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putervision

agent-reasoning-mcp

by putervision

evaluate_situation

Evaluate a situation snapshot to compute expected utilities against active weights, then output prioritized action recommendations for decision-making.

Instructions

Ingest multi-modal situation snapshot, compute expected utilities against active weights, and output prioritized action recommendations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYes
projectNo
snapshotNo
trace_idNo
session_idNo
quick_contextNo
utility_profileNo
candidate_actionsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

C2.7/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'compute' and 'output' but does not state whether the tool mutates state, requires specific permissions, has side effects, or whether it is read-only. The description lacks any statement about what happens to inputs or whether the operation is safe or reversible.

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 a single, grammatically correct sentence with no filler or repetition. It is front-loaded with the core action, and while it is not structured with separate sections, it efficiently conveys the high-level purpose in under 200 characters.

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

Completeness2/5

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

For a tool with 8 parameters, nested objects, and no output schema, the description is far too sparse. It does not explain expected input formats, the meaning of different action variants, how utilities are computed, or what the output looks like. An agent would struggle to construct a valid request without additional documentation.

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

Parameters2/5

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

With schema description coverage at 0%, the description must compensate by explaining the parameters. It offers only vague hints (e.g., 'situation snapshot' likely maps to 'snapshot', 'active weights' may relate to 'utility_profile'), but it does not clarify the meaning of most parameters (e.g., 'action' enum values snapshot/quick, 'project', 'trace_id', 'candidate_actions'). The agent cannot determine how to populate fields correctly.

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 states a clear verb-resource relationship: 'Ingest multi-modal situation snapshot, compute expected utilities against active weights, and output prioritized action recommendations.' This is concrete and distinct from siblings like 'assess_risk' or 'query_knowledge,' though it does not explicitly name an alternative. It conveys the primary function without ambiguity.

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?

The description provides no guidance on when to use this tool versus the siblings (set_goal, replan, assess_risk, etc.). There is no mention of conditions, exclusions, or alternative tools. An agent must infer usage solely from the purpose, which is not made explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.