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by io.github.cavan1977
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estimate_mcp_context_budget

Estimate how much of an LLM context window your MCP servers consume, and which tool definitions to cut first. Paste your MCP config. Returns per-server token cost, remaining budget, and ranked reduction advice.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
configYesYour MCP config as JSON. Either {"mcpServers":{...}} or a raw {name:{...}} map. Each server may declare "tools":[{name,description,inputSchema}]. Accepts a JSON object, or a JSON string containing one.
contextWindowNoModel context window in tokens (default 200000)
reservedForConversationNoTokens you want to keep for the actual conversation (default 40000)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / config / description
      Previous value: -"Your MCP config as JSON. Either {\"mcpServers\":{...}} or a raw {name:{...}} map. Each server may declare \"tools\":[{name,description,inputSchema}]."New value: +"Your MCP config as JSON. Either {\"mcpServers\":{...}} or a raw {name:{...}} map. Each server may declare \"tools\":[{name,description,inputSchema}]. Accepts a JSON object, or a JSON string containing one."
    • changedInput schema / properties / config / type
      Previous value: -"string"New value: +[
+  "string",
+  "array",
+  "object"
+]
  2. First observed

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries full behavioral burden. It clearly states the action ('Paste your MCP config') and the return value ('per-server token cost, remaining budget, and ranked reduction advice'), making the tool's behavior transparent without needing side-effect warnings.

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?

Two concise sentences, front-loaded with the main purpose and then the required action. Every clause adds value with no filler.

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?

The description provides the input action serve, key inputs covered by schema, and a clear summary of return values despite no output schema. It is complete enough for an agent to select and invoke the tool correctly, though it could mention limitations or explicit alternatives.

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 100%, so the schema already documents all parameters. The description adds no additional parameter-level semantics beyond what the schema provides, keeping it at the baseline.

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 a specific verb and resource: estimating LLM context-window consumption from MCP servers and recommending which tool definitions to cut. It distinguishes itself from planning or compressing tools by emphasizing 'estimate' and 'cut first' as its outcome.

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 description implies when to use it: when you have an MCP config and want to estimate context budget consumption. However, it does not explicitly mention alternatives like plan_context_window_budget or compress_tool_definitions, nor does it provide when-not-to-use 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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