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jtkehler

crowdsmart-mcp

by jtkehler

add_text_document

Add text as a new document to a Knowledge Agent you own, splitting it into passages and storing source details for citations.

Instructions

Add text to a Knowledge Agent you own, as a new document that keeps its source.

The text is split into passages and can be asked about at once. The source details are stored with every passage, for citations.

Args: agent_id: ID of a Knowledge Agent you own. title: Document title, shown in citations. text: The text to add. source_url: URL of the original source. author: Author of the original source. timestamp: ISO 8601 date when the source was published or created. source_type: Kind of source, for example "web", "note", "transcript" or "email".

Input Schema

NameRequiredDescriptionDefault
textYes
titleYes
authorNo
agent_idYes
timestampNo
source_urlNo
source_typeNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=false and destructiveHint=false, so the write-without-destruction profile is covered. The description adds genuine value by explaining that text is split into passages and that source details are stored per passage for citations, but omits processing timing/indexing behavior, which matters given the get_document_status sibling implies asynchronous ingestion.

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?

Front-loaded with the core action and result, then a compact per-parameter block. A sentence or two is slightly redundant with the Args list (e.g. 'keeps its source' vs. the source_* params), but overall efficient.

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?

An output schema exists so return values need not be explained, and the description covers ownership, chunking, and citation metadata. The main omission is what happens after the call (async processing, need to poll via get_document_status).

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?

Schema description coverage is 0%, so the description carries the full burden, and the Args block defines all seven parameters meaningfully — including format hints such as ISO 8601 for timestamp and example values ('web', 'note', 'transcript', 'email') for source_type. Minor gap: it doesn't state which parameters are optional or their defaults.

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?

States a specific verb and resource ('Add text ... as a new document') targeting a Knowledge Agent, and clarifies the source metadata is retained. It doesn't explicitly contrast with the sibling upload_document, but the 'text' framing makes the distinction reasonably inferable.

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 phrase 'a Knowledge Agent you own' implies an ownership precondition, but there is no explicit when-to-use versus upload_document or other ingestion tools, and no mention of failure modes or size limits.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.