learning-loop-mcp

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Learning Context

learning_context

Retrieve lessons learned for a project and task area as a legacy list or consolidated cross-source package, enabling AI agents to access verified solutions without loading full files.

Instructions

Returns a legacy lesson list or an explicit cross-source v2 package.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No
`tarea`	Yes
`project`	Yes
`kanban_board`	No
`max_distance`	No
`context_version`	No
`with_embeddings`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedv0.5.4Sep 8, 2026

addedInput schema / properties / context_version

Added value: +{
+  "default": 1,
+  "type": "integer"
+}

addedInput schema / properties / kanban_board

Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null
+}

First observedv0.4.0Sep 6, 2026

TDQS

C2.6/5.0

Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits, but it only says 'Returns', implying a read operation. It does not explicitly state read-only behavior, side effects, prerequisites, or how the legacy versus v2 mode is chosen, which is a significant transparency gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence with the verb front-loaded, which is structurally efficient. However, it is too sparse for a tool with seven parameters and two output modes, so it earns the middle score rather than higher.

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?

With seven parameters, zero schema descriptions, and no annotations, the description is severely under-specified. The output schema covers the return shape, but the two modes and their selection criteria are unexplained, and no sibling differentiation is given.

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?

Schema description coverage is 0%, so the description must compensate, but it does not explain any of the seven parameters. The phrase 'cross-source' vaguely hints at the project/tarea inputs, but there is no mapping to context_version, kanban_board, max_distance, or with_embeddings.

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 uses a specific verb ('Returns') and names a resource ('lesson list' or 'cross-source v2 package'), so it is not a tautology and conveys a general function. However, it does not differentiate from sibling tools like search or solutions, and the terms 'legacy' and 'explicit cross-source v2 package' are jargon that leave the exact scope unclear.

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?

There is no guidance on when this tool should be used versus alternatives such as search, solutions, or learning_status. The mention of two output modes implies some selection condition, but the description never states it, leaving the agent without direction.

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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