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knaisoma

data-olympus MCP server

KB Consult

kb_consult

Record a consultation to retrieve governing rules for an intent before starting code or architectural work. Surfaces pending maintenance items for operator confirmation.

Instructions

Record a consultation for (source_session, workspace) and return the governing rules for the intent. Call before code/architectural work. trigger is 'explicit' (default: a deliberate consult, clears the gate) or 'prompt_hook' (an installer auto-consult: audited, never clears).

Retrieval is hard-filtered to the in-force class (active/accepted/ approved, within its validity window, and never a memory-inbox doc): an unreviewed proposed memory, a retired/superseded decision, an expired doc, or a legacy/forged inbox file is never returned as a governing rule.

pending_actions, when present, lists open maintenance items (missing status fields, recently-expired/expiring-soon docs); omitted when the corpus is clean. Surface it to the operator and act on it only with operator confirmation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
intentYesNatural-language search query.
triggerNoConsult trigger: explicit or prompt_hook.explicit
workspaceYesProject or workspace key in the KB.
agent_identityYesHuman-readable agent identity for audit events.
source_sessionYesStable id of the agent session making the call.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Annotations are all false, so description carries full burden. It details hard-filtering logic (in-force class, excluded docs) and pending_actions behavior. Exceeds minimal disclosure requirements.

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?

Well-structured with front-loaded purpose, then trigger explanation, filtering details, and pending_actions note. Every sentence adds value without redundancy.

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 5 parameters, output schema, and sibling tools, the description provides thorough context: when to call, filtering rules, and return behavior (governing rules and optional pending_actions). No gaps remaining.

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 coverage is 100% with descriptions for each parameter. The description adds context for the trigger parameter and overall purpose but doesn't significantly enhance per-parameter meaning beyond schema. Baseline 3 is appropriate.

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 clearly states the tool records a consultation and returns governing rules for the intent, with specific verb+resource. It distinguishes from sibling tools like kb_search or kb_get by emphasizing its role in pre-work consultation.

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 to call before code/architectural work and explains the two trigger types. While it doesn't explicitly list when not to use it, the context is clear. No mention of alternatives but the sibling list provides contrast.

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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