Generative AI & Agentic AIMCP and integration contracts

Version a tool contract without breaking callers

PK
Pankit Kumar
Sr. Data Scientist at Parexel (a Goldman Sachs–backed company) · 20 September 2026 · 2 min read
Technically reviewed by Ishaan Sharma
In this article (5 sections)

Agents and clients can fail silently when a tool renames fields or changes an enum. Classify compatibility from actual caller expectations.

Compare three versions

The MCP contract lab creates authored schemas.

python
from mcp_cases import contract_version_case

result = contract_version_case()
assert result["versions"] == ["1.0", "1.1", "2.0"]
assert result["v1_to_v11_additive"] is True
assert result["v1_to_v2_breaking"] is True

V1.1 adds optional group_by and response source_version; v2 replaces required fields. The check uses sets, not an MCP runtime.

Define compatibility policy

Adding optional input or output may be compatible only if clients tolerate unknown fields. Renames, removed fields, changed types/units or enum meaning are breaking. Semantic changes can break callers even when JSON stays identical.

Publish version and deprecation dates, keep contract tests for supported callers and expose stable errors. Run both versions during migration where justified; do not silently route v1 names to new semantics.

Test recorded interactions

Replay valid/invalid calls from each supported version. Verify output parsing, authorization and business invariants. Record which host/client versions were tested.

The Generative & Agentic AI course connects tool versioning to schema drift and release review.

Exercise

Evolve a tool through one additive and one breaking change. Build compatibility tests, a migration guide and a removal gate based on observed approved clients.

Continue learning

This article is part of the MCP and integration contracts sequence. Use the neighbouring tasks when you need the prerequisite or the next application.

Reference: Semantic Versioning.

PK
Pankit Kumar
Lead Instructor, NeuraPath Academy

Pankit Kumar has 10 years in Data Science & AI, building and shipping production systems in regulated pharma and clinical environments. He is a freelance trainer at Boston Institute of Analytics, AnalytixLabs and Scaler, and has taught this material to thousands of working professionals.

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