Astronomer MCP Server

generate_celestial_body_embedding

Generates a 1024-dimensional vector embedding for a celestial body record by ID, enabling semantic search and comparison of astrophysics data.

Instructions

Calculate 1024-dimensional vector embedding for a celestial body record.

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": {
      "id": "<id>"
    },
    "name": "generate_celestial_body_embedding"
  }
}

Input Schema

Name	Required	Description	Default
`id`	Yes	Celestial Body ID
`sync`	No	If true, wait synchronously for embedding generation to finish

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Nov 13, 2026

TDQS

B3.2/5.0

Behavior3/5

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

With no annotations, the description carries the full disclosure burden; it usefully reveals the embedding dimension (1024) but does not say whether the result is persisted to the record, whether an existing embedding is overwritten, or what happens when sync=false (job handle vs. returned vector). These are material gaps for an operation that likely mutates stored state.

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?

A single front-loaded sentence with no filler, and the key detail (vector dimensionality) is placed where the agent reads it first.

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?

There is no output schema and no annotations, so the description is the only place to learn the return shape and side effects, and it omits both. For a generation tool with an asynchronous option, the agent cannot tell whether it receives a vector, a job ID, or just a persisted record.

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%, and the schema already explains both 'id' and 'sync' including the default, so the description adds no parameter meaning beyond it. Baseline 3 applies when the schema does all the work.

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?

The description gives a specific verb and resource ('Calculate ... vector embedding for a celestial body record') plus the output dimensionality, so its function is unmistakable. It does not, however, distinguish itself from the sibling 'generate_star_system_embedding', leaving the agent to infer which entity type each covers.

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?

There is no when-to-use guidance at all: nothing says when to embed a body versus a star system, when to prefer this over 'generate_deep_synthesis', or when to set sync=false. The agent must infer the usage context entirely from the tool name.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server