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Chainsaw MCP Server

Chainsaw: read result text chunks

chainsaw_result_chunk
Read-onlyIdempotent

Read UTF-8 chunks from a stored result file or oversized JSON row, and resume at next_byte_offset to page through large outputs without loading full logs.

Instructions

Read UTF-8 text chunks of a file or oversized JSON row. Resume next_byte_offset.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
handleYesResult handle returned by a hunt, search, dump or analysis tool.
max_bytesNoContent byte budget per chunk (256-131072); UTF-8 characters are never split.
row_offsetNoSource row index of one oversized JSON row to stream (the row_offset an oversized error reports). Omit to stream the stored file itself.
byte_offsetNoByte position to resume from; pass next_byte_offset from the previous chunk.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, covering the safety profile. The description adds only that it reads 'UTF-8 text chunks' and supports resuming via next_byte_offset, which is minimal context beyond the schema and annotations. No additional behavioral traits such as rate limits or auth needs are disclosed.

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 consists of two short, front-loaded sentences with no wasted words. The core action is stated first, followed by a resume hint, making it easy to parse quickly.

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

Completeness3/5

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

With annotations covering safety and a 100% schema description coverage, the description is adequate but incomplete. It does not explain the workflow context (e.g., obtaining a handle from a hunt/search/dump) or when to choose this tool over other result-reading siblings, leaving an agent to infer its place in the sequence.

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 all four parameters are fully documented in the schema. The description mentions 'next_byte_offset' (which maps to the byte_offset parameter) but adds no syntax or format details beyond what the schema already provides. Baseline 3 applies when the schema does the heavy lifting.

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 states a specific verb ('Read') and resource ('UTF-8 text chunks of a file or oversized JSON row'), which clearly distinguishes it from most siblings. However, it does not explicitly differentiate from other result-reading tools like chainsaw_result_page or chainsaw_result_events. So it is clear but lacks sibling differentiation.

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

Usage Guidelines2/5

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

The description only says 'Resume next_byte_offset', which hints at resume semantics but provides no explicit guidance on when to use this tool versus alternatives (e.g., chainsaw_result_page). There is no when/when-not context or naming of alternatives, leaving usage largely to inference.

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