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LingoHub MCP by usefulapi

Get project file status

lingohub_get_project_status
Read-only

Translation status of one project: each resource file with per-locale segment counts (NOT_TRANSLATED, DRAFT, TRANSLATED, APPROVED, ...), failing quality checks, the export formats it supports and whether a Git repository is connected and can be pushed to. GET .../projects/{project_url}/status.

Input Schema

NameRequiredDescriptionDefault
branchNoProject branch name. Default: the project's default branch. An unknown branch is HTTP 422.
project_urlYesProject URL slug (e.g. "feature-demo"); list_projects returns it as project_url.
workspace_urlYesWorkspace URL slug (e.g. "lingohub-demo"); list_projects returns it as workspace_url.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=true, so the safety profile is covered. The description adds real value beyond that by disclosing the full response shape (segment status buckets, quality-check failures, export formats, Git connection state) and the underlying GET endpoint, which matters given there is no output schema. It says nothing about pagination, rate limits, or response size for large projects.

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?

Two sentences, front-loaded with the core purpose followed by the payload inventory and the endpoint. Every clause carries information, though the trailing 'GET .../projects/{project_url}/status' is mildly redundant with the schema's project_url definition.

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 must characterize the return value, and it does so thoroughly enough for an agent to decide whether to call it. Parameters are fully covered by the schema, so the only shortfall is the absence of guidance on response size or paging for projects with many resource files.

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 description coverage is 100%, so workspace_url, project_url, and branch are all documented in the schema, including the branch default and the HTTP 422 on unknown branches. The description adds no parameter-level meaning beyond restating project_url in the endpoint path; baseline 3 applies.

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 verb-resource pair ('Translation status of one project') and enumerates exactly what the payload contains: per-locale segment counts, failing quality checks, supported export formats, and Git connectivity. It implicitly separates itself from lingohub_get_project (metadata) and lingohub_get_workspace_status (workspace scope) via 'one project', but never names those siblings explicitly.

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 scope ('one project', per-locale counts, quality checks, Git pushability) implies when an agent would want this over get_project or get_workspace_status, but there is no explicit when-to-use/when-not statement or named alternative. Usage must be inferred from the returned-data list.

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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