PI Doc MCP
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@PI Doc MCPWhat authentication methods does PI Web API support?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
PI Doc MCP
Ground your AI answers in live AVEVA PI System documentation.
What it does
PI Doc MCP is a Model Context Protocol (MCP) server that gives AI assistants like Claude direct access to the official AVEVA PI System documentation. Instead of relying on training data that may be outdated or incorrect, the AI fetches real answers from live docs — eliminating hallucination on PI-specific topics.
Results are scoped strictly to docs.aveva.com/category/pi-system. Other AVEVA product families (System Platform, CONNECT, etc.) are excluded by design.
Related MCP server: Documentation Retrieval MCP Server (DOCRET)
How it works
The server proxies the publicly accessible docs-be.aveva.com API in real time. No API key, no local doc files, no indexing step — every search and page fetch goes directly to AVEVA's documentation backend and returns content that is always up to date.
Prerequisites
Python 3.11 or later — python.org/downloads
uv — fast Python package manager
curl -LsSf https://astral.sh/uv/install.sh | shClaude Code CLI — installation guide
Installation
1. Clone the repository
git clone https://github.com/naufal-halal/pi-doc-mcp.git
cd pi-doc-mcp2. Install dependencies
uv sync3. Register with Claude Code
claude mcp add pi-docs --scope user -- uv run --directory /path/to/pi-doc-mcp python server.pyReplace /path/to/pi-doc-mcp with the absolute path to the cloned folder.
4. Restart Claude Code to pick up the new MCP server. You should see pi-docs listed when you run:
claude mcp listAvailable Tools
Once registered, Claude has access to three tools:
Tool | Description |
| Search PI System docs by keyword. Optional: |
| Fetch the full text of a documentation page by URL. Optional: |
| List all 90+ PI System documentation bundles grouped by product area (PI Server, PI Web API, Interfaces, Connectors, etc.). |
Usage Examples
Ask Claude questions like:
"What authentication methods does PI Web API support?"
"What are the required tag attributes for the PI RDBMS Interface?"
"How do I configure buffering for a PI Interface on an interface node?"
"Show me the AF SDK getting started guide."
"List all available PI System documentation bundles."
Claude will search the live docs and cite the exact page it used.
Scope
This server covers documentation bundles under docs.aveva.com/category/pi-system, including:
PI Server (Windows and Linux)
PI Web API and AF SDK
PI Vision, PI DataLink, PI Manual Logger
PI Interfaces (OPC DA/HDA, RDBMS, UFL, Modbus, DNP3, Batch, and more)
PI Connectors (OPC UA, MQTT, BACnet, IEC 61850, and more)
Adapters for Edge Data Store
PI Integrators, PI SQL / OLEDB, PI OPC UA Server
Use list_pi_bundles inside Claude to see the full list.
References
Disclaimer
This project is not affiliated with, endorsed by, or supported by AVEVA. It proxies AVEVA's publicly accessible documentation API for personal and developer use. Users are responsible for complying with AVEVA's terms of use.
License
MIT © Naufal — see LICENSE
Available Tools
2 toolsget_pageA
Fetch the text of a PI System documentation page by URL.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | docs.aveva.com page URL from search_pi_docs | |
| max_chars | No | Max characters to return (default 4000, max 12000) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided. The description only states that it fetches text, without disclosing behavioral traits such as error handling, rate limits, whether it follows redirects, or what happens if the URL is invalid. This leaves significant gaps for the agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no wasted words. It is front-loaded with the action and resource, and is appropriately sized for the tool's simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple fetch tool with two parameters and no output schema, the description is reasonably complete. It could mention the dependency on search_pi_docs for the URL, but the schema parameter description includes this. The description is adequate for a straightforward tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (both parameters have descriptions). The tool description adds minimal meaning beyond the schema; it repeats the fetch action. Baseline 3 is appropriate as the schema already documents the parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Fetch the text') and the resource ('a PI System documentation page by URL'). It distinguishes itself from the sibling tool 'search_pi_docs' by specifying a different operation (fetching vs searching).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage after obtaining a URL from search_pi_docs (as noted in the schema for the 'url' parameter), but does not provide explicit when-to-use or when-not-to-use guidance. No alternatives are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_pi_docsA
Search AVEVA PI System documentation (scoped to docs.aveva.com/category/pi-system). Returns titles, URLs, and excerpts.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search terms, e.g. 'Kerberos authentication' or 'configure PI Interface buffering' | |
| bundle | No | Optional bundle ID to restrict search, e.g. 'pi-web-api', 'af-sdk', 'pi-server-f' | |
| n_results | No | Results to return (default 5, max 20) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It correctly indicates a search operation with no mention of destructive behavior, but fails to disclose details like authentication, rate limits, or whether results are live or cached. The behavior is adequately described for a read-only search.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with purpose, and contains no wasted words. Every sentence adds essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 3 parameters with full schema coverage, no output schema, and a simple sibling, the description covers the key aspects: scope, input (query, optional bundle, number of results), and output format. It could mention pagination or error handling but is sufficient for a search tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All parameters are fully described in the schema (100% coverage). The description adds no additional meaning beyond the schema's parameter descriptions, so it meets the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Search') and resource ('AVEVA PI System documentation') with explicit scoping to docs.aveva.com/category/pi-system. It also states the return fields (titles, URLs, excerpts), clearly distinguishing from sibling 'get_page' which likely retrieves a single page.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for searching PI system docs but does not explicitly state when to use it versus the sibling 'get_page' or provide exclusions. Usage context is clear but lacks explicit guidance on alternatives or when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
2 tool updates
v0.1.0- First observed
get_page - First observed
search_pi_docs
TDQS
The two tools have clearly distinct purposes: search returns a list of relevant pages, while get_page retrieves the full text of a specific page. There is no overlap or ambiguity.
Both tools follow a consistent verb_noun pattern: 'search_pi_docs' and 'get_page'. The naming is predictable and clear.
With only two tools, the set is minimal but appropriate for a documentation server focused on search and retrieval. It covers the core operations without being overbearing.
The set provides search and full-text retrieval, which are the primary operations for a documentation server. Missing features like browsing by category or listing all pages are minor gaps that do not critically hinder agents.
Maintenance
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