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dreamagent_get_chat_status

Read-only

Check the progress and final status of a specific AI development session using its session key. Use this to monitor an asynchronous edit until it completes, fails, or is cancelled — keep checking until a terminal state. The session key comes from dreamagent_chat or dreamagent_list_sessions and is an identifier, not a secret.

Returns (actual fields): 'active=', 'run_status=', 'next_after=' (cursor for the next check), optional 'new_output:' (text produced since the last check), then either "Still working — poll again" or a terminal 'done=true' line: finished (with the final output tail), 'the edit was NOT started: ' (rejected early, e.g. HTTP 402 insufficient credits), or 'stream error' (the connection to the run broke — the server-side run may still have finished; verify with dreamagent_get_edit_progress). run_status values: queued | running | cancel_requested | completed | failed | cancelled | interrupted | unknown.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
afterNocursor from the previous check (0 on the first check).
session_keyYesthe session key returned by dreamagent_chat.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.8/5.0
Behavior5/5

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

Annotations only declare readOnlyHint=true and destructiveHint=false, but the description goes well beyond that by detailing polling behavior, return fields, run_status values, and terminal states. It even discloses edge cases like stream error and the possibility that the server-side run may have finished.

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?

The description is dense but every sentence contributes useful information: purpose, usage, session key origin, return fields, status values, and error handling. While slightly run-on in structure, it is not wasteful and stays relevant for a polling tool of this complexity.

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 (polling, multiple terminal states, error cases), the description is complete. It covers all return scenarios, run_status enumerations, and how to act on stream errors. The presence of an output schema also reduces the need to describe return format further, and the description fills remaining gaps effectively.

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 input schema already covers both parameters with clear descriptions, giving a baseline of 3. The description adds useful context beyond the schema, such as 'is an identifier, not a secret' for session_key and the cursor's role in 'the next check' for after, which enriches the parameter semantics.

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's purpose: 'Check the progress and final status of a specific AI development session using its session key.' It uses a specific verb ('check') and resource ('progress and final status'), and distinguishes it from siblings like dreamagent_get_edit_progress and dreamagent_get_project_status.

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?

It explicitly says when to use the tool: 'Use this to monitor an asynchronous edit until it completes, fails, or is cancelled.' It also provides alternative guidance for error cases: 'verify with dreamagent_get_edit_progress' on stream error, and mentions session key provenance from dreamagent_chat or dreamagent_list_sessions.

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.

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