Retrieves raw output from a completed task as plain text for LLM analysis. Accepts project and task IDs to fetch the result.
Get raw output from a completed task for LLM analysis.
Args: project_id: ID of the project task_id: ID of the task
Returns: Raw task output as plain text
| Name | Required | Description | Default |
|---|---|---|---|
| task_id | Yes | ||
| project_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v1.0.4Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. Description states 'completed task' implying prerequisite, but doesn't disclose other behaviors like error handling, authorization, or side effects. Adequate for a simple get but could be more thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Concise with clear Args and Returns sections. However, Returns section is minimal (single line). Every sentence earns its place, but structure could be improved with bullet points.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool has output schema but description only says 'Raw task output as plain text.' No mention of error conditions, prerequisites beyond 'completed task', or response format. Adequate but not fully complete given complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, meaning description adds minimal value beyond schema. Args section merely repeats parameter names without additional semantics like format, constraints, or examples. Does not compensate for low coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Get raw output from a completed task for LLM analysis.' Verb 'get' and resource 'raw output' are specific. Distinguishes from siblings like 'get_task' by specifying 'raw output' and 'completed task'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. The purpose implies it's for raw output, but no when-not or alternative names given. Lacks context for selection among many list/get tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.