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Zignet

Zignet

by fulgidus
OverviewSchemaSourceRelated ServersDiscussionsAdmin
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suggest_fix

Generate suggestions to fix Zig code errors using an error message and the surrounding code context.

Instructions

Get intelligent suggestions for fixing Zig code errors

Tool call template

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": {
      "error": "<error>",
      "code": "<code>"
    },
    "name": "suggest_fix"
  }
}

Input Schema

NameRequiredDescriptionDefault
codeYesCode context where the error occurred
errorYesError message to get fix suggestions for

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.15.2-i

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. 'Get ... suggestions' weakly implies a non-mutating read, but nothing states whether fixes are returned as text, as patches, or applied, and there is no mention of latency, rate limits, or side effects. For a tool with zero annotation coverage this is a meaningful gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single short sentence with the resource and purpose front-loaded and zero filler. It is efficient, though its brevity contributes to the gaps noted elsewhere.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no annotations, the description should at least hint at what a suggestion looks like and whether it modifies anything. For a two-parameter tool the surface is small, but the agent is left guessing about the return value and side effects.

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%, with both parameters documented in the schema itself, so the baseline of 3 applies. The description adds no additional semantics about how 'error' and 'code' should relate or what format they take.

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 states a specific verb (suggest fixes) and resource (Zig code errors), so the agent knows this is a suggestion-generation tool rather than a compiler or analyzer. It does not explicitly distinguish itself from the closest sibling analyze_zig, which could plausibly also surface error information, 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 guidance, no ordering relative to compile_zig or analyze_zig, and no mention of prerequisites such as the code having already been compiled or an error actually existing. The agent must infer the workflow entirely.

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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