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Record a known issue on a project

log_project_issue

Record a significant issue you discovered while working a project — especially one that lives in an external system and will disappear once dismissed (a Google Ads policy warning, a GA4 tagging error, a broken feed). Paste the exact wording into detail so it survives, then create a task to fix it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoDeep link to the issue, if it has one.
titleYesOne-line statement of the issue.
clientNo
detailNoFull text of the issue, verbatim where possible.
sourceNoWhere you found it, e.g. 'Google Ads', 'GA4', 'Shopify'.
projectNoName or @handle of the project. Fuzzy-resolved.
task_idNoTask created to fix this, if it already exists.
severityNo
client_idNo
project_idNo
discovered_onNoYYYY-MM-DD. Defaults to today.

TDQS

A4/5.0
Behavior4/5

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

Annotations only say readOnlyHint=false and destructiveHint=false. The description adds meaningful behavioral context: the issue is persisted so it survives dismissal, and the tool also creates a task to fix it. It does not disclose every side effect, but it goes beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is only two sentences and front-loaded with the core purpose. The examples and the detail/task guidance earn their place, though the second sentence packs in a lot and could be slightly more structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an 11-parameter tool with no output schema, the description explains the key scenario and the most important action, but it does not clarify many optional parameters, the mechanics of task creation, or what the agent can expect after invocation. It is adequate but leaves notable gaps.

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?

Schema description coverage is 64%, and the description adds useful semantics for detail ('Paste the exact wording ... so it survives') and hints at task creation. However, several parameters like url, client, source, severity, and client_id are left to the schema alone, and the description does not fully compensate for the coverage gap.

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 states a specific verb and resource: 'Record a significant issue you discovered while working a project.' It also gives concrete examples (Google Ads policy warning, GA4 tagging error, broken feed) and clarifies this is for discovered issues, distinguishing it from generic project event logging.

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

Usage Guidelines4/5

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

It clearly explains when to use the tool: for significant issues, especially those living in external systems that may disappear once dismissed. It also gives a concrete workflow instruction: paste exact wording into detail and create a task to fix it. However, it does not explicitly name alternatives or say when not to use this tool.

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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TDQS

A3.7/5.0
Disambiguation3/5

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.

Naming Consistency4/5

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.

Tool Count2/5

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.

Completeness4/5

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.

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