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JevGuard MCP Server

JevGuard MCP Server

by seb4ez
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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jevguard_prune_state

Prune complex JSON state by removing nulls, empty collections, and whitespace, while protecting against cyclic reference errors.

Instructions

Sanitizes and prunes complex JSON state payloads by removing nulls, empty collections, collapsing whitespace, and protecting against cyclic references.

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": {
      "state": "<state>"
    },
    "name": "jevguard_prune_state"
  }
}

Input Schema

NameRequiredDescriptionDefault
stateYesThe state payload dictionary or structure to sanitize and prune.
prune_listsNoWhether to strip empty values and nulls from lists (default: false).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.0.1
    • changedInput schema / properties / state / description
      Previous value: -"The state payload (dict, list, or primitive) to sanitize and prune."New value: +"The state payload dictionary or structure to sanitize and prune."
    • addedInput schema / properties / state / type
      Added value: +"object"
  2. First observedv1.0.0

TDQS

B3.4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses several behaviors: removing nulls, empty collections, collapsing whitespace, and protecting against cyclic references. However, it doesn't disclose whether the operation mutates the input or returns a new object, whether it's destructive, or any side effects. The 'protecting against cyclic references' is a useful behavioral detail, but the mutation/return behavior is a significant 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?

The description is a single sentence that packs in the core operations and the cyclic reference protection. It's concise and front-loaded with the main purpose. It could be slightly more structured (e.g., separating the pruning operations from the safety feature), but it's efficient and readable.

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?

For a tool with 2 parameters, 100% schema coverage, and no output schema, the description covers the main purpose and key behaviors. However, it doesn't explain the return value (sanitized state? success indicator?), which is important for an agent to know what to do with the result. It also doesn't clarify whether the input is mutated in place, which is a meaningful gap for a tool that 'prunes' state.

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%, so the schema already documents both parameters. The description adds context about what 'sanitize' means (removing nulls, empty collections, collapsing whitespace) which helps understand the 'state' parameter's purpose. The 'prune_lists' parameter is not explicitly mentioned in the description, but the schema covers it. Baseline 3 is appropriate since the schema does the heavy lifting.

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 tool's function: sanitizing and pruning JSON state payloads, with specific operations (removing nulls, empty collections, collapsing whitespace, protecting against cyclic references). It distinguishes itself from sibling tools like jevguard_cache_fingerprint and evaluate_* tools, which have different purposes. However, it doesn't explicitly name a sibling alternative for comparison, so it doesn't fully differentiate within a family of similar tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use this tool: when you need to sanitize/prune JSON state payloads. It doesn't explicitly state when not to use it or mention alternatives. The sibling tools are clearly different (evaluation, patching, fingerprinting), so the context is somewhat clear, but there's no explicit guidance on when to choose this over another tool.

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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