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3lehr
by 3lehr

knowledge_browse

Browse a knowledge tree node to see its child topics as titles and summaries, using '/' for the root. Enables quick, token-efficient exploration of stored knowledge.

Instructions

Browse children of a knowledge tree node. Returns titles+summaries only (token-efficient). Use '/' for root.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathNoTree path to browse, e.g. '/' or '/shared/arch'/
actorNoCalling agent identity; else BEGOD_KNOWLEDGE_ACTOR or unknown
modelNoCalling model; else BEGOD_KNOWLEDGE_MODEL or unknown
sessionNoStable session ID; else BEGOD_KNOWLEDGE_SESSION or unknown
project_filterNoFilter by project (free-form slug, e.g. one of the app dirs under <Verbundwurzel>/ -- not enforced/closed)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations present, the description carries the full burden. It does disclose that the tool returns only titles and summaries, which is a meaningful behavioral trait and implies a read-only, token-conscious operation. It does not explicitly state the absence of side effects or discuss access/auth behavior, but for a simple browse operation the disclosed behavior is reasonably transparent.

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 two short sentences and every clause earns its place: the action, the return shape, the token-efficiency rationale, and the root-path usage hint. It is front-loaded and free of 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?

For a low-complexity browse tool, the description covers the key operational facts: what is browsed, what is returned, and how to target the root. The absence of an output schema is partially mitigated by the explicit 'titles+summaries' return note. Minor gaps like whether children are immediate-only or recursive are not stated, but they are not essential for basic invocation.

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 already documents all 5 parameters with 100% coverage, so the description needs to add little. The only added guidance, 'Use "/" for root,' largely repeats the schema default for path. The description adds no new meaning to actor, model, session, or project_filter beyond what the schema provides.

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 names a specific verb ('Browse') and resource ('children of a knowledge tree node'), and further clarifies the return payload is 'titles+summaries only.' This distinguishes it from siblings like knowledge_read or knowledge_search, which presumably return full content or search results.

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 gives a concrete usage hint ('Use "/" for root') and implies the tool is for lightweight tree navigation by mentioning token-efficiency. However, it does not explicitly state when to prefer this over knowledge_read, knowledge_search, or the other knowledge siblings, so the selection logic is left mostly to inference.

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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