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clo3d-mcp

clo_api_doc

Fetch live documentation for any CLO function by supplying its qualified name, such as pattern_api.GetPatternCount.

Instructions

Read live documentation for a CLO function. Prefer a qualified name such as pattern_api.GetPatternCount.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
function_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations, the description carries the full burden. It implies a read-only operation ('Read') but does not disclose potential side effects, failure modes, rate limits, or authentication requirements. It doesn't mention what happens if the function name is invalid or not found. The description is too sparse to be behaviorally transparent.

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 sentences with no wasted words. The purpose is front-loaded in the first sentence, and the input hint in the second. It is appropriately sized for the tool's simplicity.

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 parameter, likely returns documentation text) and has an output schema, so the description doesn't need to explain return values. However, it lacks guidance on how to discover function names (e.g., use clo_api_search) and what to do on errors. This is adequate but not comprehensive.

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 0%, so the description must compensate. It adds value by recommending a qualified name format with an example ('pattern_api.GetPatternCount'), which gives the agent a concrete pattern. However, it doesn't fully define acceptable formats or what happens with unqualified names, leaving gaps.

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 action ('Read') and the resource ('live documentation for a CLO function'). It is distinct from siblings like clo_api_search (which searches) and clo_api_call (which executes), though it doesn't explicitly name alternatives. The example of a qualified name adds specificity.

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 like clo_api_search or clo_api_describe. The only hint is the input example, which is about formatting the function_name, not about usage context. No exclusions or conditions are provided.

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