list_project_issuesRead the known issues recorded on a project — blockers found in external systems, their severity, status and the tasks fixing them. Read this before you start work so you do not re-discover or duplicate a known problem.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| client | No | ||
| status | No | Defaults to open + in_progress. | |
| project | No | Name or @handle of the project. Fuzzy-resolved. | |
| client_id | No | ||
| project_id | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the safety profile (readOnlyHint=true, destructiveHint=false), and the description's 'Read' verb is consistent with them. It adds useful context about data scope (blockers from external systems) and returned fields (severity, status, fixing tasks). It does not disclose pagination or limit defaulting, but the status default is already documented in the schema.
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 with zero waste: the first states purpose and return scope, the second gives the usage rationale. The action verb is front-loaded and every clause earns its place.
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?
The description partially compensates for the missing output schema by naming the returned fields (severity, status, fixing tasks) and for the read-only annotation coverage by explaining the operational purpose. However, with six parameters and no output schema, the unexplained limit/client/client_id parameters and the name-vs-id targeting ambiguity leave clear gaps for an agent invoking the 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?
Schema description coverage is only 33% (status and project have descriptions; limit, client, client_id, and project_id have none). The description does not compensate: it never mentions limit, client, or the ID parameters, and gives no guidance on when to use a name versus an ID. An agent cannot tell how 'project' and 'client' interact or how limit behaves.
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?
Names a specific verb/resource ('Read the known issues recorded on a project') and clarifies scope via the em-dash: 'blockers found in external systems, their severity, status and the tasks fixing them.' This differentiates it from the write-siblings log_project_issue and update_project_issue, but it doesn't explicitly distinguish it from similar list tools like list_project_events, which are discoverable in the sibling list.
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 an explicit when-to-use directive: 'Read this before you start work so you do not re-discover or duplicate a known problem.' The timing (before starting work) and rationale (avoid duplicating known problems) are clear. It stops short of naming alternatives or exclusions, but the usage context is unambiguous.
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.
Most tools target a distinct resource and action, but several adjacent pairs are easy to confuse: add_comment vs add_progress_note, call_executor vs call_integration, log_client_decision vs update_client_context vs memory_save, and list_context_sources vs list_integrations. The descriptions do disambiguate them, but the boundaries are subtle enough that misselection is likely with 81 tools.
The overwhelming majority follow a clear verb_noun snake_case pattern (create_task, update_project, list_integrations, set_webhook), and get_/ list_/ create_/ update_ families are predictable. Minor deviations exist: memory_read/memory_save/memory_search invert to object_verb, and bare nouns like whoami, glossary, and security_posture break the pattern, but there is no chaotic casing or mixed conventions.
At 81 tools, this is far above the weight that is comfortable for an agent's tool-selection surface, especially since many tools belong to families that could be consolidated (webhooks, worker keys, project logs, context/memory). Although the domain is broad, the count will overwhelm agents and increase misrouting.
The surface is unusually comprehensive: tasks, projects, workers, leases, artifacts, comments, handoffs, webhooks, keys, context, memory, integrations, and support all have create/read/update/delete or equivalent lifecycle coverage. The gaps are minor, such as remove_dependency without a visible add_dependency and no artifact/comment deletion, but agents can work around them.