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index_embeddings

Convert stored Telegram messages into vector chunks, enabling semantic search across group chats.

Instructions

Index cached Telegram messages into vector chunks for semantic search.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chatNoChat ID, @username, or omitted for TELEGRAM_DEFAULT_CHAT_ID.
rebuildNoDelete existing chunks for the configured model/dimensions before indexing.
limit_chunksNoChunks to embed in this run.
after_message_idNoStart indexing messages after this ID.
Behavior2/5

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

No annotations provided, so description carries full burden. It does not disclose side effects (e.g., modifying storage), potential destruction (rebuild parameter hints at deletion but not stated), or performance implications. The term 'cached' is ambiguous.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single front-loaded sentence with no wasted words. However, it could benefit from brief additional context about the indexing process or expected output.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Without annotations or output schema, the description should more fully explain the tool's behavior and results. It omits what the agent can expect after calling (e.g., confirmation, chunk count) and does not clarify the vector dimension or model used.

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 coverage is 100% with descriptions for all 4 parameters. Description adds no extra meaning beyond what the schema provides, so baseline of 3 is appropriate.

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 verb 'Index' and resource 'cached Telegram messages' with a specific outcome 'into vector chunks for semantic search'. It distinguishes from sibling tools like semantic_search_messages which searches already indexed messages.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or that it should be run before semantic search. The agent cannot determine appropriate context from the description alone.

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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