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timps_run_task

Runs a 10-agent SDLC pipeline for software tasks: covering product management, architecture, coding, review, QA, security, performance, DevOps, and documentation.

Instructions

Run the full 10-agent SDLC pipeline (product manager → architect → code generator → reviewer → QA → security → performance → devops → docs). Use for software development tasks: writing code, fixing bugs, building APIs, etc.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestYesThe development task, e.g. 'Write a REST API for user auth in Python'.
languageNoProgramming language (default: python).python
max_iterationsNoMaximum pipeline iterations (default: 10).
Behavior3/5

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

Annotations are absent, so the description must carry the burden. It mentions '10-agent SDLC pipeline' and the sequential stages, implying a complex multi-step process, but does not describe potential side effects, resource intensity, or failure modes. It adds some value (listing stages, examples) but leaves many behavioral details undisclosed.

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 compact, two sentences, and front-loaded with the key action and pipeline stages. The use case examples are useful and not redundant. Minor redundancy: '10-agent' is repeated in the pipeline stages listing, but still concise.

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

Completeness3/5

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

The description is adequate for a complex tool with full schema coverage and no output schema. It explains the pipeline stages and typical use cases, but given the tool's complexity (10 agents), it could benefit from more detail on expected behavior, iterations, or potential limitations. No output schema means description could clarify return format, but it doesn't.

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

Parameters3/5

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

The input schema already fully describes all three parameters (request, language, max_iterations) with clear descriptions and defaults (100% coverage). The tool description adds no additional parameter semantics beyond what the schema provides, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it runs a full 10-agent SDLC pipeline, specifying the stages (product manager → architect → ... → docs) and giving concrete examples of use cases (writing code, fixing bugs, building APIs). This distinguishes it from sibling tools like timps_dispatch or timps_agent_composer, though it doesn't explicitly name alternatives.

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 provides a clear context: 'Use for software development tasks' with examples, which helps the agent decide when to invoke this tool. However, it does not explicitly state when NOT to use it or mention alternative tools for narrower tasks, but the general guidance is sufficient.

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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