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Glama

Algenta MCP Server

by io.github.thyn-ai

list_decisions

Retrieve the Decision Memory audit trail — all logged decisions, most recent first. Use with_outcome_only=true to see only decisions where actual results have been recorded. outcome_delta = actual_outcome - expected_value: negative means worse than predicted.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number (default 1).
limitNoCanonical results per page (default 20, max 200).
page_sizeNoResults per page (default 20, max 100).
with_outcome_onlyNoWhen true, return only decisions with recorded actual outcomes.

Schema Changelog

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

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

No annotations, so description carries full burden. Discloses sort order (most recent first) and explains outcome_delta formula. Adequate for a read-only list 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 with no waste. First sentence states purpose, second clarifies a parameter, third defines a key field.

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?

With 4 parameters and no output schema, description covers core purpose, key parameter, and includes formula. Could mention pagination but is otherwise complete for a list tool.

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?

Schema coverage is 100%, but description adds value for with_outcome_only (reason to use) and defines outcome_delta. For other parameters, it relies on schema, which is sufficient.

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?

Description clearly states the tool retrieves the Decision Memory audit trail with all logged decisions, most recent first. It distinguishes from sibling tools like get_decision (single) and delete_decision.

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

Usage Guidelines3/5

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

Implied usage for listing decisions, but no explicit guidance on when to use vs alternatives like list_agent_runs or list_runs. No when-not or alternative references.

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.

Resources