent, apply contingency buffers, and state assumptions before committing to a timeline.
It breaks down large feature requests into discrete work units, ensuring no task exceeds two days.
It forces documentation of architectural risks and external API dependencies, including their potential impact range on the timeline.
It validates the proposed timeline against concrete completion rates from previous, similar projects.
It mandates a minimum contingency time, adjusting the buffer size based on whether the work is familiar or brand new.
It requires the explicit listing of all scope limits, resource availability, and dependencies that must hold true for the estimate to be valid.
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Estimation Prover: 1 Tool for Project Validation
Use the single `validate_estimation` tool to rigorously test any project scope, ensuring you account for every technical risk and resource limitation.
019e599cvalidate estimation
Forces you to decompose scope, map unknowns, cite historical precedent, apply contingency buffers, and state assumptions before committing to a timeline.
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What you can do with this MCP connector
validate_estimation forces your agent and engineering team to break down scope, map unknowns, cite historical data, apply buffers, and state assumptions before you commit to a timeline. It breaks down big feature requests into small tasks, making sure no unit takes longer than two days. It documents architectural risks and external API dependencies, outlining the potential impact range for each.
It validates the proposed timeline against concrete completion rates from previous, similar projects. It requires a minimum contingency time, adjusting the buffer size based on whether the work is familiar or brand new. Finally, it forces you to explicitly list all scope limits, resource availability, and dependencies that gotta hold true for the estimate to be valid.
How Estimation Prover MCP Works
- 1 Start by feeding the tool a preliminary project timeline and scope. You must break the work down into tasks of two days or less.
- 2 Next, input all known risks (e.g., 'API X is unstable') and cite a specific, measurable historical project that proves the timeline is achievable.
- 3 Finally, include a calculated contingency buffer (e.g., '30% buffer added') and state every assumption—from team size to requirement stability—that must be true.
The bottom line is, it stops you from making big, unverified guesses about project timelines.
Who Is Estimation Prover MCP For?
The tech lead who keeps arguing with the PM about unrealistic deadlines. The engineering manager who needs to prove timelines to stakeholders. Product owners tired of scope creep and fuzzy requirements. If you’re tired of 'It'll take a few weeks,' this is for you.
Runs the validate_estimation tool on a draft project plan to ensure the team has budgeted for known technical risks and sufficient contingency time before presenting to leadership.
Uses the tool to decompose a large feature into micro-tasks, proving that each piece of work is manageable and doesn't rely on unstated assumptions.
Inputs the initial scope and constraints into the tool, forcing themselves to quantify risks and provide historical context, making their plan defensible.
What Changes When You Connect
- Eliminate scope ambiguity. By forcing you to decompose work into units of two days or less, you immediately expose vague tasks and scope creep.
- De-risk the timeline. The tool requires identifying technical unknowns and mapping their specific impact ranges, preventing hidden dependencies from derailing the schedule.
- Anchor estimates in reality. Instead of 'I think it will take X,' you ground the timeline in concrete historical performance data from similar past projects.
- Guarantee resilience. It mandates adding a contingency buffer (minimum 20% to 40%+), ensuring the project schedule absorbs minor delays without failing.
- Force clarity on boundaries. You must explicitly list all assumptions—like 'assuming no team changes'—making the estimate immediately testable and accountable.
- Prevent 'optimism bias.' It prevents developers from setting wildly optimistic dates by requiring a multi-faceted validation process.
Real-World Use Cases
Building a New Mobile Catalog
The PM gives a rough 2-week estimate for a new mobile catalog. The agent runs validate_estimation. The tool immediately flags the scope as vague and demands decomposition into components like 'database sync' and 'listing UI'. The PM fixes the breakdown and adds a 30% buffer, resulting in a verified timeline.
Rethinking a Legacy Integration
The team needs to integrate a stable third-party API, but the required timeline is tight. The agent runs validate_estimation and forces the team to cite a specific historical precedent for that API integration. Without that proof, the tool rejects the estimate, forcing the team to slow down and properly scope the risk.
Planning a Novel Feature
A team wants to build a completely new feature with no history. The agent runs validate_estimation. The tool requires a large contingency buffer (40%+), forcing the team to account for the high uncertainty and preventing an overly aggressive timeline.
Handling Scope Creep Mid-Project
A stakeholder requests a small, 'easy' addition to a project. The team uses validate_estimation to test the new scope. The tool requires a full breakdown and a new risk assessment, immediately showing the added complexity and preventing unauthorized scope expansion.
The Tradeoffs
Single-Number Guessing
Stating, 'It will take about 3 weeks.' This approach ignores all technical unknowns, resource limits, and the fact that one small task will inevitably run late.
→
Instead, run validate_estimation. Break the work into tasks ≤2 days, map the known risks, and add a buffer. This provides a defensible, structured estimate.
Ignoring Precedent
Claiming a task is easy because it's similar to something done before. This fails if the underlying dependencies or environment have changed since the last time.
→
Run validate_estimation and cite a specific historical reference (e.g., 'Last year's web catalog sync took 3 days due to conflicts'). This grounds your estimate in verifiable reality.
Skipping the Buffer
Underestimating risk and claiming 'no buffer is needed' because the requirements seem simple. This leaves zero room for unexpected integration failures.
→
Always run validate_estimation. Even for simple tasks, the tool forces you to apply a minimum 20% contingency buffer to account for integration delays.
When It Fits, When It Doesn't
Use this if you need to prove that a project timeline is defensible against scope creep and technical risk. You must use it when: 1) Starting a new project; 2) Making a high-stakes commitment to a deadline; or 3) Integrating a new, complex dependency. Don't use it if your only goal is a quick, 'gut feeling' estimate. If you just need to quickly gauge a ballpark date, use a simple time tracking tool or a manual conversation. Estimation Prover is for high-stakes commitment only. It forces rigor, which is exactly what you need when the stakes are high.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Estimation Prover. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 1 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Project estimates always seem too optimistic.
When you estimate a project manually, you tend to forget the little things. You skip the times spent debugging flaky APIs, the inevitable scope questions, or the dependency checks. You end up with a single, clean date that doesn't exist in reality.
With Estimation Prover, the agent forces you to break the work into micro-tasks and list every potential failure point. The result is a detailed, multi-step validation that shows exactly where the time is going. No guesswork—just proof.
Estimation Prover MCP Server: Validate Estimates
You don't have to manually check for scope decomposition, historical data, or buffers. The agent handles these checks automatically, flagging missing components and forcing you to add the necessary details.
It’s not just a checklist. It's a mandatory filter that validates your entire plan against established project management best practices. You get a verifiable, resilient estimate every time.
Common Questions About Estimation Prover MCP
How do I use the validate_estimation tool? +
You must provide the tool with a full scope breakdown of tasks (ideally 2 days or less). The tool then checks if you've addressed unknowns, cited historical data, and applied a contingency buffer.
Does Estimation Prover handle scope creep? +
Yes. If you add new scope, you must run the validate_estimation tool again. It immediately forces you to re-decompose the work, re-assess the risks, and adjust the buffer, quantifying the impact of the change.
Is the estimate provided by validate_estimation guaranteed? +
No. The tool only proves that the estimate follows structured project management principles. It's a rigorous check against planning fallacies, not a guarantee of flawless execution.
What kind of data does validate_estimation need? +
It needs granular data: specific historical precedents (not general feelings), a breakdown of tasks, and explicit listing of all resource assumptions.
Does Estimation Prover work with large, multi-service projects? +
Yes. By forcing the decomposition of complex milestones into small, verifiable units, it keeps the entire project scope manageable and auditable.
How does the `validate_estimation` tool handle ambiguous project requirements? +
The tool forces decomposition, requiring you to break the vague requirements into micro-tasks (ideally 2 days or less). It won't accept a single timeline estimate if the scope isn't granular enough.
What happens if I get a 'SCOPE_VAGUE' verdict from `validate_estimation`? +
The verdict explicitly tells you which axes failed. You must adjust your input by adding decomposition, identifying unknowns, or adding a buffer before trying again.
Does `validate_estimation` require specific historical data types? +
Yes, it needs concrete, specific historical precedents—not just 'experience.' It relies on actual metrics, like 'last year's sync issues took 3 days.' Failure to cite specifics results in a failed validation.
How does Estimation Prover validate an estimate? +
It analyzes the inputs based on a 5-pivot validation. You provide the task decomposition, risk mapping, historical context, buffer metrics, and assumptions. It rejects single-line guesses or projects without buffers.
What is the recommended buffer size? +
The tool enforces a minimum 20% buffer on projects with clear precedents, and increases to 40% or more for complex integrations, new frameworks, or systems with high architectural risk.
How does Reference Class Forecasting work here? +
It forces you to compare the new project with similar work completed in the past. If your past authentication integration took 3 weeks instead of the planned 1 week, you must adjust the new estimate's baseline accordingly.
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