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knowledge_document

Destructive

Write or review Markdown documents with evidence-backed context from your project. Save documents or run a delivery check to identify gaps.

Instructions

Evidence-backed document writing/review.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleNo
actionYeswrite=save Markdown;review=delivery check.
contentNo
filenameYes
languageNo
overwriteNo
diagram_modeNo
project_nameNo
client_facingNo
document_typeYes
required_sectionsNo
include_wiki_update_planNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
stateYes
guidanceNo
nextActionYes
resultTextNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv2.9.0
    • changedInput schema / properties / action / description
      Previous value: -"write=save Markdown; review=delivery check."New value: +"write=save Markdown;review=delivery check."
    • removedOutput schema / additionalProperties
      Removed value: -true
  2. First observedv2.7.0

TDQS

C2.5/5.0
Behavior2/5

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

Annotations already indicate this is destructive (destructiveHint=true) and not read-only. The description adds only the vague 'evidence-backed' qualifier and does not disclose key behavioral traits such as overwrite behavior, file-system side effects, or what a review entails. With a destructive tool, more transparency is expected.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short and front-loaded, but it is under-specified rather than appropriately concise. It consists of a single phrase and omits essential context for a tool with 12 parameters and multiple actions.

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

Completeness1/5

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

For a complex tool with 12 parameters, an action enum, destructive annotations, and many siblings, a one-phrase description is far from complete. It lacks guidance on parameter usage, behavioral effects, and selection among sibling tools, making it inadequate for reliable agent invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 8%, and the description provides no parameter-level meaning beyond the schema's single 'action' explanation. The many optional parameters like overwrite, diagram_mode, client_facing, and required_sections are left entirely unexplained, and the description does not compensate for the coverage gap.

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 action ('writing/review') on a specific resource ('document') with a distinctive qualifier ('evidence-backed'). It is clear about the tool's basic function, though it does not explicitly differentiate itself from sibling tools like knowledge_document_context or knowledge_files.

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

Usage Guidelines3/5

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

The phrase 'writing/review' implies when the tool should be used, but no explicit guidance is given about when to choose this tool over alternatives or when not to use it. The description provides an implied usage context, but lacks exclusions or sibling comparisons.

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