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ingest_plan

Analyze source text to produce narrative takeaways and a structured plan of proposed actions, letting you review and approve changes before anything is written.

Instructions

Conversational ingest, step 1 of 2. Reads the source, returns the LLM's narrative takeaways and a structured plan of proposed actions — without writing anything yet. The user reviews the takeaways + plan, decides which actions to approve, and then calls ingest_execute(planId, approvals) to commit the approved subset.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textNoRaw source text to ingest (PDF text, URL extract, paste).
titleNoOptional title; LLM will infer if omitted.
raw_idNoRe-plan against an already-ingested raw doc instead of saving new text.
source_urlNoOptional source URL for attribution.
Install Server

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the transparency load. It clearly states the tool reads the source and 'without writing anything yet', which signals a side-effect-free planning pass, and it exposes the user-review/approval gate. It stops short of explaining how/where the plan is stored between the two steps and what exact identifier is returned.

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?

Two tight sentences cover what the tool does, what it returns, the no-write caveat, and the exact next tool call. Every clause adds information and there is no redundant restatement of parameter names.

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?

For a two-step flow with no output schema, it covers the main contract: input, narrative takeaways, structured plan, no persistence, and the follow-up execute call. It doesn't describe the exact output shape or how planId is surfaced, which is the only notable gap.

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 description coverage is 100%, so the schema already documents all four parameters clearly. The description adds only workflow-level context around planId and approvals, not per-parameter semantics, which is acceptable but not extra value.

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 names a concrete pipeline stage ('step 1 of 2'), says it reads the source and returns narrative takeaways plus a proposed action plan, and explicitly clarifies that nothing is written yet. This makes the tool distinct from its sibling ingest_execute and other content-creation tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives clear usage context: use this tool first when the user wants a reviewable ingest plan, and only after approval call ingest_execute(planId, approvals). It does not explicitly compare itself with ingest_source, so alternative selection is slightly incomplete rather than fully explicit.

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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