Skip to main content
Glama
Proofpane

Proofpane

Official
by Proofpane

ingest_to_rag

Upload a local file to a governed RAG corpus. The cloud parses it (PDF/DOCX/HTML/text), scrubs secrets, chunks, embeds, and audits—making content searchable for compliance-ready Q&A.

Instructions

Upload a LOCAL file into the Proofpane governance RAG corpus. The cloud parses it (PDF / DOCX / HTML / text), DLP-scrubs secrets, chunks + embeds it, and audits the ingest. After this, the file's contents are searchable with search_compliance_docs on the returned collection. Use when the user wants a local document made available for governed retrieval / Q&A.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYesAbsolute (or cwd-relative) path to the local file to ingest.
collectionNoOptional target collection. Must start with 'ai_gov_' (tenant-namespaced). Omit to use the org's default upload collection.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Addedv0.1.3
  2. Removedv0.1.1
  3. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses the full pipeline (cloud parsing, DLP scrubbing, chunking/embedding, audit) and the post-condition that contents become searchable on the returned collection. Missing details like permissions or failure modes, but this is substantially transparent for an ingestion tool.

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 three sentences, front-loaded with the action and outcome. Every clause adds value: formats, security, processing, and subsequent searchability. No filler or redundancy.

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 two-parameter tool with no output schema, the description explains end-to-end behavior and tells the agent what the caller gets (a collection usable with search_compliance_docs). It is complete enough to select and invoke correctly in most scenarios.

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% and already provides detailed descriptions for both path and collection, including the namespace rule. The description adds a local-file emphasis and the concept of a returned collection, but doesn't materially improve on the schema's parameter guidance, so the baseline 3 applies.

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 states exactly what it does: uploads a local file into the Proofpane governance RAG corpus. It clearly distinguishes from siblings like search_compliance_docs by framing this as the ingestion side and explaining that retrieval happens separately.

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?

Explicitly instructs when to use: 'Use when the user wants a local document made available for governed retrieval / Q&A.' It also points to search_compliance_docs as the retrieval alternative. It doesn't explicitly list when-not-to-use scenarios or exclusions, but the usage context is clear.

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

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Proofpane/releases'

If you have feedback or need assistance with the MCP directory API, please join our Discord server