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execute_flow

Routes user queries through epistemic signals to automatically select and execute the appropriate pipeline for fast answers, verification, compilation, change-driven events, or self-improvement.

Instructions

Execute a full canonical epistemic flow end-to-end.

Routes the query through the Epistemic Ingress Controller (4 signals: intent, belief coverage, freshness, risk), then chains the appropriate pipeline steps automatically:

① Fast Answer: Belief → Action ② Verify Before Answer: Belief → Verification → Action ③ Compile On Demand: Truth → Belief → Verification → Action ④ Change-Driven: Event → Truth → Belief → Verification → Action ⑤ Self-Improvement: Misses → Verification → Evolution → Belief

Args: query: The user query or event description diff_text: Raw diff for change-driven flows (Flow ④) is_event: True if this is a change-driven event event_type: Type of event (pr, commit, release, incident, scheduled)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
is_eventNo
diff_textNo
event_typeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Without annotations, the description partially discloses behavior by outlining the internal pipeline steps and mentioning the involvement of belief and action. However, it does not state side effects (e.g., writes to a belief store), permissions needed, or concurrency implications.

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 is well-structured with a clear first sentence, bullet points for flows, and an args list. It is somewhat lengthy but each part serves a purpose. Front-loading of the main function is effective.

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?

Given the tool's complexity (4 parameters, no annotations, output schema exists), the description provides a solid overview of the orchestration process and parameter roles. It lacks details on error handling or prerequisites, but overall it is sufficient for an AI agent to understand the tool's purpose.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description includes an 'Args' section with brief descriptions for all four parameters (query, diff_text, is_event, event_type), adding meaning beyond the schema's titles. Although the context signals indicate 0% schema coverage, the description actually covers all parameters adequately.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool executes a full canonical epistemic flow end-to-end and lists the five specific flow types. It distinguishes from sibling tools like epistemic_route or compile_beliefs by being the orchestrator, but does not explicitly highlight the differentiation.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives. The description explains the internal routing and flow types but does not specify prerequisites, exclusion criteria, or mention sibling tools for comparison.

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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