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questdb

mcp-server-questdb

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add_cell

Append a SQL or markdown cell to a QuestDB notebook, returning the new cell ID. Optionally run the cell immediately and receive per-statement status without row data.

Instructions

Append a cell to the notebook. Returns the new cell id and, if run=true, a per-query status array. You never see query rows or column data. Set type:"markdown" to add a prose cell instead of a SQL cell — its sql field then carries the markdown source, it is rendered (never executed), and run is ignored.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
runYesIf true, run the cell immediately after inserting. Response then includes `ran` (true iff every statement succeeded), `queryCount`, and `results: string[]` — one entry per `;`-separated statement, each `"success"`, `"cancelled"`, or `"ERROR: <message>"`. The order matches the cell's SQL. Still no row data. Pass null to skip. Running is DQL-only: a cell containing DDL/DML (INSERT/UPDATE/CREATE/DROP/...) is added but NEVER executed — the response gets `{ ran: false, skipped: true, note: <reason> }`. Markdown cells (type:"markdown") are likewise never executed. Take consent from the user, then call run_cell explicitly.
sqlYesSQL for the cell. May contain multiple statements separated by `;`. In draw mode, multi-statement cells overlay series on a single chart: the first query's timestamp column is the x-axis anchor, subsequent queries contribute additional numeric series merged on the time axis. For a markdown cell (type:"markdown"), this is the markdown source text instead of SQL.
typeYesCell kind. "markdown" creates a rendered prose cell that is never executed (`run` is ignored). null/omitted = "sql".
buffer_idYes
after_cell_idYesInsert after this cell id; pass null to append to the end.
Behavior5/5

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

No annotations are given, but the description fully discloses return values (cell id, per-query status), the absence of row/column data, markdown rendering behavior, and run-flag semantics including the skipped response. This is thorough and exceeds what the schema alone offers.

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 appropriately sized for the tool's complexity. It is front-loaded with the main purpose and each sentence carries important information without fluff or redundancy.

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 no output schema and no annotations, the description covers all essential aspects: return values, execution behavior, edge cases (markdown, DQL-only, skipped status), and usage guidance. It fully enables correct invocation.

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?

Even with 80% schema coverage, the description adds substantial meaning: run is clarified as DQL-only with a consent requirement, sql explains multi-statement draw-mode behavior, and type explains markdown semantics. This goes well beyond 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?

The description clearly states the tool appends a cell to a notebook, distinguishing it from update/delete/run cell siblings. It also explains the markdown variant, which differentiates the two cell types.

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 explicitly directs the agent to obtain user consent and call run_cell instead of setting run=true, and clarifies that DDL/DML and markdown are never executed. This provides clear when-to-use and when-not-to-use guidance.

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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