The documentation, as a tool your agent can call: 950+ AI-dev guides. Search + fetch tools.
Scored across 2 tools
The two tools have clearly distinct purposes: search finds articles and returns snippets, while fetch retrieves the full text of a specific article by ID. There is no overlap or ambiguity between them.
Both tool names are single lowercase verbs ('fetch' and 'search') that directly describe their actions. The naming is simple, consistent, and follows a predictable pattern.
Two tools is on the low end for a documentation server, but it is a reasonable minimal set for search-and-retrieve functionality. The count is borderline but not inappropriate for the narrow scope.
The search-and-fetch lifecycle is complete for a documentation corpus: discover articles via search, then retrieve full content via fetch. Minor gaps exist (e.g., no pagination or listing all articles), but agents can accomplish the core task without dead ends.
Retrieve the complete markdown of one documentation article by the id returned from search (for example en/claude-code/advanced-techniques/hooks-automation). The text is returned in full; metadata.gated only reports whether the article sits behind the paywall on the web. An unknown id is an error — call search first.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Article id from a `search` result. | |
| 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. |
| Name | Required | Description |
|---|---|---|
| id | Yes | |
| url | Yes | |
| text | Yes | |
| title | Yes | |
| metadata | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds valuable behavioral context beyond that: the full markdown is returned, `metadata.gated` only reflects web paywall status, and unknown ids produce an error. This is a clear improvement over the structured metadata alone.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with the core action, and every sentence adds information: the retrieval behavior, the paywall semantics, and the required prerequisite. There is no filler or redundant repetition of schema/annotation content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that an output schema exists and the input schema covers all parameters at 100%, the description does not need to explain return shape or parameter formats. It covers the necessary behavioral aspects—full content, error condition, and the relationship to `search`—leaving no critical gap for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds extra meaning for the `id` parameter by giving a concrete example of the expected format and reinforcing that it must come from a `search` result. The `llm_model` parameter is already fully documented in the schema, so no further description contribution is needed there.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The title and description clearly state the resource (a documentation article) and the specific operation (retrieve complete markdown by id). It explicitly ties the input to values returned by `search`, which distinguishes it from its sibling tool without ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly instructs the agent to call `search` first and warns that an unknown id is an error. This gives clear when-to-use guidance and prevents misuse, even though it does not enumerate every non-use case explicitly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search the AI Developer Toolkit documentation: 950+ guides on Cursor, Claude Code and OpenAI Codex, covering setup, agent workflows, hooks, MCP, testing, CI and deployment, in English and Polish. Returns at most 10 ranked results, each with a short snippet rather than the article text; an empty list means the corpus has nothing on the topic. Pass a result id to fetch for the full text.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Natural-language query or keywords. Polish queries return Polish articles. | |
| 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. |
| Name | Required | Description |
|---|---|---|
| results | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true accrual openWorldHint=false. The description adds valuable behavioral detail beyond those flags: results are capped at 10, ranked, snippet-only, and an empty list is authoritative for corpus coverage. This strongly supports correct interpretation of the closed-world annotation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, each earning its place: scope, result behavior, and routing to `fetch`. The most important information is front-loaded, with no redundant or filler content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a two-parameter, read-only search tool with a fully documented schema, an output schema, and one clearly named sibling, the description is complete. It covers scope, result limits, snippet format, empty-result semantics, language behavior, and the hand-off to `fetch`.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 both `query` and `llm_model` comprehensively. The main description does not add parameter-specific semantics beyond what the schema provides; per the rubric, the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb ('Search'), a concrete resource ('AI Developer Toolkit documentation'), and scopes the corpus by tool, topic, and language. It also distinguishes itself from the sibling `fetch` by stating it returns snippets rather than article text.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly routes deeper retrieval to the sibling: 'Pass a result id to `fetch` for the full text.' It also clarifies the meaning of an empty result list, removing ambiguity about when to conclude the corpus lacks content.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
fetch2 fields changedInput schema / properties / llm_modelAdded 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"
+}Input schema / requiredPrevious value: -[
- "id"
-]New value: +[
+ "id",
+ "llm_model"
+]search2 fields changedInput schema / properties / llm_modelAdded 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"
+}Input schema / requiredPrevious value: -[
- "query"
-]New value: +[
+ "query",
+ "llm_model"
+]fetchsearchDocs Q&A: search 169 data and AI guides, fetch any page as markdown. Read-only, keyless.
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