ContextStream MCP Server

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

The description adds valuable behavioral context beyond the annotations. While annotations indicate this is not read-only, idempotent, or destructive, the description explains the tool's role in session initialization, automatic context retrieval, and the ingest_recommendation field with specific follow-up actions. It also mentions semantic search capabilities and the need for the user's first message. No contradiction with annotations exists.

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 well-structured and front-loaded with the core purpose and usage guidelines. Most sentences earn their place by providing essential information about when to call the tool, what it returns, parameter usage, and follow-up actions. However, the section about ingest_recommendation could be slightly more concise, and some repetition exists in explaining context_hint.

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 the complexity of an 11-parameter initialization tool with no output schema, the description does a good job of explaining the tool's role, critical parameters, and expected outcomes. It covers the ingest_recommendation field thoroughly with actionable guidance. However, without an output schema, more detail on the structure of returned data (workspace info, project info, etc.) would be helpful for completeness.

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?

With 100% schema description coverage, the baseline is 3. The description adds some semantic value by emphasizing the importance of the context_hint parameter ('RECOMMENDED: Pass the user's first message here for semantic search') and providing an example. However, it doesn't significantly enhance understanding of most parameters beyond what the schema already documents thoroughly.

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 clearly states the tool's purpose: 'Initialize a new conversation session and automatically retrieve relevant context.' It specifies this is the 'FIRST tool AI assistants should call when starting a conversation' and distinguishes it from siblings by emphasizing its role as the initial setup tool. The description goes beyond the title by explaining the automatic context retrieval and return values.

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 provides explicit usage guidelines: 'This is the FIRST tool AI assistants should call when starting a conversation.' It also gives specific instructions about when to use the context_hint parameter ('Pass the user's FIRST MESSAGE as context_hint') and clarifies temporal scope ('You only need context on subsequent messages'). The description effectively guides when and how to use this tool versus alternatives.

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