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Galatic.ai

by ai.galatic
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Search library

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Read-onlyIdempotent

Search the signed-in user's Galatic.AI library. Videos are matched by meaning (what happens in them, what is said), anything else by its prompt or title. Returns {results:[{id,title,url}]}.

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": {
      "query": "<query>",
      "context": "<context>",
      "llm_model": "<llm_model>"
    },
    "name": "search"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYes
contextYesExplain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
llm_modelYesThe exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
conversation_idNoPass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-open-world and non-destructive behavior, so the bar is lower. The description still adds real value beyond them: it discloses the matching model (embeddings for video, lexical prompt/title for other assets) and, since there is no output schema, spells out the return shape {results:[{id,title,url}]}.

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?

Three tight sentences with zero filler: scope first, matching semantics second, return shape last. Every sentence carries information the structured fields do not already provide.

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?

With no output schema, the description correctly compensates by stating the response shape, and annotations cover the safety profile while the schema covers parameters. What is missing is result-set behavior — how many results come back, whether there is pagination or truncation, and which asset types (music, images, episodes) the library actually contains.

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 75%, and the three non-obvious parameters (context, llm_model, conversation_id) carry detailed schema documentation, so the schema does the heavy lifting. The description adds nothing about any parameter — notably it never mentions the required 'query' or the conversation_id bootstrap rule — which is acceptable but leaves no extra meaning.

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?

States a specific verb and resource ("search the signed-in user's Galatic.AI library") and explains the matching semantics: videos by meaning, everything else by prompt or title. It does not, however, distinguish itself from plausible siblings like list_gallery, list_artists or list_episodes, so the agent must infer the boundary.

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?

There is no explicit when-to-use / when-not-to-use statement and no named alternative. The sentence about semantic video matching versus prompt/title lookup implicitly tells the agent this is the tool for concept-level retrieval rather than enumerating a list, but that is inference rather than guidance.

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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