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What it takes to run a project

deploy_facts
Read-onlyIdempotent

Get pre-deployment facts for an AI project: Docker files, arm64/GPU needs, RAM, external services, ports, README requirements, docs/demo links, and release/security upkeep. Check before installing.

Instructions

The facts an agent needs before installing or deploying one project: Docker files, arm64 build, GPU need, minimum RAM, outside services it needs, web port, what its README says it needs, docs and demo links, and upkeep (latest release, releases in 12 months, security advisories, its package). null means Archestack does not know. Archestack does not track hosting cost or deploy templates.

Input Schema

NameRequiredDescriptionDefault
projectYesProject id like "best-of-ai/qdrant", a GitHub repo like "qdrant/qdrant", or a name like "Qdrant"

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already establish read-only, idempotent, non-destructive, closed-world behavior, so the bar is lower. The description still adds real value by defining the null sentinel ("null means Archestack does not know") and by explicitly delimiting what is NOT tracked (hosting cost, deploy templates), which prevents the agent from misreading absence as a negative answer.

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?

Front-loaded with the purpose, then a dense but purposeful field enumeration, then two short boundary sentences. The long list is justified because no output schema exists, though it stretches into a run-on and could be tightened.

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?

With no output schema, the description correctly compensates by enumerating return categories and defining null semantics, plus stating what is out of scope. Only the absence of any hint about output shape (flat vs nested, one record vs many) keeps it short of 5.

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 the single `project` parameter is documented with three accepted identifier formats, so the schema carries the semantics. The description adds nothing about the identifier itself — baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific resource — the deployment/installation facts for a single project — and enumerates the categories returned (Docker files, arm64, GPU, RAM, external services, port, README requirements, links, release/advisory upkeep). This clearly separates it from a generic project lookup, though the framing is a noun phrase rather than a verb and no sibling is named as the alternative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

"The facts an agent needs before installing or deploying one project" implies the trigger condition, but gives no explicit when-not guidance and never mentions get_project or the other siblings, so the agent must infer which tool to pick when it wants general project metadata versus deploy-readiness facts.

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