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agentspan-ai

MCP Test Server

by agentspan-ai

collection_merge

Merge two dictionaries into one, giving precedence to values from the second dictionary when keys conflict.

Instructions

Merge two dictionaries. Values from dict_b win on conflict.

Args: dict_a: The base dictionary. dict_b: The dictionary to merge in (wins on conflict).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dict_aYes
dict_bYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.4

TDQS

B3.2/5.0
Behavior2/5

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

No annotations exist, so the description carries the full burden, and it leaves the most important behavioral question unanswered: whether the merge is shallow or deep (nested objects are explicitly in scope per the schema) and whether the input dictionaries are mutated. It does disclose the conflict rule ('dict_b wins'), which is the one genuinely useful behavioral detail.

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 front-loaded sentences plus a tight Args block; nothing is redundant given 0% schema coverage, and the decisive rule is stated before any parameter detail.

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 output schema means return values need no explanation, and the tool is simple with only two required params. However, with nested dictionaries supported and zero annotation coverage, the shallow-vs-deep merge semantics and input mutation behavior are missing and are exactly what an agent needs to call this safely.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must carry parameter meaning, and it does: dict_a is 'the base dictionary' and dict_b 'wins on conflict'. Both parameters are given roles, which is real added value over bare 'Dict A'/'Dict B' titles, though nested-value handling is still unspecified.

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?

States a specific verb+resource ('Merge two dictionaries') plus the resolution rule, which is more than the name alone conveys. It does not differentiate itself from the closest sibling, collection_zip, which also combines two collections, 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 when-to-use or when-not-to-use guidance and no alternative named. The only usage-relevant fact is the conflict rule, which is a behavior, not a selection guideline.

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