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io.github.oleksiijko/pmb-ai

io.github.oleksiijko/pmb-ai

by oleksiijko
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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find_lessons

Retrieve procedural rules for a query or project, filtering out lessons from other projects while keeping generic cross-project rules.

Instructions

Standalone 'what procedural rules apply to X'. find_lessons(query, project?) → lessons with surface_id; project scope excludes explicit lessons from other projects while retaining generic rules. FOLLOW them, then mark_lesson_followed.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "find_lessons"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNomax lessons to return (default 5)
queryNotopic to filter by (empty = recent lessons across all projects)
projectNooptional project name; explicit lessons from other projects are excluded while generic cross-project rules remain

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.2.2

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full burden but only partially satisfies it: it discloses the project-scoping behavior (excludes other projects' explicit lessons while retaining generic rules) and the workflow step of marking followed. It does not describe return limits, pagination, or the read-only nature, though 'find' implies a read operation.

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 short and front-loaded with purpose, and the follow-up instruction is direct. It earns efficiency, though the phrase 'Standalone' and the use of 'surface_id' as unexplained jargon slightly reduce clarity.

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 simple schema (3 optional params), an output schema, and clear sibling context, the description is reasonably complete. It covers the core behavior, project scoping nuance, and the expected follow-up action, leaving only minor gaps like parameter details that the schema already fills.

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?

The schema covers 100% of parameters, so the baseline is 3. The description adds minimal meaning beyond the schema by restating query and project semantics, but it does not clarify the limit parameter or provide additional context beyond what the schema already explains.

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 the tool's purpose as 'what procedural rules apply to X' and specifies find_lessons(query, project?) returns lessons with surface_id. This clearly distinguishes it from sibling tools like recall or project_overview, though the phrasing is somewhat informal and the term 'standalone' is ambiguous.

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?

The description implies when to use the tool by framing it as a lookup for procedural rules and adds a follow-up instruction ('FOLLOW them, then mark_lesson_followed'). However, it does not explicitly state when not to use it or mention alternative tools that might be preferable in other contexts.

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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