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capsulemcp

by soil-dev
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TypeScript
Hybrid

list_entity_tracks

Read-only

List track instances on a specific opportunity, project, or party and compare track definition IDs with apply_track history to separate auto-applied from manual tracks.

Instructions

List track INSTANCES on a specific record — i.e., which tracks have been applied to this opportunity / project / party. Distinct from list_track_definitions, which lists the templates. NOTE: some boards have stage-triggered automation that auto-applies tracks when an entity enters specific stages — tracks returned here may include BOTH manually-applied tracks (via apply_track) and auto-applied tracks from Capsule board rules. To distinguish, compare each track's trackDefinition.id against your application's apply_track call history.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityYesWhich entity type.
entityIdYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv2.3.0
    • changedInput schema / required
      Previous value: -[
-  "entity"
-]New value: +[
+  "entity",
+  "entityId"
+]
  2. Changed2 schema fields changedv2.1.2
    • changedInput schema / properties / entity / description
      Previous value: -"Use 'kases' for projects."New value: +"Which entity type."
    • changedInput schema / properties / entity / enum
      Previous value: -[
-  "parties",
-  "opportunities",
-  "kases"
-]New value: +[
+  "parties",
+  "opportunities",
+  "projects"
+]
  3. Addedv1.7.0
  4. Removedv1.6.2
  5. First observedv1.0.0

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already mark the operation as read-only and non-destructive. The description adds meaningful behavioral detail by warning that results may include both manually-applied and automation-applied tracks, and explains how to distinguish them using trackDefinition.id. This goes beyond what annotations provide.

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 efficiently front-loaded with the core purpose, then gives a sibling distinction and a high-value caveat about auto-applied tracks. Every sentence contributes useful information without padding.

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 absence of an output schema, the description provides key return semantics by mentioning track instances and trackDefinition.id. It covers the main ambiguity around auto-applied tracks. Some additional details such as pagination or full item shape are missing, but for a non-destructive list tool the description is largely complete.

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 schema describes entity via an enum and a short description, but entityId has no schema-level description. The description adds domain context by mapping the call to 'this opportunity / project / party', which partially clarifies the parameters, but it does not explain how entityId should be obtained or what it represents beyond a 'specific record'.

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 and resource: 'List track INSTANCES on a specific record' and explains what those instances mean. It explicitly differentiates from list_track_definitions, making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly names the sibling alternative list_track_definitions and clarifies that this tool returns applied instances, not templates. It also provides important context about auto-applied tracks, helping the agent reason about when and how to interpret results.

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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