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KB-MCP Server

KB-MCP Server

by isshiki-dev
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
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ingest_document

Add documents to a knowledge base for semantic search by storing content with embeddings. Enables AI clients to manage and retrieve information from stored documents.

Instructions

Ingest a document into the knowledge base. Stores content with embeddings for semantic search.

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": {
      "title": "<title>",
      "content": "<content>"
    },
    "name": "ingest_document"
  }
}

Input Schema

NameRequiredDescriptionDefault
titleYesDocument title
contentYesDocument content (text, markdown, etc.)
metadataNoOptional metadata

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool 'ingests' and 'stores' content, implying a write operation, but doesn't cover critical aspects like required permissions, rate limits, idempotency, or what happens on duplicate ingestion. This leaves significant gaps for a mutation tool.

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 extremely concise with two clear sentences that directly state the tool's function and outcome. Every word earns its place, and it's front-loaded with the core purpose, making it efficient and easy to parse.

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?

Given this is a mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., side effects, error handling), return values, or how it integrates with siblings like 'query_knowledge'. For a tool that modifies state, more context is needed for safe and effective use.

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 fully documents parameters like 'title' and 'content'. The description adds no additional parameter semantics beyond what's in the schema, such as format details for 'content' or examples for 'metadata'. This meets the baseline for high 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 clearly states the tool's purpose with a specific verb ('ingest') and resource ('document'), explaining it stores content with embeddings for semantic search. It distinguishes from siblings like 'delete_document' or 'get_document' by focusing on creation/storage, though it doesn't explicitly contrast with 'update_document' which might have overlapping functionality.

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?

The description provides no guidance on when to use this tool versus alternatives like 'update_document' or 'delete_document'. It mentions storing content for semantic search, which hints at use with 'query_knowledge', but lacks explicit when/when-not instructions or prerequisites for effective tool 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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