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worklab-tools

by Ryeliu
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

index_knowledge

Rebuild the vector index from knowledge base markdown files for semantic search and filtering. Run after adding or updating content.

Instructions

Rebuild the vector index from all markdown files in the knowledge base. Scans companies/, people.md, projects.md, meetings/ and indexes all content into ChromaDB with rich metadata for semantic search and filtering. Call this after adding or updating knowledge base files.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

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

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are provided, so the description carries full burden. It discloses the scope (specific directories), destination (ChromaDB), and the fact that it rebuilds the full index. However, it does not explicitly state whether this is destructive to the existing index or if it is a long-running operation, though 'rebuild' implies recreation.

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 three sentences, each adding value: purpose, scope, and usage trigger. It is front-loaded with the main action and has zero redundancy.

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 zero-parameter tool, the description is quite complete: it specifies input files, output destination, and when to call it. An output schema exists, so further return-value detail is unnecessary. However, it could have mentioned potential side effects like index replacement, but 'rebuild' covers this implicitly.

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?

This tool has zero parameters, and schema coverage is vacuously 100%, so a baseline of 4 is appropriate. The description adds context about what is scanned and where data is indexed, which is useful beyond the empty schema.

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's purpose: rebuilding the vector index from markdown files, listing specific directories scanned. It distinguishes itself from siblings like upsert_knowledge (which likely handles individual entries) and search_knowledge (querying).

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?

Explicitly provides when to use: 'Call this after adding or updating knowledge base files.' It does not mention alternatives or exclusions, but the context is clear enough for the agent to decide between this and sibling tools.

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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