QuestionPunk

Responses Get Counts

responses_get_counts

Read-onlyIdempotent

Count a survey's responses without fetching any of them: the way to answer "how many responses". Omitted filters count everything, including AI (synthetic) and flagged responses, so say which filters you applied; scope with responseType 'real' and suspiciousLevel 'clean' when the user means real people. Use the same filters with report_get so the numbers describe one population.

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": {
      "surveyId": "<surveyId>"
    },
    "name": "responses_get_counts"
  }
}

Input Schema

Name	Required	Description
`status`	No	completed, abandoned (left before the end) or in_progress. Omit for all.
`dateEnd`	No	Only responses started at/before this time, as Unix milliseconds (13 digits)
`surveyId`	Yes	Survey ID (from survey_list)
`dateStart`	No	Only responses started at/after this time, as Unix milliseconds (13 digits; 2025-01-01T00:00Z is 1735689600000)
`responseType`	No	Real respondents or synthetic (AI) responses. Omit for both.
`suspiciousLevel`	No	Fraud-check filter. 'clean' excludes suspicious responses; 'flagged' is high + medium.
`surveyRevisionId`	No	Only responses collected on this published revision

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 7, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), so the description's job is to add semantics — and it does with the non-obvious default that unfiltered counts include AI/synthetic and flagged responses. That is precisely the kind of hidden behavior that would otherwise cause a wrong answer, and it is disclosed up front.

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?

Front-loads the purpose and the 'no fetching' contrast before the filter caveats, and every sentence carries actionable information. It is dense but not padded; the report_get consistency point is the one item that could be trimmed, though it still prevents population mismatch.

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?

With an output schema present, return values need not be explained, and annotations handle safety. What remains — which population is counted by default and how to align it with reporting — is fully addressed, leaving nothing an agent needs in order to call this correctly.

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 100%, so the baseline is 3; the description earns an increment by explaining the semantic consequence of omitting filters and by recommending the specific real/clean combination for a common intent. It doesn't restate enum values or date formats, correctly leaving those to the 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?

States a specific verb and resource ('count a survey's responses') and explicitly contrasts it with fetching them, so an agent can separate it from responses_list, responses_read, and responses_export without opening a schema. The parenthetical '"how many responses"' pins the exact user intent it serves.

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?

Goes beyond a general when-to-use statement by naming a concrete selection policy ('scope with responseType real and suspiciousLevel clean when the user means real people') and a companion-tool rule ('use the same filters with report_get so the numbers describe one population'). It also warns that omitted filters broaden scope, which tells the agent what not to do.

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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