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The available documentation explains how Sanity webhooks and MCP SDKs behave, but it does not establish that a particular evidence pipeline was built or tested. Rather than claim unverified first-hand results, this guide lays out a practical, documentation-grounded way to capture MCP failures and correlate them with Sanity webhook events.
What an MCP failure trace needs to show
A useful trace should let an operator answer four questions: what operation was attempted, which layer failed, what evidence identifies the request or event, and what result reached the caller. Treat this as an implementation recommendation, not a schema mandated by Sanity or the MCP SDKs.
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- Time and correlation: record timestamps and available request, transaction, or event identifiers.
- Operation: identify the tool or workflow action and the relevant project, dataset, document, or webhook where applicable.
- Outcome: preserve whether the operation succeeded, returned a tool-level failure, failed at the protocol layer, lost its transport, or encountered a downstream webhook problem.
- Sanitized evidence: retain only the input and output detail needed to diagnose the issue, with a deliberate redaction and retention policy.
The exact fields, storage system, retention period, and redaction rules are design choices; the cited documentation does not prescribe them.
Separate the failure layers
The MCP Python SDK documentation distinguishes tool errors, protocol errors, and unexpected exceptions. These are different signals and should not be collapsed into one generic “MCP failed” event. This guidance describes the Python SDK and should not be assumed to define every SDK’s behavior.
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Tool errors
A ToolError communicates a model-visible tool failure. The Python SDK advises using this path when the model might be able to correct its arguments or choose a different action. Preserve the tool name, sanitized arguments, and returned failure so an operator can see what the model was told.
Protocol errors
An MCPError represents a protocol-level error that fails the call. Record it separately from a tool result: it indicates a failure in the MCP interaction rather than simply an unsuccessful tool operation.
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Unexpected exceptions
An unexpected exception is treated as a server crash, and the Python SDK documentation says a traceback is logged server-side. Keep the caller-facing outcome and the operator-side exception trace linked by a correlation identifier, while ensuring logs do not expose secrets or unnecessary content.
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Correlate Sanity webhook deliveries reliably
Sanity documents webhook delivery as at least once, so a receiver can see the same event more than once. Use the supplied idempotency-key to suppress duplicate processing, and preserve the event metadata needed to connect a delivery to the workflow that handled it.
Useful documented headers include:
idempotency-keyfor duplicate suppressionsanity-transaction-idandsanity-transaction-timesanity-datasetandsanity-document-idsanity-project-idandsanity-webhook-idsanity-operation
Sanity also notes that webhook concurrency is limited to one request and that frequent document updates can produce many triggers. Event selection and downstream processing should account for those behaviors rather than assume a one-to-one, exactly-once stream. See Sanity’s GROQ-powered webhooks documentation.
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Choose the right Sanity MCP surface
Sanity documents its hosted MCP server at https://mcp.sanity.io for AI assistants interacting with Sanity projects. Its documented capabilities include GROQ queries, content operations, project management, and migration assistance. OAuth is the default authentication method; API-token authentication is also an option, with permissions governed by the token’s role and scope. Check the current setup and tool documentation because product capabilities and instructions can change. See Sanity’s MCP server guide.
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Sanity Context MCP is a separate, structured, read-only interface to content allowed by its configuration. It is not the write path; Sanity’s reference points to its MCP server for MCP-based writes. When documenting a workflow, name the actual surface and its permissions so a read-only context connection is not mistaken for a content-writing server. See the Sanity Context MCP reference.
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Log and trace with the deployed SDK in mind
Logging guidance depends on SDK, transport, and protocol revision. The TypeScript SDK v2 logging page says MCP protocol-level logging is deprecated in specification revision 2026-07-28; it recommends stderr for stdio servers or OpenTelemetry. The Python SDK documentation describes message tracing through OpenTelemetry and distinguishes logs from spans.
Do not translate either page into a universal rule for every implementation. Record the deployed SDK and version, transport, and protocol revision alongside the logging decision, then use the matching documentation: TypeScript SDK v2 logging, progress, and cancellation and Python SDK logging.
A practical evidence flow
- At the MCP boundary: assign or preserve a correlation identifier, record the operation and timestamp, and classify the outcome as a tool result, protocol error, unexpected exception, or transport/connection issue.
- At the Sanity boundary: capture relevant webhook headers, especially the idempotency key and transaction metadata, without storing more payload content than diagnosis requires.
- Before processing a webhook: check the idempotency key and prevent a repeated delivery from applying the same downstream action twice.
- Across the workflow: link the MCP operation and webhook event through explicit identifiers where available; do not assume a Sanity transaction identifier is automatically an MCP request identifier.
- For diagnosis: retain the sanitized final result or error and, for server-side exceptions, the corresponding operator log or trace. Set redaction and retention rules appropriate to the data and access model.
This is a design pattern derived from the documented error distinctions and webhook metadata, not a report of a measured deployment or a Sanity-prescribed architecture.
What an implementation report should establish
A first-person account of a built pipeline needs evidence beyond product documentation. To substantiate such a claim, an author would need to identify the real components and versions, show a sanitized representative trace, explain how duplicates are handled, and describe verification and data-retention choices. No performance, reliability, or time-saving result follows from the cited documentation alone.
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