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agent-knowledge

agent-knowledge

by yucx-go
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

ak_ingest

Add text to a knowledge vault. Extracts claims and entities to build structured memory for AI agents.

Instructions

Ingest text into the knowledge vault. Extracts claims and entities.

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": {
      "text": "<text>"
    },
    "name": "ak_ingest"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYesText content to ingest.
titleNoTitle for the ingested source (optional).
source_typeNoType of source: text, file, url, conversation.text

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.1

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of disclosing behavioral traits. It mentions that the tool extracts claims and entities, but it does not disclose whether the operation is destructive, appends or replaces data, requires specific permissions, or returns any acknowledgment. The lack of such context for a mutating tool 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.

Conciseness5/5

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

The description is exceptionally concise, consisting of two short sentences that immediately state the primary purpose and a key behavioral outcome. There is no redundant or filler content.

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?

The tool has three parameters, no output schema, and no annotations, yet the description provides minimal context. It omits return values, any side effects, and notably does not explain how it differs from the sibling 'ak_ingest_umsf'. Given the moderately complex operation of ingesting various source types, the description is insufficient for an agent to use it reliably in all contexts.

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?

Since the schema provides full descriptions for all three parameters (text, title, source_type), the description is not required to add parameter details. It adds no extra semantic value beyond the schema, but the schema already covers all parameters, so the baseline score of 3 is appropriate.

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 ('Ingest text into the knowledge vault') and an added behavior ('Extracts claims and entities'), using a specific verb and resource. However, it does not distinguish this tool from the closely named sibling 'ak_ingest_umsf', leaving ambiguity about which ingest variant to use.

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?

No guidance is provided on when to use this tool versus alternatives. The description simply states what it does, with no mention of use cases, exclusions, or comparison to the sibling ingest tool. This leaves the agent without decision criteria for selection.

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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