Generate Fibonacci numbers as a sequence or return the nth term, with exact big-integer values. Pure CPU, instant.
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": {
"n": "<n>"
},
"name": "fibonacci"
}
}| Name | Required | Description | Default |
|---|---|---|---|
| n | Yes | Count or index |
Changes observed during successful MCP inspections.
Input schema / properties / nAdded value: +{
+ "description": "Count or index",
+ "type": "integer"
+}Input schema / requiredAdded value: +[
+ "n"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are supplied, so the description carries the full behavioral burden. It does add real value by declaring "Pure CPU, instant" (no network, no side effects, fast) and "exact big-integer values" (precision guarantees), but says nothing about output shape or how enormous values are serialized back to the caller.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two compact clauses with no waste, and the core action is front-loaded. Slightly compressed to the point that the mode-selection detail is omitted rather than stated.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter tool with no annotations and no output schema, the description should say what comes back — a list, a single value, and whether huge values are strings or numbers (a genuine concern for Fibonacci big integers in JSON). That return-shape gap is the main missing piece.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, but the schema's own text ("Count or index") is ambiguous. The description partially resolves this by naming both roles (sequence length vs. index), yet never says how a single 'n' selects a mode, leaving the parameter's dual meaning under-explained.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource ("Generate Fibonacci numbers") and covers both operating modes, sequence vs. nth term, with a distinguishing quality (exact big-integer values). It doesn't name a sibling like factorial or prime, but the resource is specific enough that confusion is unlikely.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies two usage modes (sequence output vs. single term), but never says how to choose between them, nor when to prefer this over siblings such as factorial, prime, or math_eval. The agent must infer mode selection from the lone 'n' parameter.
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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