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Get Agent Details

get_agent_details
Read-only

list_agents returns only one-line summaries — call this to get the full picture for a specific agent. Run-level history is returned here as recent_runs; each entry carries its run id, which you pass to get_run_transcript for that run's full tool-by-tool transcript. Agent-specific operational learnings are returned as a top-level learnings list (managed via append_learning). Dict with agent fields, workspace, learnings, recent_runs, recent_runs_truncated (True when older runs remain past this page of recent_runs), and tracking_summary. When the agent has prospects, tracking_summary.funnel carries the derived outreach rollup the agent workspace shows — per-channel funnels, connection acceptance_rate, reply_rate, and the people-level lead_funnel — so read rates and lead buckets from there rather than deriving them off the raw stage counts. lead_funnel.reached holds the numbers on the funnel's rungs (per rung, everyone who got at least that far); its other stage keys count only the people currently at that stage. mcp_changes lists changes made outside Sliq by the user's or a teammate's MCP clients (such as Claude) calling Sliq tools directly, newest first, one entry per burst of calls to one tool: id, tool_name, client_label (with the teammate's email when a teammate made it), count, first_at, last_at, and latest_arguments (the burst's latest call's arguments). Pass an entry's id as change_id to get_mcp_change_calls to read every call in the burst.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idYesID of the agent to load
recent_runs_limitNoHow many of the most recent runs to include (default 10, max 50)
recent_runs_offsetNoRuns to skip for paging the recent_runs list (default 0). When `recent_runs_truncated` is True, re-call with recent_runs_offset += recent_runs_limit to page into older runs.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedInput schema / properties / agent_id
      Added value: +{
+  "description": "ID of the agent to load",
+  "type": "integer"
+}
    • removedInput schema / properties / task_id
      Removed value: -{
-  "description": "ID of the task to load",
-  "type": "integer"
-}
    • changedInput schema / required
      Previous value: -[
-  "task_id"
-]New value: +[
+  "agent_id"
+]
  2. Added

TDQS

A4.4/5.0
Behavior4/5

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

readOnlyHint=true already establishes the safe-read profile, so the description's job is lighter. It still adds real behavioral context beyond annotations: the recent_runs_truncated flag semantics, the offset-paging recipe, and the note that tracking_summary.funnel holds derived rollups that should be read directly rather than recomputed. It stops short of disclosing rate limits or whether any fields are lazy-loaded.

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?

Long, but front-loaded into summary/returns sections with the purpose and the list_agents contrast in the first sentence. The tracking_summary paragraph is dense and detailed, but each claim (funnel, lead_funnel.reached semantics, mcp_changes fields) is non-obvious and earns its space for an agent reading results.

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

Completeness5/5

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

There is no output schema, so the description must carry the return-value burden, and it does: it names every top-level key, explains recent_runs_truncated, and details the funnel and lead_funnel field semantics. Combined with the sibling routing hints, an agent has everything needed to call and interpret this tool.

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%, and all three parameters are already documented in the schema (agent_id, recent_runs_limit with max 50, recent_runs_offset with paging guidance). The description adds nothing about parameter syntax it doesn't repeat, so the schema does the heavy lifting and the baseline 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 (load full details for a single agent) and enumerates exactly what is returned: workspace, learnings, recent runs, tracking summary. It also explicitly contrasts with the sibling list_agents, which 'returns only one-line summaries', so an agent can distinguish the two without opening either schema.

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

Usage Guidelines5/5

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

Gives an explicit triggering condition ('Use this when the user asks about a specific agent's progress, history, or contents') plus the disambiguating rule against list_agents. It also routes onward correctly: run ids go to get_run_transcript, learnings are managed via append_learning, and change ids go to get_mcp_change_calls.

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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