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Read a slice of a stored raw output

local_llm_artifact
Read-onlyIdempotent

Fetch an exact slice of raw material behind an earlier result via artifact ref, by line or character range, with sensitive data scrubbed.

Instructions

Return an exact slice of the raw material behind an earlier result, by its artifact ref (a_xxxxxxxx from a trailer): by line (line_start/line_end, numbered) or by character (offset/limit). Use when the digest left out something you need exactly. In PII mode the slice is scrubbed (placeholders); in ASSIST mode only secrets are scrubbed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
refYesArtifact ref from a result trailer, e.g. a_1f2e3d4c.
limitNoMaximum characters to return (character mode).
offsetNoCharacter offset to start from (character mode).
line_endNoLast line to return, inclusive (0 = line_start + 199).
line_startNoFirst line to return, 1-based (line mode; 0 = character mode).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already mark the tool read-only and idempotent, and the description adds meaningful behavior beyond that: line vs character slicing, exact-range semantics, and mode-dependent scrubbing in PII/ASSIST modes. This gives an agent valuable expectations about what the returned content will actually contain.

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?

Three sentences with no fluff. The core action and artifact ref format are front-loaded, slicing modes are grouped, and the usage trigger and scrubbing caveat are stated compactly.

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?

With a rich output schema, full parameter coverage, and clear annotations, the description covers what remains: when to invoke it, how refs are obtained, how slicing modes work, and how output is transformed in different modes. Nothing critical 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, but the description adds useful grouping: line_start/line_end select line mode while offset/limit select character mode. It also clarifies the relationship between the two modes, which the individual schema fields only imply.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific verb and resource: 'Return an exact slice of the raw material behind an earlier result' via an artifact ref. It is inherently distinguishable from siblings like run/status, but it does not explicitly name any sibling or contrast itself with another tool.

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?

The description gives an explicit usage condition: 'Use when the digest left out something you need exactly.' This is clear context, but it does not list alternatives or state when not to use the tool, so it falls short of a full when/when-not comparison.

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