Returns a feature by ID or key (#F12): spec, gist, plan tasks (tasks, top to bottom by ord) and bugs as compact lines, employees (users), designs and attachments (attaches) with file IDs, and specRevision - the spec revision needed to edit the spec. Useful when the user asks about a feature, its plan or who works on it (workAssigneeID).
| Name | Required | Description | Default |
|---|---|---|---|
| key | No | Feature key #F12; not with featureID | |
| featureID | No | Feature ID; not with key |
Changes observed during successful MCP inspections.
Input schema / $schemaRemoved value: -"http://json-schema.org/draft-07/schema#"Input schema / properties / key / anyOfPrevious value: -[
- {
- "maxLength": 12,
- "minLength": 1,
- "type": "string"
- },
- {
- "exclusiveMinimum": 0,
- "maximum": 9007199254740991,
- "type": "integer"
- }
-]New value: +[
+ {
+ "maxLength": 12,
+ "minLength": 1,
+ "type": "string"
+ },
+ {
+ "exclusiveMinimum": 0,
+ "type": "integer"
+ }
+]Input schema / properties / key / descriptionPrevious value: -"Feature key #F12 (or F12, 12); not with featureID"New value: +"Feature key #F12; not with featureID"Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the safe read-only, non-destructive, closed-world profile, so the bar is lower. The description adds real behavioral value beyond that: it explains that tasks/bugs come back as compact lines ordered by ord, that attachments carry file IDs, and that specRevision is 'the spec revision needed to edit the spec' – a non-obvious editing prerequisite.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the purpose and then the return payload, with no filler. The enumeration is dense but every item earns its place by compensating for the absent output schema.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description fully carries the burden of describing the return shape and does so exhaustively, while also noting the specRevision prerequisite for editing. Nothing an agent needs to call or interpret this tool correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so both parameters (key and featureID) are already documented including the '#F12' format and the mutual exclusivity. The description restates 'by ID or key (#F12)' but adds no syntax or selection guidance beyond the schema, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource ('Returns a feature by ID or key') and enumerates exactly what is returned: spec, gist, plan tasks, bugs, employees, designs, attachments, and specRevision. This clearly distinguishes it from sibling read tools like feature_list, feature_search, and feature_code_context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides a concrete usage trigger: 'Useful when the user asks about a feature, its plan or who works on it (workAssigneeID).' However, it never names alternatives (e.g., feature_search or feature_list) or states when this tool should not be chosen, leaving sibling selection to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.