Massive Context MCP

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'Respects concurrency limit to manage system resources' which hints at performance constraints, but doesn't describe what the tool actually does (e.g., queries LLMs, returns results), error handling, rate limits, or authentication needs. The behavioral traits are insufficiently explained for a tool with 6 parameters.

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 appropriately sized with a brief purpose statement followed by parameter explanations. It's front-loaded with the core function. However, the parameter explanations could be more integrated into the flow rather than a separate 'Args:' section, and some details (like concurrency defaults) might be redundant if the schema is well-structured.

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 6 parameters with 0% schema coverage, no annotations, but an output schema exists, the description is moderately complete. It covers the purpose and some parameters, but lacks crucial context about what 'processing' entails, how it relates to sibling tools, and behavioral details. The output schema may handle return values, but the overall context is incomplete for effective tool use.

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 0%, so the description must compensate. It adds some meaning by explaining 'query' as 'Question/instruction for each sub-call', 'provider' options, and default values for 'concurrency'. However, it doesn't clarify 'context_name' (what context?), 'chunk_indices' (what are chunks?), or 'model' specifics. The description partially compensates but leaves key parameters ambiguous.

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 states the tool 'Process multiple chunks in parallel' which indicates a batch processing function, but it's vague about what 'chunks' are or what 'processing' entails. It doesn't clearly distinguish this from sibling tools like 'rlm_sub_query' (singular vs. batch) or 'rlm_auto_analyze' (analysis vs. querying). The purpose is understandable but lacks specificity about the resource being processed.

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 no guidance on when to use this tool versus alternatives. It mentions respecting concurrency limits, but doesn't explain scenarios where batch processing is preferable (e.g., efficiency for multiple chunks vs. using 'rlm_sub_query' individually) or prerequisites. Without context about sibling tools, an agent cannot make informed decisions.

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