Skip to main content
Glama

Get Project

projects_get
Read-only

Fetch a specific project by ID from your authenticated Cloudeval account to view its details and configuration.

Instructions

Fetch one Cloudeval project by id from the authenticated account's project list.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
baseUrlNoCloudeval API base URL. Defaults to the MCP server --base-url, active profile, CLOUDEVAL_BASE_URL, or the public API.
profileNoCloudeval CLI config profile to read defaults from. Defaults to the server --profile or CLOUDEVAL_PROFILE.
projectIdYesCloudeval project id to fetch.
frontendUrlNoCloudeval frontend base URL for generated links. Defaults to --frontend-url, active profile, CLOUDEVAL_FRONTEND_URL, or public frontend.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYes
dataYesTool-specific result payload.
commandYes
traceIdNo
frontendUrlNo
filesWrittenNo

Schema Changelog

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

  1. First observedv0.38.3

TDQS

A4/5.0
Behavior3/5

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

Annotations already cover read-only, non-destructive, and open-world hints. The description adds the useful context that the project must come from the authenticated account's own project list. It does not discuss error cases or missing-ID behavior, but the annotations and output schema reduce the need for that detail.

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?

A single, tightly worded sentence that leads with the action and resource. It contains no filler, does not restate schema details, and is easy to scan.

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 simple read tool with one required parameter, full schema documentation, annotations, and an output schema, the description provides enough context to invoke it correctly. It could additionally mention that project IDs come from projects_list, but nothing critical is missing.

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?

All four parameters are fully documented in the input schema with 100% coverage, so the description does not need to repeat parameter details. It adds no parameter-specific meaning beyond the schema, which meets the baseline for high schema coverage.

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 uses a specific verb ('Fetch'), names the exact resource ('one Cloudeval project'), and qualifies the scope ('by id from the authenticated account's project list'). This clearly distinguishes it from sibling tools like projects_list by emphasizing a single-ID lookup.

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?

The description clearly states this is for fetching one project by ID within the authenticated account's scope, which gives an agent solid context for when to use it. However, it does not explicitly mention alternatives like projects_list for enumeration or projects_overview for high-level summaries, so it stops short of explicit routing.

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/ganakailabs/cloudeval-cli'

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