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jd_list_work_plans

Read-only

Retrieve an organization's work plans filtered by year, field, work type, or status, returning operations and assignment details.

Instructions

List the work plans of an organization, optionally for a year, kind of work or fields.

    Returns `work_plans`, each with `erid`, `fieldUri`, `workType`, `year`, `workStatus`,
    `workOrder`, `instructions`, `sequenceNumber`, `operations` and `assignmentCount`.
    Get `org_id` from jd_list_organizations; the `erid` of a plan is the `erid` for
    jd_get_work_plan.

Input Schema

TableJSON Schema

Name	Required	Description
`year`	No	Plan year, for example 2025. Omit for all years.
`org_id`	Yes	Organization ID from jd_list_organizations.
`field_ids`	No	Only plans for these field IDs from jd_list_fields.
`work_type`	No	Only plans of this kind of work. Omit for all.
`work_status`	No	Only plans in this state. Omit for Deere's default.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 16, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds real workflow context by naming the parent lookup (jd_list_organizations) and the downstream consumer (jd_get_work_plan), though it says nothing about pagination or result limits for a potentially large list.

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?

Front-loaded with the core action, then the handoff note. The enumerated return fields are somewhat redundant given an output schema exists, but the rest is tight and waste-free.

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?

With an output schema present, the description needn't explain returns, yet it does name the key fields when passing erid downstream. Combined with the org_id sourcing instruction, an agent has everything needed to call this correctly in sequence.

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 coverage is 100% and each parameter already carries its own description (e.g., 'Omit for all years', 'Omit for Deere's default'). The description only restates the filter axes ('optionally for a year, kind of work or fields') without adding format or semantic detail, so the baseline 3 applies.

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?

States a specific verb and resource ('List the work plans of an organization') plus the filter dimensions, and explicitly distinguishes itself from the sibling read tool jd_get_work_plan by naming the erid handoff. An agent can tell it apart from jd_get_work_plan without opening either schema.

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?

Gives clear context: get org_id from jd_list_organizations, and use the returned erid with jd_get_work_plan. It does not state when NOT to use it (e.g., retrieving a single plan's detail), but the chaining guidance makes the intended workflow unambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.