MappaIQ

Export file

generate_file

Create a downloadable file for the user. Use when they ask for an export, report, or any file download. Supports: excel (xlsx — the default for any spreadsheet/tabular export), csv (only if the user explicitly asks for CSV), pdf (formatted document), txt (plain text), json. For tabular data prefer type:'excel' with headers+rows — it renders with a bold header row, matching every other export in the app. For reports use pdf with a content string. The file appears as a download button in the chat.

Input Schema

Name	Required	Description
`rows`	No	Data rows for tabular files
`type`	Yes	File format — use 'excel' by default for any spreadsheet export; only use 'csv' if the user explicitly asks for a CSV file
`title`	No	Document title for PDF/Excel
`content`	No	Raw content for txt/json, or narrative body for pdf
`context`	Yes	Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
`headers`	No	Column headers for tabular files (csv/excel)
`filename`	Yes	Filename without extension (e.g. 'comp-summary')
`llm_model`	Yes	The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
`conversation_id`	No	Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 17, 2026

TDQS

A4.1/5.0

Behavior3/5

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

Annotations declare readOnlyHint=false and destructiveHint=false, giving the safety profile. The description adds genuinely useful behavior beyond that: the file renders as a download button in chat and tabular excel gets a bold header row. It does not, however, cover what happens on failure or anything about the analytics-only params.

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 and when-to-use are front-loaded, followed by format guidance; each sentence carries specific information. It is on the longer side but avoids filler, so only marginally less tight than ideal.

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

Completeness4/5

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

With 9 params at full schema coverage and no output schema, the description supplies the routing logic, format defaults, and return behavior an agent needs. It never explains the required analytics params (context, llm_model), but the schema covers those in detail.

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%, so the schema already documents each parameter (including the excel-default note on `type`). The description adds cross-parameter guidance (pair type:'excel' with headers+rows; use pdf with a content string), which is a modest value-add but largely overlaps the enum description.

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 (create/downloadable file) and resource (file/export) and immediately scopes it to the export/download use case. The reader can distinguish it from siblings like import_file. The format enumeration further pins down the scope.

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?

Explicitly says when to use it (user asks for export, report, or file download) and provides format-selection rules with a clear exclusion (CSV only if the user explicitly asks; excel is the default for tabular). Routing among formats is unambiguous.

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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