Astronomer MCP Server

update_context_window

Adjust the active LLM context window size in tokens to fit current workloads and manage how much context the model can use.

Instructions

Dynamically update the active LLM context window token size.

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": {
      "context_window": "<context_window>"
    },
    "name": "update_context_window"
  }
}

Input Schema

Name	Required	Description	Default
`context_window`	Yes	New context window size in tokens (e.g. 65536, 131072, 262144)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Nov 13, 2026

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are supplied, so the description carries the full disclosure burden for a mutation. It says 'update' but does not state whether the change is persisted or session-scoped, whether it takes effect for in-flight requests, whether it is reversible, or what error surfaces on an out-of-range value. For a write operation with zero annotation coverage this is a significant gap.

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?

One short sentence, front-loaded and free of padding. The adverb 'Dynamically' contributes almost no information, but nothing is bloated or buried.

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

Completeness3/5

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

The tool is simple — one required integer, no nesting, no output schema — so the schema covers the mechanics. What is missing is the effect of the call: scope of 'active', persistence, and failure modes. Those are behavioral facts only the description could supply, and it does not.

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%: the single parameter is typed as an integer with an exclusive minimum of 0, a maximum, and example values. The description adds nothing beyond restating that the value is a token size. With the schema doing all the work, the baseline 3 applies.

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 names a specific verb and resource: updating the active LLM context window token size. That is unambiguous and matches the parameter name exactly. It stops short of saying what 'active' means (current session vs. global setting), which keeps it out of 5 territory.

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 guidance on when to call this rather than reading settings or simply configuring the client. No prerequisites, no exclusions, no mention of what happens if the requested size exceeds the model's maximum. The agent is left to infer the trigger condition entirely.

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

Deploy Server