Estimation Prover MCP for AI. Stop guessing. Start building verifiable timelines.
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Estimation Prover forces structured project planning before you commit to a timeline. This MCP validates estimates by forcing granular scope breakdown, identifying all technical unknowns and dependencies, mapping historical precedents, and applying concrete contingency buffers.
Stop guessing; start calculating.
What your AI can do
Validate estimation
Forces a structured review of your project plan, requiring scope decomposition, unknown mapping, historical comparisons, buffers, and explicit assumptions to validate any timeline.
Breaks large, vague tasks into small, manageable units that are easier to estimate.
Requires documentation of technical risks and external dependencies, noting the potential impact if they materialize.
Grounds current estimates in concrete data from similar projects completed in the past.
Adds a specific, measurable contingency time to account for known project volatility and cognitive biases.
Forces explicit definition of all resource limits, scope stability points, and external dependencies.
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Estimation Prover: 1 Tool Available
This MCP gives your agent the ability to run a comprehensive check on any proposed timeline, ensuring it accounts for every risk and assumption.
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Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
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Forces a structured review of your project plan, requiring scope decomposition, unknown mapping, historical comparisons, buffers, and...
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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 connection provides 1 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
The Pain of the Guessing Game
Right now, project planning often boils down to sticky notes on a whiteboard and optimistic meetings. You gather everyone in one room, throw around high-level tasks—'do feature X,' 'fix bug Y'—and someone suggests a timeline based on how fast they *think* things should go.
It feels productive, but it’s just vague scope that produces vague estimates. You leave the meeting with a date, but you haven't actually built anything; you've only documented assumptions and ignored every potential point of failure.
Estimation Prover: Getting an Accurate Timeline
With this MCP, the process is different. Your agent doesn't accept a date; it demands evidence. It forces you to break 'feature X' into discrete engineering tasks, map every technical risk like library deprecation, and reference exactly how long that specific thing took last year.
The result isn't just a number; it’s an auditable plan. You walk away with a verifiable timeline backed by historical data, quantified risks, and explicit boundaries.
What your AI can actually do with this
Project timelines are notoriously wrong. Developers and PMs tend to underestimate—a phenomenon called the Planning Fallacy. Estimation Prover acts as an immediate gatekeeper for any proposed timeline. It doesn't just calculate a date; it forces you through structured checks that expose blind spots in your plan.
Instead of giving one vague number like 'three weeks,' this tool demands you break the work into micro-tasks, documenting every potential technical risk and dependency failure point. You have to cite specific historical projects for comparison, not just say 'based on experience.' It also forces you to assign a numerical contingency buffer—a real safety net—and list every single assumption: Who's available? Does the API even work yet? If any of those things shift, your original estimate fails.
Connecting Estimation Prover through Vinkius lets your AI client enforce this rigor directly into your planning process. You get an objective verdict on whether a timeline is realistic or just wishful thinking.
019e599c-9255-717c-ac30-741e709e57cb Here's how it actually works
The bottom line is that you get an objective pass/fail grade on your project plan before any code is written.
First, feed the tool your proposed project scope. The system will reject any single timeline or vague description.
Next, it forces you to map out every dependency, list technical risks, and provide a specific historical comparison for grounding.
Finally, it outputs a verdict: whether the estimate is verified, if it needs more buffer, or if the scope itself is too ambiguous.
Who is this actually for?
Product Owners, Development Leads, and Project Managers who are sick of sinking months into projects only to discover the timeline was based on a 'best guess.' This MCP forces you to confront reality early in the planning cycle.
Uses this tool to validate cross-system integration timelines, ensuring that every external API dependency is accounted for and given appropriate time buffers.
Runs the MCP against preliminary feature sets before presenting them to stakeholders. This proves whether the required scope is actually feasible within market deadlines.
Uses it internally during sprint planning to force junior developers to decompose tasks and identify hidden technical debt or knowledge gaps in the current codebase.
What Changes When You Connect
Stops scope creep before it starts. By forcing granular decomposition, you immediately see if a 'single task' is actually 10 separate units of work.
Mitigates the Planning Fallacy. You stop relying on gut feelings and start basing your timeline on concrete historical data from past projects.
Forces risk ownership. It requires you to name technical unknowns—like API changes or library deprecations—and quantify their potential impact, so they can't be ignored later.
Guarantees contingency time. You don't just get a date; you get a realistic buffer number that accounts for human error and integration delays.
Defines the boundaries. The tool forces every stakeholder to state assumptions about resources and scope stability, preventing late-stage 'feature creep' surprises.
See it in action
The Vague Feature Request
A Product Manager proposes a new user dashboard update, estimating it will take 3 weeks. The agent runs this through the MCP and immediately flags 'SCOPE_VAGUE,' forcing the PM to break the work down into database changes, UI components, and data validation layers for accurate timing.
The Overly Optimistic Dev Lead
A Development Lead estimates a complex microservice migration will take 2 sprints. The MCP checks it against historical records, revealing that similar migrations always took 35% longer due to unexpected network latency issues, forcing the lead to adjust the plan.
The Missing Safety Net
A team plans a small internal tool, assuming everything will be straightforward. The MCP runs its check and returns 'NO_BUFFER,' reminding them that even simple tasks require at least a 20% contingency time for unexpected integration hiccups.
The honest tradeoffs
The Single Number Guess
Saying, 'Based on how fast we were last month, this will take two weeks.' This ignores specific technical risks and the complexity of new features.
Instead, run the estimate through validate_estimation. You must map out every dependency, cite a specific historical project, and apply a numerical buffer to prove your timeline.
Ignoring Dependencies
Assuming 'the API documentation will be ready.' This leaves critical path items unquantified, guaranteeing the entire schedule stalls.
Use validate_estimation to explicitly list 'API documentation readiness' as an assumption and assign it a dependency risk level.
Vague Scope Descriptions
Writing 'Build out user authentication.' This is too broad for any reliable timeline, making the estimate useless immediately.
Decompose the scope into parts: 'migrate JWT to OAuth 2.1' (task 1), and 'update React context' (task 2). The MCP will then build a realistic total.
When It Fits, When It Doesn't
Use this MCP if your project involves measurable components, external integrations, or depends on existing technical debt. It is essential for any timeline that crosses team boundaries or relies on older systems. Don't use it if you are doing purely abstract, theoretical research or highly subjective creative work where success criteria aren't quantifiable. If your plan can be summarized in a single sentence and doesn't reference past work, the estimate will fail.
Questions you might have
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.
How do I get started connecting Estimation Prover to my development workflow? +
You connect this MCP via your preferred AI client. Once connected, you can simply prompt your agent with a preliminary estimate for immediate validation. The system routes the scope data through the structured estimation process.
What kind of input does validate_estimation need to run correctly? +
The tool needs more than just a paragraph; it requires four distinct inputs: a task breakdown (decomposition), identified unknowns, specific historical references, and stated assumptions. It processes structured data points, not general text blocks.
Does Estimation Prover handle complex systems with multiple service dependencies? +
Yes, it analyzes dependency chains effectively. For optimal results, however, break the large system into functional micro-milestones first. This ensures each component receives detailed attention regarding its unique risks.
What should I do if my initial estimate contains circular dependencies? +
If a dependency loop exists (e.g., A needs B, and B needs A), the tool flags it immediately. You must manually break the cycle by defining an external service or temporary workaround to allow validation to proceed.
Is my proprietary project scope data secure when using Estimation Prover? +
Your input is processed within a secured, session-based environment. The MCP does not retain sensitive, unvalidated project details after the execution completes. Focus on providing the raw inputs for analysis.
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