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This connector has been deprecated

This connector has been replaced by https://glama.ai/mcp/connectors/io.phas/phasio

import_write

Destructive

Import historic data into Phasio: companies, contacts, addresses, parts with their files, quotes and orders, and the history of an order: routings, production, shipments and messages. You resolve every ambiguity yourself: find existing rows with the query tools and name them {"id": "..."}; give each operation a key (the item's id in the source system) and name rows of this or an earlier import {"ref": ""}. Add operations in dependency order (a company before its address, a part before its order, an order before its production, shipments and messages). The user reviews each operation before the approval. Give an operation a note when the user must know why: for example why you matched an existing row, or what you assumed for a missing value. Workflow: action=create (title) -> action=add (planId, operations [{key, op, args, note?}], in parts; the same key replaces an operation) -> after the last add, action=split (planId): a large plan becomes a queue of batches, each a plan with its own planId, run in order; a small plan stays as it is -> action=dry_run (planId); a dry run splits a large plan on its own, keeps batch 1 under the planId and tests batch 1 only -> fix the errors with add or remove (planId, keys) and run again -> when the dry run has no errors, ask the user to review, approve and apply the plan in Phasio (Imports); you cannot approve or apply -> when the status is APPLIED (import_read action=plan), upload each file: POST a multipart form with the part "file" to its uploadUrl, with your bearer token. Upload ONE file at a time and wait for the answer; each CAD file starts an analysis. The limit is 20 uploads a minute; on HTTP 429 wait one minute. Only the user can undo an applied plan, in Phasio (Imports); you cannot undo. An undo is refused after the first file of the plan is uploaded: ask the user to confirm the applied plan before you upload. Batches: the answer of a batch names its queue and the batch whose turn it is (turn). Work only on the batch whose turn it is: dry-run it and fix it with add or remove on its own planId (a batch takes at most a tenth more operations than the batch size, room for fixes: IMPORT_BATCH_FULL); a later batch that names rows of an earlier one fails its dry run until the user applied that batch. import_read action=operations describes the args of each operation.

Input Schema

NameRequiredDescriptionDefault
keysNo
titleNo
actionYes
planIdNo
operationsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare destructiveHint=true and idempotentHint=false, and the description substantially enriches that: undo is user-only and is refused once the first file is uploaded, uploads are rate-limited to 20/min with a 429 backoff, keys are replace-semantics, and batches form an ordered queue with a 'turn' concept. This is far more behavioral context than the annotations alone convey.

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

Conciseness3/5

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

The purpose is front-loaded and most sentences carry real content, but the body is a single dense paragraph of ~250 words with repeated parentheticals ('in Phasio (Imports)' twice) and arrow-chained steps that are hard to scan. The workflow would benefit from clearer segmentation.

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?

For a complex, destructive, no-output-schema tool this covers a great deal: workflow, batch semantics, upload mechanics, rate limits, and undo constraints. Gaps remain around the concrete return shapes of add/dry_run and the operation args (outsourced to import_read), but an agent has enough to call it correctly.

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?

With 0% schema coverage the description carries the full burden, and it does well: key is defined as the source-system id, ref as a pointer to this or an earlier import, planId as the plan handle, keys as the fix target for add/remove, and note as the user-facing rationale. The nested operation args are deferred to import_read action=operations, which is a reasonable pointer but leaves that parameter family undocumented here.

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 opening sentence names a specific verb (import) and enumerates the exact resource set: companies, contacts, addresses, parts with files, quotes, orders, and order history (routings, production, shipments, messages). It also distinguishes itself from the read counterpart by pointing to import_read for status and operation-arg documentation, so an agent can tell the write and read paths apart.

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 lays out the full workflow (create -> add -> split -> dry_run -> fix -> user review/approve) and explicitly names the boundaries: 'you cannot approve or apply' and 'Only the user can undo an applied plan.' It routes status/arg inspection to import_read and gives error-specific guidance (IMPORT_BATCH_FULL, batch turn ordering), leaving little to inference.

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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