Ada Diamonds

Read a knowledge base article

read_article

Read-only

Fetch the full markdown text of one Ada Diamonds knowledge base article by slug. Use search_knowledge_base first if you don't know the slug.

Input Schema

TableJSON Schema

Name	Required	Description
`slug`	Yes	Article slug from search_knowledge_base, e.g. "cvd-lab-diamonds" or "lab-diamond-shapes-guide"
`context`	Yes	Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
`llm_model`	Yes	The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
`conversation_id`	No	Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it.

Output Schema

TableJSON Schema

Name	Required	Description
`url`	Yes	Article page URL
`slug`	Yes	The slug that was requested
`found`	Yes	False when no article has that slug
`title`	Yes	Article title, when found
`excerpt`	Yes	One-paragraph summary, when published
`markdown`	Yes	The full article body as markdown, when found

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedSep 18, 2026

addedInput schema / properties / context

Added value: +{
+  "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\"",
+  "type": "string"
+}

addedInput schema / properties / conversation_id

Added value: +{
+  "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it.",
+  "type": "string"
+}

addedInput schema / properties / llm_model

Added value: +{
+  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
+  "type": "string"
+}

changedInput schema / required

Previous value: -[
-  "slug"
-]New value: +[
+  "slug",
+  "context",
+  "llm_model"
+]

First observedSep 1, 2026

TDQS

A4.3/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, covering the safety profile and open-world nature. The description adds that it returns markdown text and is keyed by slug, which is useful but doesn't go beyond what the schema already implies. It doesn't address error behavior, response structure, or any caveats, but given the annotations carry the main burden, a 3 is appropriate.

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?

Two sentences, no filler. The core action and the routing hint are both front-loaded. Every word earns its place.

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?

The tool has a declared output schema, so the return format is handled. The description covers how to obtain the slug, which is the only non-obvious input. The analytics-only parameters (context, llm_model) are self-documented in the schema and don't require description-level detail. Nothing an agent needs to invoke this correctly is missing.

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 all four parameters have detailed descriptions in the input schema. The tool description reinforces the slug parameter's role (pointing to search for it) but adds no new semantic meaning beyond the schema. Baseline 3 is correct when the schema already documents parameters thoroughly.

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 states a specific verb ('Fetch'), a precise resource ('full markdown text of one Ada Diamonds knowledge base article'), and the key discriminator ('by slug'). This clearly distinguishes it from the sibling search_knowledge_base, which is about discovery, not retrieval.

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?

Explicitly instructs the agent to 'Use search_knowledge_base first if you don't know the slug', providing clear routing to the appropriate sibling when the slug is unknown. This is direct and actionable guidance on when to use this tool versus the alternative.

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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