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rawtreedb

RawTree MCP Server

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by rawtreedb

Insert JSON

insert-json

Insert one or more JSON objects into a RawTree table, which is automatically created on first use. Ideal for ingesting events, logs, traces, and metrics.

Instructions

Purpose: Insert one JSON object or an array of JSON objects into a RawTree table. RawTree auto-creates the table on first insert.

NOT for: Loading data from a public URL (use insert-from-url). Not for transformed URL ingest; transforms only apply to JSON request bodies.

Returns: Insert confirmation, usually { "inserted": <row_count> }. Firehose transform returns request metadata.

When to use:

  • User wants to send events, logs, traces, metrics, or arbitrary records to RawTree

  • You need to create a table by inserting the first row

  • You need to validate that RawTree accepts a payload shape

  • You have OTLP, CloudWatch Logs, CloudTrail, or Firehose JSON that should be flattened by RawTree

Workflow: Choose a table name → send a small representative payload → run describe-table or run-query to verify.

Key trigger phrases: "insert this", "send event", "write to RawTree", "create table with data", "ingest JSON"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tableYesTarget table name. RawTree accepts identifiers like events, traces, api_logs.
dataYesA JSON object or a non-empty array of JSON objects to insert.
transformNoOptional RawTree built-in transform for JSON body inserts: otlp-traces, otlp-logs, otlp-metrics, cloudwatch-logs, cloudtrail, or firehose.
columnsNoFor transform=firehose only: TSV column names matching each Firehose record line.
Behavior4/5

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

With no annotations, the description discloses key behaviors: auto-creates tables, returns insertion confirmation with row count, and explains transform scope. However, it does not clarify whether inserts are idempotent or if duplicates are allowed, leaving a minor gap.

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-organized into labeled sections, front-loads the purpose, and uses concise, information-dense sentences. Every section adds value without redundancy.

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?

Given the tool's complexity (4 params, optional transforms, no output schema), the description covers purpose, return values, usage scenarios, and a workflow. It is mostly complete, though a note on data append vs. overwrite would strengthen it.

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 parameters are fully documented. The description adds marginal extra semantics (e.g., return value format, transform enum list), but these are also inferable from the schema or context.

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 inserts JSON objects into a RawTree table, distinguishes from 'insert-from-url' for URL data, and notes auto-creation of tables, making the purpose highly specific and differentiating.

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?

Provides explicit 'NOT for' scenarios, lists concrete when-to-use cases (e.g., sending events, creating tables), and outlines a workflow with verification steps, offering thorough 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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