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list_agent_runs

Read-only

List the organisation's dispatched AI agent runs across every agent, newest first: run id, agent (task) name, status (pending/running/awaiting_user/completed/failed/cancelled/expired/skipped), what triggered it, when it started and finished, and its outcome summary. Long outcome summaries are clipped here and say so where they were cut - call get_agent_run for the full text. Use this to see what the AI team is doing right now or has done recently. Filter by 'task' (agent name or id) and 'status'. Read-only.

Input Schema

NameRequiredDescriptionDefault
taskNoFilter to one agent: its exact name or task UUID.
limitNoMax runs to return (default 20).
statusNoFilter to one run status.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Beyond the readOnlyHint annotation, it discloses a genuine behavioral trait: outcome summaries are clipped here and the response indicates where they were cut, with a pointer to get_agent_run for full text. The closing 'Read-only' merely restates the annotation and adds nothing, but the clipping disclosure is valuable.

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?

Front-loaded with the verb+resource, then the return contents, then the alternative tool, then the filters. Dense but purposeful; the trailing 'Read-only' is the only sentence that does not earn its place given readOnlyHint is already declared.

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

Completeness4/5

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

With no output schema, the description carries the burden of describing return values and does so, including the status enum values and truncation behavior. It under-specifies the default/max result count and pagination, which is the remaining gap for an agent paging through runs.

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 100%, so the schema already documents task, limit and status with their defaults and bounds. The description only restates that 'task' and 'status' are filters and does not mention limit or add format/semantics beyond the schema, so the baseline of 3 applies.

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?

States a specific verb and resource ('List the organisation's dispatched AI agent runs across every agent'), plus ordering ('newest first') and the exact fields returned. An agent can distinguish this from sibling get_agent_run and list_agent_tasks without opening a schema.

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?

Explicitly says when to reach for it ('to see what the AI team is doing right now or has done recently') and routes to get_agent_run when the full outcome text is needed. No explicit when-not or exclusion for other listing siblings, but the context is clear.

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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