Compute quantitative finance European Option prices (Call and Put) and Greeks (Delta, Gamma, Vega, Theta) via Black-Scholes model.
| Name | Required | Description | Default |
|---|---|---|---|
| spotPrice | Yes | Underlying stock/asset spot price S | |
| volatility | No | Annualized implied volatility sigma (decimal or %) | |
| strikePrice | Yes | Strike price K | |
| riskFreeRate | No | Risk-free interest rate r (decimal or %) | |
| timeToExpiryYears | Yes | Time to expiration T in years |
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?
No annotations are provided, so the description carries the full behavioral burden. It clearly discloses that the tool computes both prices and Greeks and specifies the model and option type. It does not enumerate Black-Scholes assumptions like no dividends or lognormal returns, but the core computational behavior is transparent.
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 a single sentence that wastes no words and front-loads the primary action. It quickly communicates the resource, the specific outputs, and the model, making it easy for an agent to parse.
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?
The input schema fully documents all parameters, and the description names both the output categories and the mathematical model. There is no output schema, and the description does not specify the return structure, but for a straightforward computational tool this is a minor gap rather than a blocking omission.
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 100%, so all five parameters are already documented with descriptions and defaults. The tool description adds no additional parameter-level meaning, so it stays at the baseline score of 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?
The description clearly states a specific verb and resource: it computes European option prices and Greeks via the Black-Scholes model. It also distinguishes itself from the sibling calculators by naming the unique outputs (Call/Put prices, Delta, Gamma, Vega, Theta) and the model used.
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 usage for pricing European options and computing Greeks, which is enough to orient an agent. However, it does not explicitly state when to use this tool versus alternatives or mention any exclusions, such as not supporting American options or exotic derivatives.
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.
Most tools are clearly separated by domain and target calculation, such as rocket_deltav versus projectile_motion or black_scholes versus compound_wealth. A few pairs like home_loan_emi/mortgage_piti and contractor_parity/billable_floor could be initially confused, but the descriptions resolve the intended use cases.
All tool names are lowercase snake_case and generally follow a topic-plus-suffix pattern, which is readable and consistent. The pattern is not a strict verb_noun convention, and acronym-heavy names like feie_nomad_tracker, scorp_optimizer, and casio_991_solve introduce stylistic variance.
At exactly 25 tools, this is at the heavy but still usable end of the scale. The broad spread across tax, finance, engineering, physics, math, and cloud cost makes the server feel more like several domain calculators merged into one service.
Each tool is a self-contained calculation with no missing follow-up operations, so there are no obvious dead ends for the workflows it targets. The main gaps are minor adjacent calculators—such as NPV, depreciation, or broader statistical inference—that agents could work around or obtain elsewhere.
