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findagent_add_kb_document

Add a text document to a knowledge base you own by pasting its content. It ingests asynchronously (parse → structure-aware chunk → embed with the KB’s bound key). Content is de-duplicated by hash: re-adding the exact same text is a no-op (the response deduped flag is true, no second document). Requires the KB to have an embedding key bound, or the document will not ingest. For files (PDF/markdown/docx), use the web Knowledge Base uploader.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe document content to add — paste the full text (plain text, max 1 MB).
kb_idYesThe knowledge base id (from findagent_list_kbs).
titleNoA short human-readable title for the document (shown in findagent_list_kb_documents). Optional — defaults to "Untitled document".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
documentNo
instructionsNo

TDQS

A4.7/5.0
Behavior5/5

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

The description discloses several non-obvious behaviors beyond annotations: asynchronous ingestion pipeline (parse → structure-aware chunk → embed with the KB’s bound key), hash-based deduplication with the `deduped` response flag, and the silent failure mode when no embedding key is bound. The readOnlyHint=false annotation agrees with the mutating nature of the tool, so there is no contradiction.

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 front-loaded with the core action and then efficiently adds async behavior, deduplication, embedding-key requirement, and file exclusion. Each sentence carries distinct information and no sentence feels redundant or padded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 3-parameter tool with an output schema, the description covers the full operational context: how ingestion works, how duplicates are handled, what could cause failure, and when to use a different upload path. The agent has enough information to invoke the tool correctly and interpret likely outcomes.

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 already documents all three parameters (text, kb_id, title) sufficiently. The description adds contextual preconditions like KB ownership and bound embedding key, but it does not materially extend parameter-level semantics beyond what the schema provides. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific action — 'Add a text document to a knowledge base you own by pasting its content' — which names the verb, resource, and input method. It clearly distinguishes this from sibling tools like findagent_delete_kb_document and findagent_list_kb_documents by restricting scope to adding text content only.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description states exactly when to use the tool: for pasted text documents in a KB you own. It gives a concrete exclusion — for files (PDF/markdown/docx), use the web Knowledge Base uploader — and notes the prerequisite that the KB must have an embedding key bound. This is explicit when/when-not 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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TDQS

A4.1/5.0
Disambiguation5/5

Every tool targets a clearly scoped resource+action combination, and the versioning variants (bump_version vs repull vs reintrospect_mcp) are explicitly disambiguated by agent kind. Even with 52 tools, the descriptions make each purpose distinct enough to avoid misselection.

Naming Consistency4/5

Nearly all tools follow the findagent_<verb>_<object> snake_case pattern, and families share predictable verbs like list, create, delete, edit, and submit. Minor exceptions such as findagent_preflight, findagent_whoami, and findagent_earnings keep it from perfect consistency.

Tool Count2/5

At 52 tools, this far exceeds the 25-tool threshold for a coherent MCP surface. The count may reflect a broad platform, but as a single toolset it is heavy and likely to strain agent selection and context.

Completeness3/5

The surface covers publishing, versioning, knowledge bases, org management, GitHub import, purchases, and the demand board in impressive depth. However, there are notable dead ends: no unpublish/delete for a live agent, no request-fulfillment/linking action, and no org deletion or KB document update.

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