list_annotationsList a project’s annotations, optionally filtered by status, type, or priority. Start here with status "open" to see what needs fixing.
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | ||
| limit | No | Page size, 1-200 (default 50). | |
| offset | No | ||
| status | No | ||
| priority | No | ||
| project_id | Yes | The id of the project to list annotations for. |
| Name | Required | Description | Default |
|---|---|---|---|
| items | Yes | ||
| limit | No | ||
| total | No | ||
| offset | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the tool's safety is clear. The description adds that filters are optional, which is already in the schema, but does not reveal additional behaviors like pagination or default ordering. The annotation burden is mostly carried by structured data.
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, no filler. The main action and a usage tip are front-loaded. Every sentence adds value.
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 6 parameters and an output schema (so return values need not be described), the description covers the main purpose and a filtering suggestion. However, it omits details on pagination, sorting, or unfiltered behavior, leaving some gaps for a complete understanding.
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 coverage is only 33% (2/6 parameters with descriptions). The description mentions filters by status, type, or priority but provides no details about their values, behavior, or the pagination parameters (limit, offset). With low coverage, the description should compensate but does not.
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 clearly states 'List a project’s annotations' with optional filters, distinguishing it from siblings like get_annotation (single) and list_annotation_issues. It also recommends starting with 'open' status, making the purpose specific and actionable.
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 advises 'Start here with status "open" to see what needs fixing,' providing clear context for when to use this tool. It implicitly contrasts with sibling tools for single annotations or issues, but does not explicitly state exclusions or alternatives.
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.
Each tool targets a distinct aspect of annotation or project management. Complementing pairs like diagnose_annotation vs. generate_prompt and get_annotation_analysis vs. diagnose_annotation are clearly differentiated by descriptions. No two tools overlap in purpose.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., list_annotations, create_github_issue, get_activity). Verbs like get, list, create, add, update are used systematically, ensuring predictability.
With 17 tools, the server covers annotation operations, project metrics, comments, GitHub integration, and sharing without being overwhelming. The scope is well-scoped for a specialized feedback/annotation tool.
Core annotation workflows (list, get, update status, comment, diagnose, generate prompt, create issues) are covered. Missing annotation creation, deletion, and detail updates are minor gaps given the server's focus on post-creation analysis and workflow.