DreamAgent Cloud

dreamagent_get_edit_progress

Read-only

Check the latest AI edit progress for a project using its project identifier — no session key needed: the project id retrieves the session holding the latest edit. Use when the user asks whether their edit is finished, when the session key isn't available (e.g. a new conversation), and BEFORE starting any new edit.

Returns (actual fields): 'session_key=' (the identifier of the session this progress comes from), 'edit_active=', 'run_status=', 'chunks=' (output size so far), optional 'recent_output (tail):', and a terminal 'result:' line: finished (with the final output tail), 'the edit was NOT started: ' (rejected early, e.g. HTTP 402 insufficient credits), 'stream error', or 'no run is currently active'. run_status values: queued | running | cancel_requested | completed | failed | cancelled | interrupted | unknown.

Distinct from dreamagent_get_project_status: project status = creation/deployment state (creating/ready/failed); edit progress = current AI modification state; chat status (dreamagent_get_chat_status) monitors one specific session by its session key. If edit_active is true, do NOT launch another edit for the same project.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`project_id`	Yes	the project being edited.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, but the description goes beyond by detailing return fields (session_key, edit_active, run_status, etc.), enumerating possible run_status values, and warning against concurrent edits. This provides a rich behavioral picture that annotations alone do not offer.

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 information-dense but every sentence serves a purpose: purpose, usage, return format, status values, and sibling differentiation. It is structurally front-loaded with the main verb and resource, and the logical flow makes it easy to scan. No wasted words.

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 (multiple statuses, return fields, and sibling tools), the description covers all necessary contexts: when to use, what returns, status meanings, and how it differs from related tools. Even though an output schema exists, the description independently explains the return shape and critical caveats, making it fully complete for an agent.

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?

The schema already provides 100% coverage for the single param project_id ('the project being edited'), so baseline is 3. The description adds value by explaining that the project id retrieves the session holding the latest edit, clarifying why no session key is needed. This enhances understanding beyond the schema's minimal definition.

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 identifies a specific action ('Check the latest AI edit progress for a project') and resource ('edit progress'). It also distinguishes from sibling tools by explicitly noting 'no session key needed' and contrasting with project/chat status tools. This makes the tool's unique purpose immediately clear.

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?

Provides explicit when-to-use guidance: 'when the user asks whether their edit is finished', 'when the session key isn't available', and 'BEFORE starting any new edit'. It also specifies exclusions by comparing to sibling tools and instructs not to launch another edit if edit_active is true. This is textbook usage guidance.

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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TDQS

A4.5/5.0

Disambiguation5/5

Every tool has a clearly distinct purpose: create vs. list vs. status, session vs. edit vs. project, and cancel vs. release lock. The three status tools are explicitly differentiated by what they monitor (session, latest edit, deployment).

Naming Consistency5/5

All tools follow the `dreamagent_verb_noun` pattern, with verbs like create, list, get, cancel, release. Retrieval is consistently split: `get_*` for single statuses and `list_*` for collections.

Tool Count5/5

12 tools is well-scoped for a cloud AI project management platform. Each tool addresses a distinct operation, covering creation, monitoring, editing, sessions, and integrations without unnecessary redundancy.

Completeness4/5

Core workflows are covered: create/list/status for projects, sessions management, edit/cancel/progress, and credential listing. Minor gaps exist—no project deletion, no env var updates—but these are not critical for the primary AI edit and deployment flow.

Resources

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