entroly-context-engine

render_context_receipt

Convert a Context Receipt JSON artifact into a Markdown audit report for human-readable review. Local, read-only conversion without model calls or source file access.

Instructions

Render a Context Receipt JSON artifact as a Markdown audit report.

Use this after create_context_receipt when a human-readable report is needed. The conversion is local and read-only: it does not call a model, reread source files, recover omitted content, or modify the receipt store. For one omitted chunk use explain_receipt_omission; for exact original content use recover_receipt_omission. The result is Markdown on success and a bounded error string when the JSON is malformed or the receipt shape is unsupported.

Args: receipt_json: The complete JSON receipt artifact, not a receipt ID, excerpt, or already-rendered Markdown.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`receipt_json`	Yes	Complete JSON text returned by create_context_receipt or optimize_context; do not pass Markdown.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv1.0.64Sep 24, 2026

addedInput schema / properties / receipt_json / description

Added value: +"Complete JSON text returned by create_context_receipt or optimize_context; do not pass Markdown."

First observedv1.0.42Jul 4, 2026

TDQS

A4.9/5.0

Behavior5/5

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

No annotations are provided, so the description carries the full burden. It clearly discloses that the conversion is local and read-only, does not call a model, reread source files, recover omitted content, or modify the receipt store. It also describes success and error behavior: Markdown on success, bounded error string on malformed/unsupported JSON. This is thorough behavioral disclosure.

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 front-loaded with purpose, then usage guidance, behavioral caveats, alternatives, and parameter guidance. Every sentence contributes actionable information and there is no filler.

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?

For a one-parameter tool with an output schema present, the description covers purpose, when to use, when not to use, alternatives, behavioral guarantees, parameter constraints, and error behavior. Nothing an agent needs to invoke it correctly 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 meaning beyond the schema. The Args line explicitly says to pass the complete JSON artifact and not a receipt ID, excerpt, or already-rendered Markdown, supplementing the schema's 'do not pass Markdown' guidance with additional exclusions.

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 opens with a specific verb and resource: 'Render a Context Receipt JSON artifact as a Markdown audit report.' It also distinguishes itself from sibling tools by naming explain_receipt_omission and recover_receipt_omission for different purposes, so an agent can tell them apart without opening schemas.

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?

It explicitly states when to use the tool: 'Use this after create_context_receipt when a human-readable report is needed.' It also provides exclusions and alternatives: 'For one omitted chunk use explain_receipt_omission; for exact original content use recover_receipt_omission,' and clarifies this tool does not recover omitted content.

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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