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fernandoludvig

RD Station CRM MCP

List RD Station CRM Tasks

rdcrm_list_tasks
Read-only

List CRM tasks filtered by deal, assignee, status, type, and due-date range to find pending items, overdue follow-ups, or scheduled activities across your pipeline.

Instructions

List tasks with filters: deal, assignee, status (pending/done/all, default pending), type (call, email, meeting, task, lunch, visit, whatsapp) and due-date range.

Returns one line per task: status, subject, id, type, due date, assignee, deal.

Use when: "what are my pending tasks?", "overdue follow-ups this week".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number (default 1)
typeNoTask type filter
limitNoResults per page (default 20, max 200)
statusNoTask status (default 'pending')pending
deal_idNoFilter tasks of one deal
user_idNoFilter by assignee (user ID)
due_afterNoTasks due after this date (YYYY-MM-DD)
due_beforeNoTasks due before this date (YYYY-MM-DD)
Behavior3/5

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

Annotations already declare readOnlyHint=true, so the read-only safety profile is covered by structured data. The description adds the flat one-line-per-task output format which is genuinely useful behavioral context beyond the schema. It doesn't specify pagination behavior or the total count semantics, but the annotations carry the main safety burden.

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 compact and front-loaded: first sentence lists filters, second sentence gives the return shape, third provides use-case triggers. Zero wasted words, every sentence earns its place.

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?

For a filtered-list read tool with 100% schema coverage, readOnlyHint=true, and no output schema, the description is appropriately complete. It covers what the tool does, what it returns, and when to use it. The only minor gap is no mention of what happens when no tasks match (empty result behavior), but this is a low-stakes list operation.

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%, so all 8 parameters are already well-documented in the input schema (each has a description and most have defaults/enums). The description adds marginal value by summarizing the filters collectively but doesn't add format or example details beyond what the schema provides. The due_after/due_before date format (YYYY-MM-DD) is already in 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 uses a specific verb+resource ('List tasks') and immediately lists the full set of filter dimensions (deal, assignee, status, type, due date). The return format is clearly described ('one line per task: status, subject, id, type, due date, assignee, deal'), which distinguishes it from sibling deal/contact list tools like rdcrm_list_deals.

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 reference phrases ('what are my pending tasks?', 'overdue follow-ups this week') provide concrete usage context. However, there are no explicit exclusions or comparisons against alternative tools (e.g., when to use create_task vs list_tasks is obvious, but no when-not guidance is given). The natural-language triggers serve as good guidance.

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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