SPYN3 field notes / MCP + agent infrastructure
The context layer,explained
MCP adoption is accelerating, but connecting an agent is only the visible edge. These field guides explain the retrieval, permissions, provenance and tool design that make connected business data genuinely useful.
Resource library
From signal to system
Read by job, then follow the contextual links. Every page answers one search intent and connects it to the evidence, guide or worksheet that makes it actionable.
News & shifts
News & shifts
Time-sensitive protocol and platform changes, verified against primary sources.
MCP 2026-07-28 stateless specification
What the July MCP specification changes for remote servers, authorization and scaling.
News & shifts / MCP adoption02MCP authorization in 2026
A practical guide to MCP authorization in 2026: issuer validation, issuer-bound credentials, client registration changes and migration priorities.
News & shifts / MCP adoption03ChatGPT custom MCP connectors
What builders need to know about ChatGPT custom MCP connectors, developer mode, tool safety, administration and production-ready data access.
News & shifts / MCP adoption04The official MCP Registry
Understand the official MCP Registry, what server metadata can solve, what discovery cannot guarantee and how builders should prepare for distribution.
News & shifts / MCP adoption05A2A vs MCP
A2A and MCP are complementary agent protocols. Compare agent-to-agent coordination with tool and context access, plus an architecture using both.
How-to
How-to
Practical implementation guides for dependable agent context.
What is Model Context Protocol?
A plain-language explanation of MCP clients, servers, tools and resources.
How-to / Agent data07Connect business data to an AI agent
A step-by-step architecture for connecting files, websites and databases to an AI agent with retrieval, permissions, citations and MCP or REST.
How-to / Retrieval quality08Evaluate RAG retrieval
Evaluate RAG retrieval with representative questions, relevance judgments, recall, precision, ranking checks and failure analysis before generation.
How-to / Tool design09Design reliable MCP tools
Design MCP tools agents can choose and use reliably with clear names, bounded inputs, compact outputs, errors, risk hints and task-based evaluations.
How-to / Tool design10Reduce agent tool-response tokens
Reduce agent tool-response tokens with projection, pagination, snippets, progressive disclosure, structured formats and response budgets.
How-to / Retrieval quality11Build source-aware AI answers
Build source-aware AI answers by preserving provenance through ingestion, retrieval and generation, then validating citations against returned evidence.
Research
Research
Evidence-led reviews that turn retrieval findings into testable decisions.
Hybrid search vs vector search
Compare hybrid search and vector search for RAG: semantic similarity, exact-match retrieval, reciprocal-rank fusion and evaluation criteria.
Research / Retrieval quality13Does chunk size matter in RAG?
RAG chunk size affects retrieval precision, context completeness and cost. Review current evidence and run a corpus-specific chunking experiment.
Research / Retrieval quality14Metadata filtering in enterprise RAG
Metadata filtering narrows enterprise RAG by workspace, permission, source, time and document type before ranking. Learn the architecture and pitfalls.
Research / Security15Prompt injection in connected data
Understand indirect prompt injection in documents, websites and connected data, plus layered defenses for retrieval systems and tool-using agents.
Strategy
Strategy
Architecture choices, trade-offs and informed build decisions.
MCP vs RAG vs API
A decision guide to three layers that solve different architecture problems.
Strategy / Tool design17One MCP endpoint per workspace
Why a small governed surface is more useful than exposing every connector separately.
Strategy / MCP adoption18MCP vs function calling
Compare MCP and function calling: protocol portability, local tool definitions, discovery, authentication and when a product should support both.
Strategy / Tool design19Build vs buy an MCP server
Decide whether to build or buy an MCP server by comparing differentiation, source breadth, authorization, operations, evaluation and total cost.
Strategy / MCP adoption20Remote vs local MCP servers
Compare remote and local MCP servers for deployment, authentication, updates, latency, data access and multi-user governance.
Strategy / Agent data21Agent memory vs RAG
Agent memory and RAG solve different context problems. Compare conversation state, durable user facts, knowledge retrieval and safe storage design.
Templates & tools
Templates & tools
Reusable worksheets, scorecards and checklists for production work.
MCP security checklist
A practical review for permissions, credentials, tool risk and observability.
Templates & tools / Tool design23MCP tool design worksheet
A reusable MCP tool design worksheet for purpose, naming, input schema, output budget, permissions, risks, errors and evaluations.
Templates & tools / Retrieval quality24RAG evaluation scorecard
A reusable RAG evaluation scorecard for retrieval relevance, evidence coverage, faithfulness, answer quality, permissions, latency and cost.
Templates & tools / Agent data25AI data connector requirements
Evaluate an AI data connector for ingestion, permissions, freshness, retrieval, citations, MCP, operations, security and portability.
Build the layer once. Let every agent use it.
SPYN3 is building one secure context endpoint for your workspace: connect sources once, keep permissions and source identity in one place, and expose the useful slice through REST or MCP.
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