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AiDOOS Virtual Delivery Center

refine_delivery_plan

Destructive

Revise an existing delivery plan based on the user's requested changes. Given a plan_id and instructions such as adjusting the timeline, adding or removing scope, or changing the team composition, this returns the updated plan and replaces the stored plan with the revised version. Use this tool when the user wants to modify a plan created earlier in the conversation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
plan_idYesThe plan_id returned by a prior create_delivery_plan call.
feedbackYesWhat the user wants changed in the plan. Examples: 'compress to 8 weeks', 'remove the QA pod and use AI test generation only', 'add data migration as a separate workstream'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
podsYes
modulesYes
plan_idYesIdentifier for this plan; use it with refine_delivery_plan, get_delivery_plan, or recommend_next_step.
summaryYes
industryNo
ai_agentsNo
total_ausNoTotal Delivery Units across all modules. JSON key is `total_aus` for legacy compatibility.
project_nameYes
timeline_phasesNo
recommended_packYesThe recommended Delivery Pack tier for this plan.
tier_rate_per_du_usdNo$/Delivery Unit rate for this plan size, per the tier-band rate card.
total_delivery_unitsYesPublic-facing alias for total_aus. Same value.
total_cost_usd_projectNoProject-flow USD cost at the tier-band rate.

TDQS

A4.2/5.0
Behavior4/5

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

The description discloses that it 'replaces the stored plan with the revised version,' which aligns with the destructiveHint annotation and adds specific behavioral context beyond the raw annotation. It also notes it returns the updated plan. This is valuable transparency for a destructive operation, though it does not cover rollback or error conditions. No contradiction with annotations.

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 three concise sentences: purpose, mechanism, and usage condition. Every sentence adds distinct value without redundancy. It is front-loaded with the core action and includes a practical usage note. There is no fluff or unnecessary detail.

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

Completeness4/5

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

Given the presence of an output schema, the description does not need to explain return values. It covers the tool's destructive nature, the required parameters, and the appropriate usage condition. It implies the plan must already exist but does not state this explicitly; however, this is a minor gap relative to the overall completeness.

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?

Schema description coverage is 100%, and both plan_id and feedback have detailed descriptions in the schema, including examples. The description in the tool text essentially echoes this information without adding new meaning. Since the schema does the heavy lifting, the baseline of 3 is appropriate.

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 a specific verb ('Revise an existing delivery plan') with the resource and context. It differentiates from sibling tools like create_delivery_plan (creation) and get_delivery_plan (retrieval) by focusing on modification. The mention of 'requested changes' and specific adjustment examples makes the purpose unmistakable.

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 gives an explicit when-to-use condition: 'Use this tool when the user wants to modify a plan created earlier in the conversation.' This clearly distinguishes it from creation or retrieval. However, it does not explicitly name alternative tools or state when not to use it, so it stops short of a 5.

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

A4.3/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: create a plan from a description, estimate cost from work items, retrieve a plan, refine a plan, and advise next steps. The overlap between create and estimate is explicitly resolved in the descriptions, and the rest are unambiguous.

Naming Consistency5/5

All tool names follow a consistent verb-noun pattern (create, estimate, get, recommend, refine) with the noun 'delivery_plan' or related terms. The naming is uniform and predictable.

Tool Count5/5

Five tools is well-scoped for a delivery planning service. Each tool covers a distinct lifecycle step—creation, estimation, retrieval, refinement, and recommendation—without redundancy or bloat.

Completeness4/5

The tool set covers the core lifecycle: create, refine, retrieve, and advise. Minor gaps exist, such as no explicit delete or list-all-plans tool, but these are not critical for the advisory nature of the service and workarounds are possible.

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