Detect material changes in public companies since a specified date using primary sources.
Scored across 1 tool
There is only one tool, so there is no possibility of confusion or misselection. Its purpose is clearly stated as identifying material changes in a public company between two dates.
The sole tool name uses a clear verb_noun snake_case pattern: get_company_delta. With only one tool, there are no competing conventions to create inconsistency.
A single tool is borderline thin for a server surface, even if the operation is focused. The rubric treats 1-2 tools as borderline, so it earns a middling score despite being reasonably scoped.
The tool covers the core operation of comparing company states across dates and returns structured evidence. However, there is no supporting surface for discovering company identifiers or managing baselines, which are minor but plausible gaps.
Identify material changes in a public company between a baseline date and the current date. Returns structured before-and-after changes with evidence and source provenance. Research intelligence only.
| Name | Required | Description | Default |
|---|---|---|---|
| since | Yes | ||
| company | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It discloses the shape of the output ('structured before-and-after changes with evidence and source provenance') and its research-only intent, but says nothing about permissions, rate limits, cost, or behavior when the company or date is invalid.
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?
Three short sentences, front-loaded with the purpose, then the return shape, then the scope limit. Nothing is redundant or padded.
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?
An output schema exists, so return values need not be expanded. However, for a two-required-parameter tool with no annotations and no schema descriptions, omitting input format and error/permission behavior leaves the definition only minimally sufficient.
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 coverage is 0% and both parameters are bare. The narrative loosely maps 'since' to a baseline date and 'company' to a public company, but the required date format and whether 'company' is a ticker, legal name, or identifier are left entirely unexplained.
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 gives a specific verb and resource ('Identify material changes in a public company') plus scope ('between a baseline date and the current date'), so an agent immediately knows the operation. There are no sibling tools to differentiate against, which is the only thing keeping this from a 5.
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?
'Research intelligence only' signals the intended context and implicitly excludes operational/transactional use, but no alternatives are named and no prerequisites are given. Usage is implied rather than spelled out.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
get_company_deltaStructured company & industry news for AI agents: typed, dated, source-linked events.
Uncover opportunities in public markets, examine company evidence and track how it changes over time
News momentum + what-changed outcome tools for agents. Signals + links, ~296 sources.
Realtime financial context for AI agents: what changed, who is affected, and what to watch next. One suite covering news, events, guidance, filing changes, sentiment, stakeholders, and alerts. Information-efficient responses with evidence for every result. First-class point-in-time safety for backtests. Pairs well with web search and a market-data API. All data is our own.
Add one secure layer between your agents and this server.
