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io.github.oleksiijko/pmb-ai

io.github.oleksiijko/pmb-ai

by oleksiijko
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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project_overview

Retrieve complete project context in one call: get rules, decisions, goals, recent activity, and related entities for any named project.

Instructions

One-call full context for a NAMED project at the start of work. project_overview(name) → lessons (rules to follow), decisions, open goals, recent activity, related entities.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

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

Input Schema

NameRequiredDescriptionDefault
nameYescase-insensitive substring match against entity names. Picks the highest-mention entity that matches.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.2.2

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries the burden of explaining behavior. It lists the data returned, which is helpful, but it does not disclose whether the operation is read-only, how it handles ambiguous matches, or what happens if no project is found. The parameter schema covers matching behavior, but the description itself lacks these nuances.

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?

The description is a single, compact sentence with an input→output notation. Every element is informative, and it is front-loaded with the key purpose. No redundancy or filler.

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 tool's simplicity (one parameter) and the presence of an output schema, the description sufficiently covers the core function and usage context. It could mention edge cases like no match or error handling, but for a one-call overview tool, the current level is adequate.

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 input schema provides a thorough description of the 'name' parameter (case-insensitive substring match, picks highest-mention entity), giving 100% coverage. The description only restates 'NAMED project' without adding new semantics, so it meets the baseline but does not exceed it.

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 the tool provides 'full context' for a named project, listing specific content areas (lessons, decisions, open goals, recent activity, related entities). This distinguishes it from siblings that focus on narrower aspects like find_lessons or list_goals.

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?

The phrase 'at the start of work' gives a clear use case, and 'one-call' implies an efficiency advantage. However, it does not explicitly mention when not to use it or name alternatives among sibling tools, though the contrast is implicit.

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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