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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

Schema Changelog

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

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds substantial behavioral context beyond this: exact return fields ('active=', 'run_status=', 'next_after='), terminal states including early rejection reasons like HTTP 402, the meaning of stream error, and the full run_status enumeration. It also clarifies the session key is 'an identifier, not a secret,' which is non-obvious behavioral information.

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 longer than the calibration high example but stays dense and informative. It front-loads the core purpose, then organizes return values and run_statuses in a structured way. Every sentence contributes needed behavioral detail, though a slight trim could improve scannability without losing 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 (polling logic, multiple terminal states, error conditions, and cursor handling), the description is remarkably complete. It explains the fields returned, the meaning of each run_status, how to handle stream errors, and what 'done=true' lines indicate. Even with an output schema present, this description fully equips an agent to use the tool correctly in a polling loop.

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?

Schema description coverage is 100%, so both parameters are already documented. The description adds value by stating the session key comes 'from dreamagent_chat or dreamagent_list_sessions' and reinforcing the `after` parameter as a cursor from the previous check. This elevates it above the baseline 3 but does not add entirely new syntax or format details beyond the schema.

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 checks 'the progress and final status of a specific AI development session using its session key,' with a specific verb and resource. It distinguishes itself from siblings by explicitly positioning this as the tool for monitoring an asynchronous edit until terminal state, contrasting with dreamagent_get_edit_progress for verification after stream errors.

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?

The description gives explicit when-to-use guidance: 'Use this to monitor an asynchronous edit until it completes, fails, or is cancelled — keep checking until a terminal state.' It also names an alternative tool (dreamagent_get_edit_progress) for the stream error case and states the session key source, providing clear context for when to invoke this tool versus siblings.

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

A4.6/5.0
Disambiguation5/5

Each tool targets a distinct resource and action: project creation, listing, status checks, edit progress, session management, and integration queries. The status tools (get_chat_status, get_edit_progress, get_project_status) are carefully differentiated by their input and purpose, so there is no real ambiguity.

Naming Consistency5/5

All tool names follow a consistent pattern of 'dreamagent_' plus a verb_noun structure (e.g., create_project, list_sessions, get_edit_progress). This makes the entire set predictable and easy to navigate.

Tool Count5/5

With 12 tools, the set is well-scoped for managing DreamAgent projects and edits. Each tool covers a necessary operation without redundancy or bloat, staying comfortably within the ideal range.

Completeness4/5

The tool set comprehensively covers project creation, listing, status monitoring, AI editing, session management, and integration inspection. The notable gap is the lack of an update or delete project operation, but the core lifecycle and editing workflows are well covered.