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wisdom_load_context_for_task

Prevent redundant work by retrieving relevant prior knowledge from the wisdom network. Uses task description, trust, and confidence filters to fit a token budget.

Instructions

ALWAYS call this at the start of any task to load relevant prior knowledge and avoid duplicating work. Searches the wisdom network for fragments matching the task description, filtered by trust and confidence within a token budget.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectNoFilter by project UUID
token_budgetNoMaximum approximate tokens to return (default: 10000)
min_confidenceNoMinimum confidence threshold (default: 0.3)
task_descriptionYesDescription of the task to find relevant fragments for

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 provided, the description carries the transparency burden. It transparently states that the tool searches the network, filters by trust and confidence, and respects a token budget. However, it does not explicitly say whether the operation is read-only, what the exact return structure is, or whether it has side effects, leaving some ambiguity.

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 sentences with no filler. The first sentence front-loads the critical usage directive, and the second sentence concisely describes the core behavior and filters. Every phrase earns its place.

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 4-parameter retrieval tool with no output schema or annotations, the description adequately conveys the purpose, main behavior, and return type ('fragments'). It does not detail the return format or mention the project filter, but the schema covers the project parameter and the overall behavior is simple enough to call correctly.

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 description coverage is 100%, so the schema already documents all four parameters. The description mentions 'trust', 'confidence', and 'token budget' generically but does not add parameter-specific meaning beyond what the schema already provides.

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 clearly identifies the action ('load relevant prior knowledge') and the resource ('wisdom network'/'fragments'), and explains the search/filter behavior. However, it does not explicitly distinguish itself from the sibling tool wisdom_search_fragments, which likely performs a similar search.

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 description gives an explicit when-to-use directive: 'ALWAYS call this at the start of any task'. It does not mention any exclusions or alternative tools, so it stops short of a 5.

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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