The Invest dataset: how the eight graded Situational Awareness predictions map onto 17 listed AI equities, how eight well-known investors are positioned per their public SEC 13F filings, and what copying them would have returned priced on the FILING DATE (not quarter end, which no real person could have traded). Educational only — never investment advice.
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Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It provides a valuable methodological detail: returns are priced on the filing date, not quarter end, which no real person could have traded. It also clearly labels the tool educational. However, it does not mention whether the operation is read-only, what response shape to expect, or any 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one dense, information-rich sentence with every clause adding meaningful detail. The parenthetical clarification about filing date is valuable and not redundant. It is slightly long but front-loaded with the dataset name and immediately scopes the content.
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 there are no parameters and no output schema, the description does a good job explaining the three key facets of the dataset. It does not describe the exact return format or how positions are represented, but for a fixed, read-only dataset the coverage is largely sufficient for an agent to call it correctly.
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?
The tool has zero parameters, so the baseline is 4. The description adds no parameter-specific semantics, but none are needed since the input schema is empty and the dataset is a fixed corpus.
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 identifies a precise resource, 'the Invest dataset,' and details exactly what it contains: mapping of SA predictions to AI equities, investor positions from SEC 13F filings, and copy-return calculations. It is clearly distinct from sibling tools like get_claim_ledger or get_verdicts. It lacks an explicit verb like 'retrieves' or 'lists,' but the tool name and context make the action clear.
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 implies when to use it: whenever a user asks about the Invest dataset or investor positions/returns. It does not explicitly state when not to use it or point to alternatives among the sibling tools. The 'educational only' caveat is a usage boundary but not selection guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.