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Plumbline — a trust layer for LLM answers

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
employeeYesthe employee whose management chain to return

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
answerYes
claimsNo
refusedNo
coverageNo
questionNo
recoveryNo
answer_idNo
groundingNo
confidenceNo
data_as_ofNo
drafted_byNo
facts_usedNo
unverifiedNo
attributionNo
disclosuresNo
result_setsNo
snapshot_idNo
snapshot_nameNo
stage_timingsNo
correlation_idNo
figure_originsNo
reconciliationNo
refusal_reasonNo
composed_promptNo
drafted_by_sourceNo
evidence_documentsNo
model_observationsNo
suggested_questionsNo
unverified_disclaimerNo
model_reported_evidenceNo

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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TDQS

A4.4/5.0
Disambiguation4/5

Most tools occupy clearly different lifecycle slots: ask_prepare/ask_submit_draft/ask_result form the verification flow, run_sql/run_code/query_fact are distinct execution paths, and cross_check/trust_finalize handle trust outcomes. The main ambiguity is that the ready-made domain tools (search_employee, compare_departments, etc.) resemble a generic ask_prepare flow, but their descriptions explicitly route around this, so the boundary is recoverable.

Naming Consistency5/5

All tool names use lowercase snake_case and are almost uniformly verb-first: ask_, get_, run_, search_, compare_, query_, summarize_, find_, and trust_. Even compound names like ask_submit_draft and get_answer_queries follow the same predictable action-oriented pattern.

Tool Count4/5

Eighteen tools is at the upper end of what is comfortable for an agent, but each maps to a distinct surface area: question preparation, submission, raw query, execution, verification, audit, transparency, and documentation. A few convenience tools could arguably be merged into the generic ask_prepare flow, which keeps this from being a perfect score.

Completeness5/5

The trust-layer lifecycle is fully covered: ask_prepare → draft generation → ask_submit_draft → ask_result/trust_finalize, plus cross_check for corroboration and get_answer_queries/get_evidence_document for audit. There are no obvious dead ends for the intended verified-answer workflow; intentionally restricted browsing and listing are security features rather than missing operations.

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