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by labyrinth-analytics
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Python
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Rebuild Semantic Index

rebuild_index

Rebuild the semantic search index from stored sessions. Used after Pro activation or to recover from a corrupted index.

Instructions

Rebuild the LanceDB semantic search index from all stored sessions. Pro only.

Run after first Pro activation, or to recover from a corrupted index. Downloads BAAI/bge-small-en-v1.5 (~130MB) once on first run; subsequent runs use the cached model. May take 1-2 minutes for large session stores.

Returns a dict with 'indexed' (sessions added to index) and 'total_in_db' (total sessions in SQLite, including those excluded from indexing).

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": "rebuild_index"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.8.3

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations provided, the description fully covers behavioral expectations. It discloses that it downloads BAAI/bge-small-en-v1.5 (~130MB) on first run, caches the model for subsequent runs, and may take 1-2 minutes for large session stores. It also states the return format, a dict with 'indexed' and 'total_in_db' keys. This is comprehensive and avoids surprises.

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 concise and well-structured. It leads with the main purpose, then provides usage context and behavioral details in separate short paragraphs. Every sentence adds value, and there is no redundant information.

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?

Given the tool has no parameters and no output schema, the description provides all necessary information. It explains the purpose, when to use, side effects (model download, time estimate), and return value. The description is complete for an agent to select and invoke this tool correctly.

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?

The tool has zero parameters, and the schema coverage is 100%. Per guidelines, the baseline score is 4 since no additional parameter description is needed. The description briefly mentions output format, which is helpful but not required for parameter semantics.

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: 'Rebuild the LanceDB semantic search index from all stored sessions.' It specifies the resource (LanceDB semantic search index) and the action (rebuild). It also distinguishes the tool by noting it is 'Pro only' and providing specific use cases (first Pro activation, recovery from corrupted index), setting it apart from sibling tools.

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?

The description provides explicit guidance on when to use the tool: 'Run after first Pro activation, or to recover from a corrupted index.' It also mentions the model download and caching behavior, giving the agent a clear understanding of when invocation is appropriate. While it doesn't list alternatives, the context of sibling tools makes the intended use clear.

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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