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Manager & Reporting Chain (Verified)

get_manager_chain

Read-only

Return the management/reporting chain for an employee. NOTE: internal drafting is disabled on this deployment. If your client supports MCP sampling, this tool asks YOUR model to draft in the same call (verified server-side); otherwise it returns an explicit refusal, and you should use ask_prepare then ask_submit_draft to draft with your own model.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "employee": "<employee>"
    },
    "name": "get_manager_chain"
  }
}

Input Schema

Name	Required	Description	Default
`employee`	Yes	the employee whose management chain to return

Output Schema

Name	Required	Description	Default
`answer`	Yes
`claims`	No
`refused`	No
`coverage`	No
`question`	No
`recovery`	No
`answer_id`	No
`grounding`	No
`confidence`	No
`data_as_of`	No
`drafted_by`	No
`facts_used`	No
`unverified`	No
`attribution`	No
`disclosures`	No
`result_sets`	No
`snapshot_id`	No
`snapshot_name`	No
`stage_timings`	No
`correlation_id`	No
`figure_origins`	No
`reconciliation`	No
`refusal_reason`	No
`composed_prompt`	No
`drafted_by_source`	No
`evidence_documents`	No
`model_observations`	No
`suggested_questions`	No
`unverified_disclaimer`	No
`model_reported_evidence`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedAug 23, 2026

TDQS

A4.4/5.0

Behavior4/5

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

The description discloses an important deployment-specific behavior: internal drafting is disabled, and the tool may ask the agent's model to draft or return a refusal. This goes beyond the readOnlyHint annotation and helps the agent anticipate and handle the refusal path effectively. It does not contradict the readOnlyHint because the drafting behavior appears non-persistent.

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 leads with a crisp, front-loaded purpose and then adds necessary operational context about MCP sampling and fallback behavior. It is slightly repetitive ('draft with your own model' echoes 'asks YOUR model to draft'), but the information is valuable and not padded.

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?

Given the output schema and annotations already cover return structure and read-only semantics, the description is complete for agent selection. It covers the parameter, the conditional deployment behavior, the explicit refusal case, and the alternative workflow needed to proceed successfully.

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?

The schema already describes the sole parameter as 'the employee whose management chain to return' with 100% coverage, so the description adds no new semantic detail. The baseline of 3 is appropriate; there is no need for additional parameter documentation.

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 first sentence states a clear, specific verb and resource: "Return the management/reporting chain for an employee." This makes the tool's core purpose immediately obvious and distinguishes it from generic search or comparison siblings. The note also references ask_prepare and ask_submit_draft as alternatives, reinforcing the tool's distinct role.

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?

The description gives an explicit conditional guideline: if the client supports MCP sampling, use this tool directly; otherwise, the tool will refuse, and the agent should instead use ask_prepare then ask_submit_draft. This tells the agent exactly when to use this tool and when to switch to alternatives.

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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