plaid-mcp

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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it's a write operation ('annotate', 'record'), affects future analysis ('so summarize_debt_tool can reason honestly'), and has temporal effects ('After the promo_expires date, payoff analysis reverts'). It doesn't mention permissions or side effects, but covers the core mutation behavior well.

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 and front-loaded: the first sentence states the core purpose, followed by specific examples and downstream effects. Every sentence adds value—no wasted words—and the structure flows logically from problem to solution to implications.

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 tool's complexity (mutation with temporal effects), no annotations, and no output schema, the description is largely complete. It explains the what, why, and how, including interactions with 'summarize_debt_tool'. It could mention error cases or confirmation of success, but covers the essential context well for a tool with 4 parameters.

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 explains the purpose of 'effective_apr' ('record the true effective APR') and 'promo_expires' ('optionally a promo expiration date'), adding meaning beyond the schema. It doesn't detail 'account_id' or 'note', but provides enough context for the critical parameters given the low coverage.

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: 'Annotate a linked card with the real APR when Plaid misses it.' It specifies the verb ('annotate'), resource ('linked card'), and context ('when Plaid misses it'), distinguishing it from siblings like 'summarize_debt_tool' or 'get_liabilities_tool' by focusing on manual correction of data.

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 guidance on when to use this tool: 'Common case: Citi doesn't consistently report 0% intro / balance-transfer promos through /liabilities/get.' It also states the alternative ('reverts to Plaid's reported purchase APR') and links to 'summarize_debt_tool' for downstream effects, making usage clear relative to other tools.

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