fleet_get_issueRead one Heista Linear issue in full by identifier (e.g. "HEI-14") or UUID: title, state, priority, assignee, labels, full description, and recent comments (including the automated scope/fix notes agents leave). Locked to team HEI. Read-only.
| Name | Required | Description | Default |
|---|---|---|---|
| identifier | Yes | Issue identifier like "HEI-14" (preferred) or the issue UUID. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint and idempotentHint. Description adds that it returns recent comments including automated scope/fix notes, and that it's locked to team HEI, providing context beyond annotations.
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 efficient sentences covering purpose, scope, and output. No redundancy, front-loaded with key information.
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?
Given the single parameter and no output schema, the description adequately explains what the tool does and returns. Slightly could specify order or limit of comments, but sufficient for a simple read tool.
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?
The schema has one parameter with description, coverage 100%. The description adds example format 'HEI-14' and mentions UUID as alternative, enhancing the schema's info.
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?
The description explicitly states it reads one Heista Linear issue by identifier or UUID, detailing specific fields returned (title, state, priority, assignee, labels, full description, recent comments). This clearly distinguishes it from sibling tools like fleet_list_issues.
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?
The description clarifies it's read-only and locked to team HEI, implying when to use (fetching a single issue) vs alternatives like fleet_list_issues for multiple issues. No explicit when-not, but clear enough.
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.
Despite the high tool count, most tools have distinct purposes with thorough descriptions that specify when to use each. Some overlap exists among creative direction tools (call_creative_worlds vs chat_with_creative_worlds), but the descriptions clarify usage patterns.
Naming conventions are inconsistent overall: some follow verb_noun (create_powersource_url, decode_ad), others use noun_verb or compound names (adformula_intelligence, fleet_analytics_overview). However, subgroups like dispatch_* and list_*_presets maintain internal consistency.
112 tools is far beyond the typical 3-15 range for well-scoped servers. While the server covers a broad domain, the sheer number likely overwhelms agents and suggests insufficient consolidation of related operations.
The tool set covers core creative intelligence workflows: brand analysis, ad decoding, script generation, creative direction, and research. Minor gaps exist (e.g., no social media publishing tools), but the main use cases are well-supported.