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pi_dict

Define project terms and conventions injected into every Pi Conductor worker; seed before spawning and add verified facts after review.

Instructions

The project dictionary: short term -> definition entries (what a system is called, what it does, where it lives, conventions) that are injected into every worker for that project. Seed it from what you already understand BEFORE the first pi_spawn; after reviewing a worker, add facts you verified (never unverified worker claims). Not for task notes or history. action: show | set (upsert entries) | remove (terms).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dirNoAny directory inside the project (default: session cwd)
termsNoFor remove
actionYes
entriesNoFor set: [{term, definition}]. Definitions are one or two sentences (max 300 chars); name file paths.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A4.3/5.0
Behavior4/5

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

No annotations are present, so the description carries the full burden, and it does disclose the key non-obvious behavior: entries are injected into every worker for that project, and set is an upsert. The verification rule is valuable operational context. It stops short of describing what show returns, persistence scope, or auth, so it is strong but not exhaustive.

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?

Front-loaded with the resource definition, then usage, then exclusion, then action legend. Dense and mostly waste-free, though the first sentence is long and the action legend is somewhat compressed.

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 no output schema and no annotations, the description covers purpose, timing, write policy, and action semantics adequately for a 4-param tool. The remaining gap is the return shape of show and any limits on entry count, which an agent might want.

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 75%, so the schema already documents dir, terms, and entries with per-field descriptions. The description's 'action: show | set (upsert entries) | remove (terms)' reinforces the mapping and adds upsert semantics, but adds little beyond the existing schema text.

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?

States a specific resource ('project dictionary: short term -> definition entries') and what it does (show/set/remove entries that are injected into every worker for that project). It also draws an explicit boundary against siblings ('Not for task notes or history'), so an agent can separate it from pi_digest or task-note tools without opening a schema.

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

Usage Guidelines5/5

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

Gives explicit when-to-use guidance with sequencing ('Seed it from what you already understand BEFORE the first pi_spawn') and a clear post-condition ('after reviewing a worker, add facts you verified (never unverified worker claims)'). It also names an exclusion ('Not for task notes or history'), which is the when-not half.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.