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TinyFn

by io.tinyfn

multiply

Multiply two or more numbers together.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
numbersYesComma-separated numbers to multiply

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
numbersYes
productYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed8 schema fields changed
    • addedOutput schema / $defs / MultiplyResponse / properties / numbers / items / anyOf
      Added value: +[
+  {
+    "type": "integer"
+  },
+  {
+    "type": "number"
+  }
+]
    • removedOutput schema / $defs / MultiplyResponse / properties / numbers / items / type
      Removed value: -"number"
    • addedOutput schema / $defs / MultiplyResponse / properties / product / anyOf
      Added value: +[
+  {
+    "type": "integer"
+  },
+  {
+    "type": "number"
+  }
+]
    • removedOutput schema / $defs / MultiplyResponse / properties / product / type
      Removed value: -"number"
    • addedOutput schema / properties / numbers / items / anyOf
      Added value: +[
+  {
+    "type": "integer"
+  },
+  {
+    "type": "number"
+  }
+]
    • removedOutput schema / properties / numbers / items / type
      Removed value: -"number"
    • addedOutput schema / properties / product / anyOf
      Added value: +[
+  {
+    "type": "integer"
+  },
+  {
+    "type": "number"
+  }
+]
    • removedOutput schema / properties / product / type
      Removed value: -"number"
  2. First observed

TDQS

B3.4/5.0
Behavior3/5

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

No annotations are present, so the description carries the full burden, but for a pure arithmetic operation the stated behavior is largely sufficient. It does not disclose edge-case handling, input parsing behavior, or return details, though the output schema may cover the return information.

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?

The description is a single, front-loaded sentence with no filler or redundant information. It is appropriately sized for a simple arithmetic utility and every word contributes to understanding the tool.

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?

Given the very low complexity of this tool, the schema's full parameter coverage, and the presence of an output schema, the definition is nearly complete for correct invocation. The only notable gap is the lack of guidance disambiguating it from similar sibling tools like calculate_product.

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?

The input schema already provides a complete parameter description ('Comma-separated numbers to multiply'), so the description adds little beyond that. It does add the 'two or more' constraint, which is helpful, but it does not explain how the comma-separated string is parsed or validated.

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 clearly states the operation ('Multiply') and the resource ('two or more numbers'), making the tool's purpose immediately understandable. It does not explicitly distinguish itself from the sibling calculate_product, which may serve a very similar purpose, so it stops short of a 5.

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 use this tool versus alternatives such as add, subtract, calculate_product, or sum_numbers. No conditions, exclusions, or routing hints are provided, leaving the agent to infer usage solely from the name and operation.

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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TDQS

C2.3/5.0
Disambiguation2/5

Many tools have overlapping purposes, such as multiple random generators (random_integer, random_number), duplicate hashing functions (hash_md5, md5_checksum), and near-identical tools (compare, compare_2, compare_decimals). The sheer number of tools and lack of clear boundaries make it difficult for an agent to differentiate.

Naming Consistency1/5

Naming is highly inconsistent. There are duplicate tools with different names (camel_case vs to_camel_case, slug vs slugify), arbitrary suffixes like '_2', and mixing of patterns (e.g., generate_password vs password_entropy). No clear convention is followed.

Tool Count1/5

With 572 tools, the server is massively overpopulated for any coherent purpose. It includes trivial endpoints (true_endpoint, null, hello_world) and numerous duplicates, far exceeding a well-scoped utility set.

Completeness2/5

While the server covers many domains (math, strings, dates, colors, etc.), the presence of duplicate and trivial tools indicates a lack of thoughtful curation. There are gaps in basic operations (e.g., no dedicated file or network tools), and many tools are redundant.

Resources