mcp_linguistic_calculus

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

No annotations provided, so description carries full burden. Discloses core mechanics (phase q, momentum, sheaf gluing), operation types, return bundle, and side effects (mints training block, trace integration). However, missing details on error conditions, permissions, or state changes beyond described.

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?

Single paragraph of ~80 words, front-loaded with purpose and operations. Information-dense but uses specialized jargon that could be simplified. Could benefit from bullet points or clearer sectioning, but overall not overly verbose.

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?

No output schema, yet return description is vague ('crs + result bundle/phase preview'). Missing details on return structure, error handling, prerequisites, or typical usage. Assumes significant domain knowledge without concrete examples or clarifications.

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 covers all 4 parameters with descriptions. Description adds useful context: operation enum values, morphological structure of bundle, role of path_bundles for integrate/operadic. This supplements the schema without redundancy, justifying above baseline 3.

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?

Clearly states it performs synthetic differential/integral/operadic calculus on LinguisticDiscourseBundle, listing three operations and return types. Distinguishes from sibling engram tools. However, dense jargon may obscure purpose for agents unfamiliar with the domain.

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?

Implies usage for linguistic calculus operations and mentions post-calc actions (ZEDOS_TRAINING, NREM), but does not explicitly specify when to use this tool vs alternatives or provide exclusion criteria. No direct sibling tools, so guidance is minimal.

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