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Fast Embedding MCP SSE

Fast Embedding MCP SSE

by Rikka-Botan
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

index_add

Add documents to a persistent in-memory index for later querying, with optional IDs and metadata.

Instructions

Add documents to the persistent in-memory index for later querying.

Args: documents: Documents to embed and store. ids: Optional ids (auto-generated if omitted). metadatas: Optional metadata dict per document.

Returns the number added and their ids.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "documents": "<documents>"
    },
    "name": "index_add"
  }
}

Input Schema

NameRequiredDescriptionDefault
idsNo
documentsYes
metadatasNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses persistence ('persistent in-memory'), the return value (number and ids), and auto-generated ids, but lacks details on duplicate handling, error behavior, or side effects. Some transparency is present but not exhaustive.

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, front-loaded with the primary purpose, and uses a structured Args list for parameters. Every sentence adds value with no 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?

Given the tool's moderate complexity and lack of output schema, the description covers purpose, parameter semantics, return values, and persistence. It omits explicit usage alternatives, but that gap is partially addressed by the implied querying context.

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 0%, so the description must compensate. It explains each parameter: documents to embed/store, optional ids, and optional metadatas. This adds meaning beyond the raw type schema, though metadata structure remains somewhat vague.

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 'Add documents to the persistent in-memory index for later querying,' using a specific verb and resource while distinguishing itself from sibling tools like index_query and index_clear. The purpose is unambiguous.

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 phrase 'for later querying' implies when to use the tool, but it does not explicitly contrast with alternatives or mention when not to use it. No exclusions or alternative tool references are provided.

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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