Introspect your API key: calls used this month, included allowance, remaining credits, and when the counter resets.
Free — doesn't hit the billing meter. Mirrors `GET /v1/usage`
(MCP_MIRRORS_V1: change both together). Useful before a large batch of
`get_sentiment`/`get_summary` calls to check you have credit headroom.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 full burden, and it delivers: 'Free — doesn't hit the billing meter' is a key behavioral disclosure, and 'Mirrors GET /v1/usage (MCP_MIRRORS_V1: change both together)' adds implementation-level transparency. The read-only, cost-free nature is explicitly conveyed.
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 compact, front-loads the core purpose, and every segment earns its place: what it returns, that it's free, the API mirror, and a concrete use case. The MCP_MIRRORS_V1 note is slightly maintainer-oriented but still useful information.
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?
For a zero-parameter, read-only introspection tool with an output schema, the description covers everything an agent needs: what it does, when to call it, cost implications, and behavior. No critical context is missing.
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 there is no parameter semantics burden on the description. The schema coverage is trivially 100%, and the description adds relevant context about what the API key introspection covers. Baseline 4 is appropriate for a zero-parameter tool.
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 uses a specific verb ('Introspect') and names the exact resource ('your API key') plus the specific data returned: calls used, allowance, remaining credits, and reset time. This clearly distinguishes get_usage from data-analysis siblings like get_sentiment/get_summary.
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?
It explicitly recommends using the tool 'before a large batch of get_sentiment/get_summary calls to check you have credit headroom.' This gives a concrete, actionable usage context. It doesn't state exclusions, but there are no plausible alternative tools to route around among the listed siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct resource: raw headlines, daily aggregates, prices, correlation, usage, and ticker universe. Even the two sentiment-adjacent tools (get_sentiment vs get_summary) are clearly separated by granularity and response shape.
All tools follow a consistent verb_noun pattern: get_correlation, get_prices, get_sentiment, get_summary, get_usage, list_tickers. The one list_ tool is a conventional collection enumeration and does not break the predictability.
Six tools is well-scoped for a read-only sentiment/price data API. Each tool provides a distinct, necessary capability with no redundancy or bloat.
The set covers the full read-only workflow: discover tickers, fetch prices, fetch raw headlines, fetch daily sentiment aggregates, compute the correlation between sentiment and returns, and check API usage. There are no obvious missing operations for the stated domain.