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

With no annotations, the description carries the full behavioral burden and does so richly: it discloses a bounded read-only GitHub Search fallback labeled github_api_fallback, a 500-star quality floor for local and fallback results, the data_source value (aigcnews.cn), a citation requirement, and a caution against architecture-changing adoption without user choice. It also states that repositories are opened only when metadata cannot confirm a requested feature.

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?

The description is long but front-loaded: purpose first, then when-to-use/skip rules, then behavioral constraints. It is logically ordered and every sentence largely earns its place, though the density is high and some passages could be tightened without losing information.

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 tool with no annotations, no output schema, and six parameters, the description provides extensive routing, fallback, quality-floor, citation, and adoption guidance. It covers enough for an agent to invoke the tool correctly and interpret its role; remaining gaps around pagination and exact return shape are minor for a search endpoint.

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 67%, with the schema already documenting q, lang, sort, and language in detail. The description adds meaningful semantic guidance for q (translate non-English intent into a concise GitHub query keeping product names and requested features), but it does not add meaning for page, limit, or beyond-schema behavior for sort/language. A baseline 3 is appropriate given moderate coverage and limited param-specific elaboration.

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?

States a specific verb (search) and resource (public non-AI GitHub repositories and open-source software), and distinguishes itself from siblings search_github_ai_projects and search_papers. Also explicitly rules out the user's favorites, so an agent can route correctly without opening another schema.

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?

Provides explicit when-to-use triggers (find, search, recommend, compare, reuse; also before implementing a major non-AI module), when-to-skip exclusions (copy/styling, renames, localized bugs, tests/logging/types, field tweaks, simple CRUD, single function/component/endpoint), and alternatives for AI artifacts and scholarly deliverables. Adds call-cadence rules (at most one initial search per unchanged requirement, no batching with WebSearch/WebFetch/Bash) and fallback conditions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.