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Send feedback to the team

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Send feedback about Datailo to the team that builds it: a bug, a wrong answer, missing data, a missing feature, a workflow idea, or praise. Every item is read by a person. When to offer it: an answer came back empty or wrong; the user corrected you or the result; a workflow was missing a step the user needed; the user repeats a manual step the service could do for them; the user says they need something the service does not do. Offer once, in one sentence; the user's task comes first. Ask first. Show the user what you will send and send it only after they agree. Their own words, and any client, case, company or personal detail, go only with their explicit OK; then set confirmed=true. With authored_by='agent' you may report your own observation of the service (the tool, what you expected, what came back) without asking, as long as it holds none of the user's words or confidential content. Fill kind and what (the task, and what went wrong or what is needed). For a wrong answer add expected and got. Add tool and query for context (the query only with the user's OK), severity, and contact_email only when the user wants a reply. service: 'datailo' (or omit it) for the hub itself; 'avokata', 'victano', 'rekvira' when the feedback is about that service: Datailo passes it on to that service's team. The reply carries a reference id. Tell the user it reached the team; do not promise a reply or a date.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
gotNoFor a wrong answer: what came back instead
kindYesOne of: bug (something failed or errored), wrong_answer (an answer was returned but it was wrong or misleading), missing_data (a document, record or source the user expected is not covered), missing_feature (something the user needs that the service does not do), workflow_idea (a step, sequence or repeated manual task the service could handle), praise (something that worked well and should be kept), other (anything else)
toolNoThe tool the feedback is about, if one (e.g. the tool that answered wrongly); several: comma-separated
whatNoThe task and what went wrong or what is needed (3-2000 characters)
queryNoThe query or arguments that produced it; only with the user's OK
messageNoOlder name for `what`, kept so earlier callers still work; prefer `what`
serviceNoHub only: 'datailo' (default) or the service the feedback is about
sessionNoThe `session` value datailo_connect_verify returned. Pass it on every Datailo call in this conversation; omit it when the user connected with Sign in (OAuth) or a key.
expectedNoFor a wrong answer: what the right answer or result would have been
severityNolow | normal | high | blocking (blocking: the user cannot do their work)
confirmedNotrue once the user approved sending this; required for authored_by user or agent_drafted
authored_byNouser (their words) | agent_drafted (you wrote it, they approved) | agent (your own observation)
contact_emailNoOnly if the user wants a reply: the address to reply to

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / tool / description
      Previous value: -"The tool the feedback is about, if one (e.g. the tool that answered wrongly)"New value: +"The tool the feedback is about, if one (e.g. the tool that answered wrongly); several: comma-separated"
  2. Added

TDQS

A4.8/5.0
Behavior5/5

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

Goes well beyond the annotations: it discloses the consent workflow (ask first, show the payload, send only on agreement), the privacy boundary (user's words and client/case/company details only with explicit OK), the authored_by='agent' exception, that every item is read by a person, and the return contract (reference id; do not promise a reply or date). Annotations only say openWorldHint=true and not-idempotent, so the description carries and delivers the real behavioral burden.

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?

Purpose is front-loaded, then triggers, then the consent process, then field guidance. Dense but each block is functional rather than decorative. Slightly long for a single tool, though the consent/privacy material justifies most of the length.

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

Completeness5/5

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

For a 13-parameter, consent-sensitive, write-class tool with no output schema, the description covers triggers, approval gating, privacy handling, service routing, field selection, and the response contract. Nothing an agent needs to call it safely is missing.

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

Parameters4/5

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

Schema coverage is already 100%, so the baseline is 3. The description exceeds that by giving conditional usage rules the schema states flatly: add expected/got only for a wrong answer, tool/query for context (query only with consent), contact_email only if the user wants a reply, service='datailo' or a named downstream service, and confirmed=true tied to the approval workflow.

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?

Specific verb+resource (send feedback about Datailo to the team that builds it) plus an enumeration of exactly what counts (bug, wrong answer, missing data, missing feature, workflow idea, praise). No sibling tool in the list covers feedback, so the scope is unambiguous.

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?

An explicit 'When to offer it' trigger list (empty/wrong answer, user correction, missing step, repeated manual task, unmet need) plus guidance to offer once, in one sentence, with the user's task first. This is as close to operator instructions as a description gets.

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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