quote_listbrapi.dev — paginated directory of all Brazilian market tickers (stocks, ETFs, FIIs) with optional text search, sector filter, and sort. Use to discover or enumerate B3-listed symbols.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | ||
| type | No | ||
| limit | No | ||
| search | No | ||
| sector | No | ||
| sortBy | No | ||
| sortOrder | 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?
The description adds behavioral context beyond the readOnlyHint and idempotentHint annotations by specifying pagination behavior and optional filters (search, sector, sort). It also reveals the data source (brapi.dev) and covered asset classes. It does not disclose defaults like page size, but the output schema compensates for return structure, and no contradictions with annotations exist.
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 two sentences, front-loaded with the data provider and core behavior. It avoids filler and every clause contributes to understanding the tool's purpose and usage. Efficiency is exemplary without sacrificing clarity.
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 directory tool with 7 parameters and no schema descriptions, the high-level overview is helpful but not fully complete. It explains the general capability but omits specifics on parameter values (e.g., type enums, sort options). The existence of an output schema covers return values, but the input side remains partially underdocumented for an agent to use confidently.
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?
With 0% schema coverage, the description carries the burden of explaining parameters. It mentions 'text search, sector filter, and sort', mapping to search, sector, sortBy, and sortOrder, and implies page/limit via 'paginated'. However, it does not specify allowed values for `type`, `sortBy`, or `sortOrder`, leaving the agent to infer or guess. Partial compensation for the lack of schema descriptions.
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 defines the tool as a 'paginated directory of all Brazilian market tickers' with explicit scope (B3-listed stocks, ETFs, FIIs). It distinguishes itself from sibling tools like 'quote' by focusing on enumeration/discovery rather than individual quotes. The phrase 'Use to discover or enumerate' leaves no ambiguity about its purpose.
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 explicitly states when to use the tool: 'Use to discover or enumerate B3-listed symbols.' It does not mention alternatives or when not to use it, but the context is sufficient for an agent to differentiate it from sibling tools such as 'quote' or 'crypto'. A clear directive but lacks an explicit exclusion clause.
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.