SurveysAI Qualitative Analysis – interview and focus group analysis

Quote an analysis price

qualitative_quote_analysis

Read-onlyIdempotent

Prices one complete analysis package from word counts alone and returns the price in credits and USD, the model and reasoning effort that would be used, what is included and which reports are available. Read-only: nothing is stored and nothing is charged. Send counts only (input_word_counts, optionally input_bytes) and never transcript text. Check valid and errors in the result. Always call it, and show the user the price, before qualitative_start_analysis, which needs price_credits, model and effort of this quote. No account needed; with an API key that has credits:read the result also shows the account balance.

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": {
      "preset_id": "<preset_id>",
      "analysis_brief": "<analysis_brief>"
    },
    "name": "qualitative_quote_analysis"
  }
}

Input Schema

Name	Required	Description	Default
`language`	No	Analysis output language, a BCP 47 code such as en, cs or de.	en
`preset_id`	Yes	A preset id from qualitative_list_presets.
`input_bytes`	No	UTF-8 size per input slot in bytes; sharpens the token-based cost floor. Defaults to an estimate from the words.
`analysis_brief`	Yes	The research objective and optional context; the server counts its words.
`report_language`	No	Language of the selected reports, when different.
`selected_reports`	No	Report types from the preset's `reports` (available ones only).
`input_word_counts`	No	Words per input slot (all files of the slot together). Counts only, never content.
`transcript_sources`	No	Number of transcript files (default 1).

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 11, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Goes well beyond the readOnly/idempotent annotations: it states nothing is stored and nothing is charged, that no account is needed, that a credits:read API key additionally reveals account balance, and that callers must check `valid` and `errors` in the result. It also discloses a privacy constraint (counts only, never transcript text) that no annotation could convey.

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-loaded with what it returns, then the invocation rule, then the data-safety constraint, then the auth note — a logical order with no filler. It is dense and sentence-packed, so a slightly lighter structure would read better, but every clause carries information.

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 an 8-parameter, nested-object tool with no output schema, the description still names the key return fields (price in credits and USD, model, effort, inclusions, available reports, valid/errors, optional balance) and the auth/precondition context. Nothing an agent needs to call it correctly and hand off to qualitative_start_analysis 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 100%, so the baseline is 3; the description adds value by singling out `input_word_counts` and optional `input_bytes` as the charging inputs and warning that only counts may be sent. It also links the returned price_credits/model/effort back to qualitative_start_analysis's parameters, clarifying intent beyond field-level docs.

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 (prices/quotes) and resource (one complete analysis package) plus its input basis (word counts alone) and its outputs (credits, USD, model, effort, inclusions, available reports). This is clearly distinguishable from qualitative_get_pricing (static pricing table) and from qualitative_start_analysis (the tool that consumes the quote).

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?

Gives an explicit imperative and ordering: 'Always call it, and show the user the price, before qualitative_start_analysis', and explains why — the start tool needs price_credits, model and effort from this quote. That is a concrete when-to-use rule with the alternative named and the dependency spelled out.

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

All SurveysAI Qualitative Analysis – interview and focus group analysis tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?