cryptobrapi.dev — crypto price quote for a coin (e.g. 'BTC') in a target currency (default BRL). Returns price, 24h change, market cap, and volume sourced from Brazilian market data.
| Name | Required | Description | Default |
|---|---|---|---|
| coin | Yes | ||
| currency | No |
| 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?
Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds useful context by specifying return fields (price, 24h change, market cap, volume) and the data source (Brazilian market), which goes beyond the annotation hints. No contradiction.
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, front-loaded sentence that immediately communicates the core purpose. No filler words, every clause adds relevant 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 simple two-parameter quote tool, the description covers purpose, parameters, and output fields. The output schema presumably handles return structure. Minor limitations, such as what 'Brazilian market data' specifically means, are not disclosed, but overall the context is 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 description coverage is 0%, so the description must compensate. It explains 'coin' as a crypto ticker (e.g., 'BTC') and 'currency' with a default of BRL. The schema examples add 'USD' as an alternative, providing sufficient clarity for both parameters.
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 identifies the action (price quote), resource (crypto coin), and scope (target currency, Brazilian market data). It distinguishes itself from sibling tools like 'currency' and 'quote' by explicitly mentioning crypto and the data source.
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?
Usage context is implied from the name and description, but there is no explicit guidance on when to use this tool versus alternatives like 'currency' or 'quote'. No exclusions or alternative tool references are provided.
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.
Several tool clusters overlap: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research all route to the same source catalog, the six Polymarket tools have fuzzy boundaries between research, edge scanning, arbitrage, and fill checking, and available vs quote_list both enumerate B3 tickers. The descriptions are detailed and try to differentiate, but an agent could still easily pick the wrong meta-tool.
The overwhelming majority follow lower_snake_case verb_noun naming (ask_pipeworx, resolve_entity, scan_dependency, validate_claim, list_subscriptions). Deviations like available, quote, crypto, currency, inflation, and prime_rate are bare nouns, and forget is a lone verb, but the convention remains readable and largely predictable.
38 tools is excessive for what the server name (Brapi) suggests, and the set spans unrelated domains: Brazilian market data, a 5,798-tool universal data router, Polymarket betting analytics, memory, subscriptions, AI visibility, npm dependency scanning, and llms.txt generation. The count crosses the 25+ threshold and dilutes the server's focus.
Within each sub-domain the surface is fairly complete: brapi.dev quotes/directory/rates, Pipeworx routing/grounding/research/entity resolution/validation, prediction-market arbitrage/fill checks, memory CRUD, and subscription lifecycle all cover their core workflows. Minor gaps exist, such as no dedicated historical stock-price series beyond quote's OHLC window and deep_research requiring an account, but agents can work around them.