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developer-toolkit-mcp

by io.github.mjaskolski
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Search the documentation

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Search the AI Developer Toolkit documentation: 1100+ 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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural-language query or keywords. Polish queries return Polish articles.
llm_modelYesThe 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.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • 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: -[
-  "query"
-]New value: +[
+  "query",
+  "llm_model"
+]
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already establish readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds real behavioral context: a hard cap of 10 ranked results, snippet-only returns, and the meaning of an empty list as 'nothing in corpus' — details not derivable from annotations or schema.

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?

Three tight sentences: corpus scope first, return behavior second, handoff to fetch last. No redundant restatement of the title or annotations, and every clause carries information an agent needs.

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?

Even though an output schema exists (so return values need not be explained), the description usefully summarizes the result shape, cap, empty-list semantics, and the follow-up call. Nothing material to correct invocation 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 both parameters are already documented, including the Polish-query behavior and the 'never guess' rule for llm_model. The description adds no further syntax or format detail, making 3 the correct baseline.

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?

States a specific verb (Search) and resource (AI Developer Toolkit documentation) plus the corpus scope: 1100+ guides across named tools, languages, and topics. It also distinguishes itself from the sibling 'fetch' by explaining that search returns snippets and fetch returns full text.

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 clearly implies the search-then-fetch workflow and names the sibling to use for full article text, giving an agent a routing rule. It does not state when *not* to search (e.g. for a known id), so it stops short of full 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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