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GlaserIngo

Stript MCP Bridge

by GlaserIngo

Anonymize a document with Stript

stript_anonymize_file

Anonymize a local document to remove personal data before processing. Placeholders are inserted so original values can be restored later.

Instructions

Use this when the user wants to work with a document that may contain personal data, for example summarize it, translate it, or draft a reply. Ask the user for the file path. Never ask the user to paste document content into the chat. Stript detects and replaces personal data locally and only the anonymized text enters the conversation. Placeholders like [PERSON_1] can be restored later with stript_restore. Set mode to review when the user wants to check the detections in the Stript app first, Stript then opens the document and you wait for the user. Processing a new document uses one of the 5 Free Evaluation documents, already processed documents are free.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoauto anonymizes headlessly, review opens the Stript app for the user firstauto
pathYesAbsolute or ~ path of the document to anonymize
projectNoStript project id, defaults to the configured project

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
nextNo
typesNo
statusYes
warningsNo
evaluationNo
project_idNo
document_idNo
output_fileNo
replacementsNo
residual_riskNo
anonymized_textNo
degraded_reasonsNo
detections_totalNo
reduced_accuracyNo
Behavior4/5

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

The description adds substantial behavioral context beyond the sparse annotations: it explains local detection and replacement of personal data, that only anonymized text enters the conversation, the review mode interaction (opens app and waits), and the quota behavior. It does not explicitly state whether the original file is modified, but the destructiveHint annotation is false, so no contradiction exists.

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

Conciseness4/5

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

The description is longer than the ideal but every sentence contributes unique information (trigger, user instruction, constraint, behavior, restore hint, mode guidance, quota). It is front-loaded with the 'Use this when' guidance and well-structured, earning a high score despite its length.

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?

The tool has moderate complexity (modes, quota, restore, local processing). The description covers the main aspects including when to use, the review flow, quota limits, and the restore path. It does not detail the output format, but an output schema exists, so that is not required. It is sufficiently complete for effective tool selection and invocation.

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 coverage is 100%, so parameters are already documented. The description adds minor context about mode (review opens the app) but this is also in the schema description. It does not significantly enhance understanding of path or project parameters, so a baseline score of 3 is appropriate.

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 clearly states the tool anonymizes a document with Stript, specifying the trigger condition ('when the user wants to work with a document that may contain personal data') and giving concrete examples (summarize, translate, draft a reply). It distinguishes from siblings by mentioning stript_restore for restoring placeholders and describing the review mode.

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?

The description explicitly says 'Use this when...' and provides examples, states to ask for the file path, and gives a strong directive ('Never ask the user to paste document content'). It also clarifies when to use review mode and mentions the free evaluation document quota, giving clear context for appropriate usage.

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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