agent4.io

patch_knowledge_index

Change the structured index schema from one sentence of plain language.

Example requests: "also track the author so users can find other books by them", "I want to
filter by illustrator", "drop the cover link". The model may only emit add/modify/remove/refused
— it cannot rewrite the table, so it has no way to damage columns the user did not mention.

**Data that is not in the documents comes back in `refused`, not as a new column.** Asked for
a publication year that the corpus does not carry, it says so instead of inventing an empty
column. Pass that refusal on to the user verbatim.

Call with `apply=false` first and show the user what would change; batch several edits and apply
once. Applying marks the index for rebuild — it does **not** re-embed anything, so it is cheap.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`name`	Yes
`apply`	No
`request`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.4/5.0

Behavior5/5

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

With no annotations provided, the description carries the full burden of disclosure. It reveals critical constraints (only emits add/modify/remove/refused), explains refusal behavior for unsupported data, and specifies that applying is cheap (no re-embedding). These details go well beyond what annotations would typically provide.

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 efficiently structured in three tight paragraphs: definition and examples, refusal behavior, and usage workflow. Every sentence adds value, and the bolded caveat emphasizes a key point without redundancy.

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?

Given the tool's moderate complexity and the presence of an output schema (which covers return values), the description adequately covers purpose, usage, and behavioral caveats. It explains the apply workflow, refusal handling, and cost implications, making it self-sufficient for an agent.

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?

Schema description coverage is 0%. The description partially explains `request` via examples and `apply` via 'call with apply=false first', but the required `name` parameter is never explicitly defined. An agent must infer that it refers to the knowledge index to patch, which is a significant gap for a required field.

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 clearly states the tool's function with a specific verb ('Change') and resource ('structured index schema'), and provides concrete examples that distinguish it from sibling tools like build_knowledge_index. The scope is explicit: modify the schema incrementally via plain-language requests.

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

Usage Guidelines4/5

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

The description gives clear operational guidance: call with apply=false first, show the user what would change, batch edits, and apply once. It also explains that applying marks the index for rebuild without re-embedding, which is useful context. It does not explicitly name alternative tools or say when not to use, but the usage pattern is well implied.

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

A3.6/5.0

Disambiguation4/5

Tools are mostly distinct by domain and action. A few pairs like add_knowledge_file vs add_knowledge_text or build_knowledge_index vs patch_knowledge_index could cause confusion, but descriptions clearly differentiate them.

Naming Consistency4/5

The vast majority follow a consistent verb_noun snake_case pattern (create_*, list_*, get_*, update_*). A few outliers like tenant_info, usage_stats, and page_context_stats are noun-first, deviating slightly from the otherwise uniform scheme.

Tool Count1/5

With 50 tools, this is a very large surface that exceeds typical well-scoped server sizes. Even for a broad platform management API, the sheer number makes it heavy and potentially overwhelming, matching the '50+' extreme mismatch category.

Completeness2/5

The server covers creation, reading, updating, and listing for most resources but lacks any delete operations (no delete_agent, delete_knowledge_base, delete_skill, delete_share, delete_storyline, delete_page_context). This is a significant gap in lifecycle coverage that will force agents to work around missing functionality.

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