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Debugg AI MCP

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Create Project

create_project

Create a DebuggAI project by specifying its name, platform, and linking a team and a GitHub repository. Resolves team and repo by UUID or name.

Instructions

Create a new DebuggAI project. Required: name, platform (e.g. "web"), plus a team and a repo. Team and repo each accept EITHER a UUID or a name: pass teamUuid OR teamName, and repoUuid OR repoName. Name resolution does a backend search with case-insensitive exact match (returns AmbiguousMatch with candidates on multiple hits, NotFound on no hit). The repo must be GitHub-linked to the account. Returns {created: true, project: {uuid, name, slug, platform, repoName, ...}}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesProject name. Required.
platformYesPlatform (e.g. "web"). Required.
teamUuidNoTeam UUID. Provide teamUuid OR teamName, not both.
teamNameNoTeam name (backend-resolved, case-insensitive exact match). Provide teamUuid OR teamName, not both.
repoUuidNoGitHub repo UUID. Provide repoUuid OR repoName, not both. Repo must be GitHub-linked.
repoNameNoGitHub repo name, e.g. "org/repo" (backend-resolved, case-insensitive exact match). Provide repoUuid OR repoName, not both.
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses name resolution behavior (case-insensitive exact match, errors like AmbiguousMatch/NotFound), the repo linking requirement, and the return structure. It does not mention auth or rate limits, but for a creation tool this is acceptable transparency.

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?

The description is concise yet complete—no redundant sentences. It front-loads the purpose, then details parameters and potential errors efficiently. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (6 parameters, resolution logic, error handling) and no output schema, the description covers all relevant aspects: required fields, alternatives, backend behavior, and return shape. It is complete for an agent to use correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds significant meaning beyond the input schema: it explains the resolution logic for teamName/repoName, mutual exclusivity of UUID vs name, and the requirement for repo to be GitHub-linked. Schema coverage is 100% with descriptions, but the description enriches understanding.

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 clearly states the tool creates a DebuggAI project, specifies required parameters (name, platform) and optional ones (team/repo variants). It distinguishes from sibling tools like create_environment or create_test_case by focusing on project creation.

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 explains when to use the tool (to create a project) and required inputs. It details alternatives for team/repo (UUID vs name) and a prerequisite (repo must be GitHub-linked). However, it does not explicitly state when NOT to use it or compare to other tools, but the context is clear enough.

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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