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doitintl

DoiT MCP Server

Official
by doitintl

post_insight_results

Destructive

Batch create or update cloud insights with resource-level results, enabling efficient management of recommendations and findings across cloud resources.

Instructions

Manage cloud insights representing recommendations and findings for cloud resources. Creates or updates multiple insights in a single batch request. Each insight in the batch includes its metadata and resource results inline. For granular control over insight metadata and resource results independently, use the single-insight and resource-results endpoints instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultsYesList of insights to create or update.
customerContextNoScope the request to a specific customer by ID. Required for DoiT employees (whose token isn't tied to a single customer); omit for direct customer users.
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=false and destructiveHint=true, so the write/destructive nature is covered. The description adds that the operation is a batch create/update and that insights include metadata and resource results inline. However, it does not disclose upsert semantics, key-based identity, or that immutable fields are silently ignored, which are important behavioral details left to the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is four concise sentences that front-load the core batch operation, then explain the inline structure and point to alternatives. The opening sentence 'Manage cloud insights...' is slightly generic but not wasteful, and the overall structure is easy to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the essential batch aspect, inline resource results, and alternative endpoints. However, it omits customerContext scoping requirements and the upsert/overwrite mechanism, leaving important operational nuances to the schema and annotations. For such a complex tool, the description is adequate but not fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%: both top-level parameters (results, customerContext) and nested properties have detailed descriptions. The description itself does not add parameter-level meaning beyond what the schema provides, so the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the core action: 'Creates or updates multiple insights in a single batch request.' This is a specific verb+object+mode that distinguishes the batch tool from the singular post_insight_result and post_insight_resource_results siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly provides a when-not-to-use condition: 'For granular control over insight metadata and resource results independently, use the single-insight and resource-results endpoints instead.' This names the alternative endpoints, giving the agent clear decision guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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