What is mcp corpus?
MCP gives agents a standard tool surface. A serious code corpus also needs review queues, source metadata, receipts, revocation and learning from misses.
MCP corpus
An MCP corpus should be more than a vector search box.
MCP gives agents a standard tool surface. A serious code corpus also needs review queues, source metadata, receipts, revocation and learning from misses.
What MCP should expose
The useful tool surface is small: search, retrieve, apply, report issue and learn from outcome. Everything else should support trust and governance, not flood the model with raw chunks.
Private vs productized
A private MCP corpus is often the right internal first version. Moresq Corpus is the productized version when teams need walleting, receipts, quality labels, learning exports and cross-agent portability.
Designed for coding agents
The corpus is tuned for implementation cards, runbooks, snippets and prior fixes rather than generic documents. That makes retrieval actionable inside terminal coding workflows.
From search to an auditable implementation
1. Search
Start with a short intent query. Search is cheap and returns compact metadata before the agent spends tokens on a full retrieval.
2. Retrieve
Open the strongest match only when provenance, license, review state and project fit justify reuse.
3. Apply and learn
Adapt the known pattern, record the outcome and turn misses or corrections into better future retrieval signals.
What Moresq Corpus is not
Corpus is not a promise that every retrieved snippet is correct, a replacement for code review, or a silent training dump of private chats. It is a governed reuse layer: the process verifies provenance and review evidence, while the developer remains responsible for fit, testing and the final change.
Questions developers ask
How does Moresq Corpus help?
Moresq Corpus lets coding agents search reusable implementation cards, retrieve relevant snippets with proof receipts, and learn from misses, applies and corrections.
Does retrieval remove the need for code review?
No. Retrieval can reduce repeated work and expose provenance, but developers still need to check project fit, security implications and test results before shipping.
Related guides
AI coding context loss
Why AI coding assistants lose context, repeat code and forget repo decisions — and how retrieval-first repo memory fixes the workflow.
Agent repo memory
Agent repo memory for Claude Code, Codex, Cursor and MCP clients: retrieve proven implementation patterns before regenerating code.
Verified snippets
Verified code snippets with source metadata, review status, proof receipts and revocation for AI coding agents.
Retrieval-first coding
A retrieval-first coding workflow where agents search known patterns, retrieve compact context and generate only the project-specific adaptation.