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by MlkBlk-IT
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Python
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Ctx Vector Stats

ctx_vector_stats
Read-onlyIdempotent

Retrieve vector index statistics—backend, model, dimension, and vector count—to verify configuration and assess index size.

Instructions

Return vector index statistics (backend, model, dimension, vector count).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "ctx_vector_stats"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.3

TDQS

A3.7/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered without the description's help. The description adds only the identity of the reported fields, which the output schema already supplies, so it contributes little behavioral context beyond structured data.

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?

A single front-loaded sentence with no filler, and the parenthetical field list is an efficient way to convey the payload. Every clause earns its place.

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?

For a zero-parameter, read-only inspection tool with a full output schema and complete annotations, the description says enough to call it correctly. The one gap is that it never positions itself relative to ctx_stats or ctx_health, which is the main ambiguity an agent would face in this toolset.

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?

The tool takes zero parameters, so there are no parameter semantics to clarify and the baseline of 4 applies. Nothing in the description misleads about inputs, and additionalProperties=false confirms no hidden arguments.

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 uses a specific verb ("Return") and resource ("vector index statistics") and enumerates the returned fields (backend, model, dimension, vector count), so an agent knows exactly what it gets. It does not, however, distinguish itself from the close sibling ctx_stats, leaving the vector-vs-general scope split implicit.

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?

Usage is only implied: an agent can infer this is the tool for inspecting vector-index configuration rather than general store statistics. There is no explicit when-to-use statement and no named alternative such as ctx_stats or ctx_health, which a sibling-heavy namespace like this one really needs.

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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