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List dated events on a project

list_project_events
Read-only

Read the dated timeline of significant events on a project — changes, launches, incidents, external shifts and milestones. Use this to explain what analytics are showing over a date range before drawing conclusions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNoYYYY-MM-DD upper bound on occurred_on.
fromNoYYYY-MM-DD lower bound on occurred_on.
limitNo
clientNo
projectNoName or @handle of the project. Fuzzy-resolved.
client_idNo
project_idNo

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description complements this by clarifying what kind of data is returned (significant dated events with examples), which adds behavioral context beyond the annotations. No contradiction exists.

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

Conciseness5/5

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

Two sentences, front-loaded with the core purpose, followed by a practical usage hint. No filler or repetition of structured data.

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 a read-only list tool with 7 optional parameters and no output schema, the description covers the what and when but leaves some gaps. It does not mention output format, ordering, default limit, or how to choose among client/project/client_id/project_id. The schema covers some of this, but the overall picture is adequate yet incomplete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 43%, so the description should compensate. It mentions a 'date range', aligning with the to/from parameters, but does not explain limit, client, client_id, or project_id. The schema already documents project's fuzzy resolution, so the description adds little parameter-level meaning beyond what is already in the schema.

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 uses a specific verb and resource ('Read the dated timeline of significant events on a project') and lists concrete event types (changes, launches, incidents, external shifts, milestones). This distinguishes it from write tools like log_project_event and update_project_event.

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?

The description gives a clear usage context: 'Use this to explain what analytics are showing over a date range before drawing conclusions.' It does not explicitly state when not to use it or name alternatives, but the context is strong enough for an agent to select it appropriately.

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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