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.
Most tools fall into recognizable families (data lookup, entity research, prediction markets, memory, subscriptions), and the detailed descriptions help separate them. However, ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical variants, and several polymarket scanning tools overlap in purpose enough to cause misselection.
Nearly all tool names are snake_case and readable, and families share clear prefixes like ask_pipeworx_*, polymarket_*, and pipeworx_*. The main inconsistency is that the Brazilian data endpoints use bare nouns (quote, crypto, currency, inflation, prime_rate) while most other tools use verb-like action names, so there is no single verb_noun pattern throughout.
38 tools is well above the 25+ threshold for a heavy MCP surface, even though the server aggregates several distinct domains. Each tool may have a purpose, but the sheer count makes the set difficult to navigate and suggests the server is trying to be a platform rather than a focused toolset.
The server covers the full lifecycle for its core areas: lookup (ask_pipeworx, grounded, deep_research), entity workflows (resolve, profile, compare, recent_changes), memory (remember/recall/forget), and subscriptions (subscribe/list/unsubscribe/recent_alerts). Minor gaps exist, such as no subscription update/pause and no direct tool to fetch an arbitrary pipeworx:// citation, but agents can work around these.