Datapoint MCP

get_survey_responses

Retrieve raw per-annotator responses for a survey to see individual answers, spot outliers, and understand disagreement among annotators.

Instructions

Get the raw per-annotator responses for a survey.

check_survey returns aggregated results (consensus, votes, mean/median, ranks). Use this tool when you want to see individual responses from each annotator — useful for spotting outliers, seeing the spread of opinion, or understanding disagreement.

The default view excludes answered rows from chains the annotator abandoned mid-flow or that are still in progress, matching what counts toward your survey's response total. Set the include flags to surface them.

Args: job_id: The job ID returned by create_survey. page: Page number (default 1). per_page: Responses per page (default 100, max 200). include_abandoned: Include answered rows from abandoned chains. include_in_progress: Include answered rows from in-flight chains.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "job_id": "<job_id>"
    },
    "name": "get_survey_responses"
  }
}

Input Schema

Name	Required	Description	Default
`job_id`	Yes
`page`	No
`per_page`	No
`include_abandoned`	No
`include_in_progress`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.0May 30, 2026

TDQS

A4.8/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden. It explains default filtering behavior, the effect of include flags, and implies read-only operation. However, it does not explicitly state that the tool is read-only or describe any authentication requirements or rate limits, leaving minor gaps.

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

Conciseness5/5

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

The description is well-structured with a concise summary, contextual paragraph, and bulleted Args. Every sentence is informative with no redundancy. The front-loaded purpose ensures quick understanding. It is appropriately sized for the tool's complexity.

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?

Given the presence of an output schema (removing the need to describe return values), the description fully covers all aspects: purpose, usage context, parameter details, and behavioral nuances. It is complete for a tool with 5 parameters and no nested objects.

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

Parameters5/5

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

Schema coverage is 0%, so the description must compensate fully. It provides clear descriptions for all 5 parameters in an 'Args' section, including defaults, constraints (max 200), and behavioral implications for boolean flags. This adds significant value over the bare schema.

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 states 'Get the raw per-annotator responses for a survey' with a specific verb and resource, and explicitly contrasts with the sibling tool 'check_survey' which returns aggregated results. This clearly distinguishes the tool's purpose.

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?

The description provides explicit when-to-use guidance: 'Use this tool when you want to see individual responses from each annotator' and lists use cases. It also differentiates from check_survey and explains the default exclusion of abandoned/in-progress responses.

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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