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@ainize/mcp

@ainize/mcp

Official
by ainblockchain
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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Knowledge signals

knowledge_signals
Read-only

Retrieve partial, node-local usage and scoring signals for a knowledge ID—downloads, settlements, attestations, and recent events—to assess how often it has been used and verified.

Instructions

How much a knowledge has actually been used and scored, as far as THIS node can see: downloads, settlements, attestations and the recent event stream. Node-local and partial — per-day test/hit/miss counters exist in the node database but are not exposed by any endpoint yet.

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": {
      "id": "<id>"
    },
    "name": "knowledge_signals"
  }
}

Input Schema

NameRequiredDescriptionDefault
idYesthe knowledge id, e.g. "krx-all-2761"
limitNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior4/5

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

The annotations already declare readOnlyHint and openWorldHint, and the description adds meaningful behavioral context: node-local scope, partial coverage, and the specific signal categories available. The note that per-day test/hit/miss counters exist but are not exposed is especially useful, though response shape and rate limits are not covered.

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 two sentences, front-loaded with what the tool returns and followed by a relevant limitation. The phrasing 'How much a knowledge...' is slightly awkward, but there is no wasted wording.

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?

It gives a good conceptual picture and an important caveat about partial, node-local data. However, with no output schema, it leaves the response structure, event ordering, and limit semantics unstated, so it is adequate for calling but incomplete for interpreting results.

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

Parameters2/5

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

The schema documents the id parameter with an example, but limit has no description beyond its default and min/max constraints. The tool description never explains what limit controls or how it relates to the recent event stream, so it does not compensate for the 50% schema coverage.

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 names the resource (knowledge usage/scoring signals) and enumerates the signal types returned: downloads, settlements, attestations, and recent events. It does not explicitly distinguish itself from siblings like get_knowledge, but the signal-focused purpose is clear.

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?

It only implies that the tool is for inspecting usage and scoring signals. There is no explicit when-to-use versus alternatives such as get_knowledge or my_library, no exclusions, and no prerequisites. The node-local caveat describes data limits, not invocation guidance.

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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