Algenta MCP Server

log_decision

Persist a decision to the Decision Memory audit trail. Link to a simulation run_id to bind the full DecisionPlan context. Call record_outcome later to close the feedback loop and measure prediction accuracy. Every logged decision is immutably hashed — no tampering possible.

Input Schema

TableJSON Schema

Name	Required	Description
`run_id`	No	Simulation run_id that produced this decision (from simulate or recommend).
`context`	No	Business context — what was the situation when this decision was made?
`risk_p5`	No	5th-percentile downside at decision time.
`risk_p95`	No	95th-percentile upside at decision time.
`risk_pol`	No	Probability of loss (0–1) at decision time.
`rationale`	No	Explanation of why this option was chosen.
`confidence`	No	Confidence score (0–1) from the simulation.
`result_hash`	No	SHA-256 output fingerprint from the simulation.
`request_hash`	No	SHA-256 input fingerprint from the simulation.
`chosen_action`	Yes	The action that was decided upon.
`expected_value`	No	Expected outcome value at decision time.
`options_considered`	No	All option names that were evaluated.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedJul 12, 2026

TDQS

A4.1/5.0

Behavior4/5

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

No annotations provided; description adds key behavioral details: decisions are immutably hashed with no tampering possible, and implies a write operation.

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?

Three concise sentences front-load the purpose and immediately add value; no gratuitous content.

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

Completeness3/5

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

With 12 parameters and no output schema, the description covers behavioral safety and usage flow but does not explain the return value or provide more detailed context for complex parameters.

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% and all parameters are well-described in the schema; the description adds no additional parameter-level meaning, so baseline score of 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 it persists a decision to an audit trail, links to simulation run_id, and distinguishes from sibling record_outcome.

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?

Provides explicit guidance to call record_outcome later to close the feedback loop, but does not explicitly state when not to use this tool or list 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

C2.7/5.0

Disambiguation4/5

Most tools target distinct resources or actions, but there is some overlap (e.g., run_repository_fix vs run_repository_pipeline vs simulate_repository) that could cause confusion. Overall, descriptions help differentiate.

Naming Consistency3/5

Tool names are primarily snake_case with a verb_noun pattern, but there are inconsistencies (e.g., single-word verbs like 'simulate', 'tokenize', and mixed prefixes like 'preview_', 'product_'). The pattern is readable but not uniform.

Tool Count1/5

With 140 tools, the server is extremely over-scoped for typical MCP usage. This overwhelms agents and suggests poor separation of concerns, likely violating the principle of minimal tool surfaces.

Completeness4/5

The tool set covers a wide range of functionalities including data onboarding, simulation, decisions, repository management, and admin operations. Minor gaps exist (e.g., no update_agent_run), but core workflows are well-supported.

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