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search_changes

Search Microsoft Graph API change history using natural-language or keyword queries across all sources, with hybrid keyword and semantic matching.

Instructions

Search Microsoft Graph API change history using natural-language or keyword queries across all sources (seed-entra-ms, backfill-graph-changelog, self). Hybrid BM25-style keyword + semantic search, fused via Reciprocal Rank Fusion; degrades to keyword-only without OPENAI_API_KEY configured.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoSearch mode — hybrid degrades to keyword automatically without an OpenAI keyhybrid
limitNoMax results (default 10, max 50)
queryYesNatural-language or keyword query, e.g. "groups API nesting", "ID Governance separation of duties"
endpointNoFilter to a specific Graph endpoint
Behavior4/5

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

With no annotations provided, the description carries the behavioral disclosure burden. It reveals the hybrid algorithm (BM25 + semantic, Reciprocal Rank Fusion) and the fallback behavior when OPENAI_API_KEY is missing. It also identifies the data sources searched. This goes beyond a generic 'search' claim, though it doesn't detail return structure or rate limits.

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 a single, dense sentence that gets straight to the point. It covers purpose, scope, search mechanism, and a conditional dependency without wasted words. Every clause contributes meaningful information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description provides sufficient context for a search tool: it names the sources, explains the hybrid mode, and notes the fallback. With no output schema, it doesn't describe result format, but that is not critical for a search invocation. It lacks explicit guidance on when to use alternative search tools, but overall it is complete for most use cases.

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 the baseline is 3. The description itself does not add parameter-level detail beyond the schema; it mentions natural-language/keyword queries and mode degradation, which are already present in the schema descriptions for 'query' and 'mode'. No additional meaning is provided.

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 action ('Search'), the resource ('Microsoft Graph API change history'), and the scope ('across all sources (seed-entra-ms, backfill-graph-changelog, self)'). This distinguishes it from sibling tools like search_schema_objects and get_recent_changes, which serve different purposes.

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

Usage Guidelines3/5

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

The description implies usage ('Search Microsoft Graph API change history') but does not explicitly state when to prefer this tool over alternatives or provide exclusions. For example, it doesn't mention that search_schema_objects is for schema objects or get_change_detail for specific change details. However, the context is clear enough for an agent to infer common use cases.

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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