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fenbs_get_context

Read-onlyIdempotent

Read the AI context for this board before touching the work. Call it straight after fenbs_whoami. It starts with the Rules -- decisions people made that hold from now on, shown in full; they are binding, so follow every one. Then, to pick the work up: the project (what it is, its status), Where we left off, In progress (where each task stands; "· 2 files" means files are attached to it -- fenbs_get_item lists them), Waiting on you (open decisions, needs-check tasks, routines due, link actions due) and the Glossary -- start from there. Then context notes (facts earlier assistants worked out), new lessons, a count of other decisions, and what is pre-approved for AI. Pass project for that project's status, where it was left, rules, words and notes -- only ask for the project you are working on.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectNoThe project you are working on, by name. Omit for the notes that apply to everything.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, so the safety profile is covered. The description adds meaningful behavior beyond that: the Rules are 'binding, so follow every one', the '· 2 files' convention, and what each returned section means. It does not cover pagination or size limits, keeping it just short of 5.

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 purpose and sequencing, then walks the returned sections in order. It is dense and long, but with no output schema nearly every clause earns its place by explaining what comes back; the section-by-section inventory is the only place length feels close to excessive.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and a single optional parameter, the description fully compensates by enumerating the return structure (Rules, project, Where we left off, In progress, Waiting on you, Glossary, notes, lessons, counts, pre-approved). An agent has everything needed to call it and interpret the result.

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% and the parameter is already documented, but the description adds real semantics: omitting project yields everything-applicable notes, while passing it yields that project's status, rules, words and notes. This goes beyond the schema's 'by name' phrasing.

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 verb and resource ('Read the AI context for this board'), and the scope (board-wide vs per-project) cleanly separates it from siblings like fenbs_list_context_notes and fenbs_get_item. An agent knows exactly what this returns.

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?

Explicit sequencing ('Call it straight after fenbs_whoami', 'before touching the work') and explicit scoping ('only ask for the project you are working on'). The relationship to sibling tools (fenbs_get_item for files) is also named.

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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